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Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const s=t.elements,i=e.elements,r=this.elements,n=s[0],a=s[4],o=s[8],h=s[12],u=s[1],c=s[5],l=s[9],d=s[13],p=s[2],m=s[6],f=s[10],y=s[14],g=s[3],x=s[7],b=s[11],M=s[15],w=i[0],v=i[4],_=i[8],T=i[12],S=i[1],A=i[5],E=i[9],z=i[13],R=i[2],I=i[6],k=i[10],L=i[14],O=i[3],P=i[7],B=i[11],N=i[15];return r[0]=n*w+a*S+o*R+h*O,r[4]=n*v+a*A+o*I+h*P,r[8]=n*_+a*E+o*k+h*B,r[12]=n*T+a*z+o*L+h*N,r[1]=u*w+c*S+l*R+d*O,r[5]=u*v+c*A+l*I+d*P,r[9]=u*_+c*E+l*k+d*B,r[13]=u*T+c*z+l*L+d*N,r[2]=p*w+m*S+f*R+y*O,r[6]=p*v+m*A+f*I+y*P,r[10]=p*_+m*E+f*k+y*B,r[14]=p*T+m*z+f*L+y*N,r[3]=g*w+x*S+b*R+M*O,r[7]=g*v+x*A+b*I+M*P,r[11]=g*_+x*E+b*k+M*B,r[15]=g*T+x*z+b*L+M*N,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],s=t[4],i=t[8],r=t[12],n=t[1],a=t[5],o=t[9],h=t[13],u=t[2],c=t[6],l=t[10],d=t[14];return t[3]*(+r*o*c-i*h*c-r*a*l+s*h*l+i*a*d-s*o*d)+t[7]*(+e*o*d-e*h*l+r*n*l-i*n*d+i*h*u-r*o*u)+t[11]*(+e*h*c-e*a*d-r*n*c+s*n*d+r*a*u-s*h*u)+t[15]*(-i*a*u-e*o*c+e*a*l+i*n*c-s*n*l+s*o*u)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,s){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=s),this}invert(){const t=this.elements,e=t[0],s=t[1],i=t[2],r=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8],c=t[9],l=t[10],d=t[11],p=t[12],m=t[13],f=t[14],y=t[15],g=c*f*h-m*l*h+m*o*d-a*f*d-c*o*y+a*l*y,x=p*l*h-u*f*h-p*o*d+n*f*d+u*o*y-n*l*y,b=u*m*h-p*c*h+p*a*d-n*m*d-u*a*y+n*c*y,M=p*c*o-u*m*o-p*a*l+n*m*l+u*a*f-n*c*f,w=e*g+s*x+i*b+r*M;if(0===w)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const v=1/w;return t[0]=g*v,t[1]=(m*l*r-c*f*r-m*i*d+s*f*d+c*i*y-s*l*y)*v,t[2]=(a*f*r-m*o*r+m*i*h-s*f*h-a*i*y+s*o*y)*v,t[3]=(c*o*r-a*l*r-c*i*h+s*l*h+a*i*d-s*o*d)*v,t[4]=x*v,t[5]=(u*f*r-p*l*r+p*i*d-e*f*d-u*i*y+e*l*y)*v,t[6]=(p*o*r-n*f*r-p*i*h+e*f*h+n*i*y-e*o*y)*v,t[7]=(n*l*r-u*o*r+u*i*h-e*l*h-n*i*d+e*o*d)*v,t[8]=b*v,t[9]=(p*c*r-u*m*r-p*s*d+e*m*d+u*s*y-e*c*y)*v,t[10]=(n*m*r-p*a*r+p*s*h-e*m*h-n*s*y+e*a*y)*v,t[11]=(u*a*r-n*c*r-u*s*h+e*c*h+n*s*d-e*a*d)*v,t[12]=M*v,t[13]=(u*m*i-p*c*i+p*s*l-e*m*l-u*s*f+e*c*f)*v,t[14]=(p*a*i-n*m*i-p*s*o+e*m*o+n*s*f-e*a*f)*v,t[15]=(n*c*i-u*a*i+u*s*o-e*c*o-n*s*l+e*a*l)*v,this}scale(t){const e=this.elements,s=t.x,i=t.y,r=t.z;return e[0]*=s,e[4]*=i,e[8]*=r,e[1]*=s,e[5]*=i,e[9]*=r,e[2]*=s,e[6]*=i,e[10]*=r,e[3]*=s,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],s=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,s,i))}makeTranslation(t,e,s){return this.set(1,0,0,t,0,1,0,e,0,0,1,s,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),s=Math.sin(t);return this.set(1,0,0,0,0,e,-s,0,0,s,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),s=Math.sin(t);return this.set(e,0,s,0,0,1,0,0,-s,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),s=Math.sin(t);return this.set(e,-s,0,0,s,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const s=Math.cos(e),i=Math.sin(e),r=1-s,n=t.x,a=t.y,o=t.z,h=r*n,u=r*a;return this.set(h*n+s,h*a-i*o,h*o+i*a,0,h*a+i*o,u*a+s,u*o-i*n,0,h*o-i*a,u*o+i*n,r*o*o+s,0,0,0,0,1),this}makeScale(t,e,s){return this.set(t,0,0,0,0,e,0,0,0,0,s,0,0,0,0,1),this}makeShear(t,e,s,i,r,n){return this.set(1,s,r,0,t,1,n,0,e,i,1,0,0,0,0,1),this}compose(t,e,s){const i=this.elements,r=e._x,n=e._y,a=e._z,o=e._w,h=r+r,u=n+n,c=a+a,l=r*h,d=r*u,p=r*c,m=n*u,f=n*c,y=a*c,g=o*h,x=o*u,b=o*c,M=s.x,w=s.y,v=s.z;return i[0]=(1-(m+y))*M,i[1]=(d+b)*M,i[2]=(p-x)*M,i[3]=0,i[4]=(d-b)*w,i[5]=(1-(l+y))*w,i[6]=(f+g)*w,i[7]=0,i[8]=(p+x)*v,i[9]=(f-g)*v,i[10]=(1-(l+m))*v,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,s){const i=this.elements;let r=n.set(i[0],i[1],i[2]).length();const o=n.set(i[4],i[5],i[6]).length(),h=n.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],a.copy(this);const u=1/r,c=1/o,l=1/h;return a.elements[0]*=u,a.elements[1]*=u,a.elements[2]*=u,a.elements[4]*=c,a.elements[5]*=c,a.elements[6]*=c,a.elements[8]*=l,a.elements[9]*=l,a.elements[10]*=l,e.setFromRotationMatrix(a),s.x=r,s.y=o,s.z=h,this}makePerspective(t,e,s,i,r,n){void 0===n&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const a=this.elements,o=2*r/(e-t),h=2*r/(s-i),u=(e+t)/(e-t),c=(s+i)/(s-i),l=-(n+r)/(n-r),d=-2*n*r/(n-r);return a[0]=o,a[4]=0,a[8]=u,a[12]=0,a[1]=0,a[5]=h,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=l,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,s,i,r,n){const a=this.elements,o=1/(e-t),h=1/(s-i),u=1/(n-r),c=(e+t)*o,l=(s+i)*h,d=(n+r)*u;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-c,a[1]=0,a[5]=2*h,a[9]=0,a[13]=-l,a[2]=0,a[6]=0,a[10]=-2*u,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,s=t.elements;for(let t=0;t<16;t++)if(e[t]!==s[t])return!1;return!0}fromArray(t,e=0){for(let s=0;s<16;s++)this.elements[s]=t[s+e];return this}toArray(t=[],e=0){const s=this.elements;return t[e]=s[0],t[e+1]=s[1],t[e+2]=s[2],t[e+3]=s[3],t[e+4]=s[4],t[e+5]=s[5],t[e+6]=s[6],t[e+7]=s[7],t[e+8]=s[8],t[e+9]=s[9],t[e+10]=s[10],t[e+11]=s[11],t[e+12]=s[12],t[e+13]=s[13],t[e+14]=s[14],t[e+15]=s[15],t}}r.prototype.isMatrix4=!0;const n=new i.a,a=new r,o=new i.a(0,0,0),h=new i.a(1,1,1),u=new i.a,c=new i.a,l=new i.a},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=s(3);const r={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},n={h:0,s:0,l:0},a={h:0,s:0,l:0};function o(t,e,s){return s<0&&(s+=1),s>1&&(s-=1),s<1/6?t+6*(e-t)*s:s<.5?e:s<2/3?t+6*(e-t)*(2/3-s):t}function h(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function u(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class c{constructor(t,e,s){return void 0===e&&void 0===s?this.set(t):this.setRGB(t,e,s)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,s){return this.r=t,this.g=e,this.b=s,this}setHSL(t,e,s){if(t=i.f(t,1),e=i.d(e,0,1),s=i.d(s,0,1),0===e)this.r=this.g=this.b=s;else{const i=s<=.5?s*(1+e):s+e-s*e,r=2*s-i;this.r=o(r,i,t+1/3),this.g=o(r,i,t),this.b=o(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let s;if(s=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=s[1],r=s[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const s=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(s,i,r)}}}else if(s=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=s[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=r[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const s=e>0?1/e:1;return this.r=Math.pow(t.r,s),this.g=Math.pow(t.g,s),this.b=Math.pow(t.b,s),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=h(t.r),this.g=h(t.g),this.b=h(t.b),this}copyLinearToSRGB(t){return this.r=u(t.r),this.g=u(t.g),this.b=u(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,s=this.g,i=this.b,r=Math.max(e,s,i),n=Math.min(e,s,i);let a,o;const h=(n+r)/2;if(n===r)a=0,o=0;else{const t=r-n;switch(o=h<=.5?t/(r+n):t/(2-r-n),r){case e:a=(s-i)/t+(s65535?o.i:o.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,s=0){this.groups.push({start:t,count:e,materialIndex:s})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const s=this.attributes.normal;if(void 0!==s){const e=(new l.a).getNormalMatrix(t);s.applyNormalMatrix(e),s.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return f.makeRotationFromQuaternion(t),this.applyMatrix4(f),this}rotateX(t){return f.makeRotationX(t),this.applyMatrix4(f),this}rotateY(t){return f.makeRotationY(t),this.applyMatrix4(f),this}rotateZ(t){return f.makeRotationZ(t),this.applyMatrix4(f),this}translate(t,e,s){return f.makeTranslation(t,e,s),this.applyMatrix4(f),this}scale(t,e,s){return f.makeScale(t,e,s),this.applyMatrix4(f),this}lookAt(t){return y.lookAt(t),y.updateMatrix(),this.applyMatrix4(y.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let s=0,i=t.length;s0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const s in e)void 0!==e[s]&&(t[s]=e[s]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const s=this.attributes;for(const e in s){const i=s[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const s=this.morphAttributes[e],n=[];for(let e=0,i=s.length;e0&&(i[e]=n,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const n=this.groups;n.length>0&&(t.data.groups=JSON.parse(JSON.stringify(n)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const s=t.index;null!==s&&this.setIndex(s.clone(e));const i=t.attributes;for(const t in i){const s=i[t];this.setAttribute(t,s.clone(e))}const r=t.morphAttributes;for(const t in r){const s=[],i=r[t];for(let t=0,r=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),n=Math.atan2(r,e*s);t=Math.sin(t*n)/r,a=Math.sin(a*n)/r}const r=a*s;if(o=o*t+l*r,h=h*t+d*r,u=u*t+p*r,c=c*t+m*r,t===1-a){const t=1/Math.sqrt(o*o+h*h+u*u+c*c);o*=t,h*=t,u*=t,c*=t}}t[e]=o,t[e+1]=h,t[e+2]=u,t[e+3]=c}static multiplyQuaternionsFlat(t,e,s,i,r,n){const a=s[i],o=s[i+1],h=s[i+2],u=s[i+3],c=r[n],l=r[n+1],d=r[n+2],p=r[n+3];return t[e]=a*p+u*c+o*d-h*l,t[e+1]=o*p+u*l+h*c-a*d,t[e+2]=h*p+u*d+a*l-o*c,t[e+3]=u*p-a*c-o*l-h*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,s,i){return this._x=t,this._y=e,this._z=s,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const s=t._x,i=t._y,r=t._z,n=t._order,a=Math.cos,o=Math.sin,h=a(s/2),u=a(i/2),c=a(r/2),l=o(s/2),d=o(i/2),p=o(r/2);switch(n){case\\\\\\\"XYZ\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c+l*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c+l*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c+l*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+n)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const s=e/2,i=Math.sin(s);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(s),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,s=e[0],i=e[4],r=e[8],n=e[1],a=e[5],o=e[9],h=e[2],u=e[6],c=e[10],l=s+a+c;if(l>0){const t=.5/Math.sqrt(l+1);this._w=.25/t,this._x=(u-o)*t,this._y=(r-h)*t,this._z=(n-i)*t}else if(s>a&&s>c){const t=2*Math.sqrt(1+s-a-c);this._w=(u-o)/t,this._x=.25*t,this._y=(i+n)/t,this._z=(r+h)/t}else if(a>c){const t=2*Math.sqrt(1+a-s-c);this._w=(r-h)/t,this._x=(i+n)/t,this._y=.25*t,this._z=(o+u)/t}else{const t=2*Math.sqrt(1+c-s-a);this._w=(n-i)/t,this._x=(r+h)/t,this._y=(o+u)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let s=t.dot(e)+1;return sMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=s):(this._x=0,this._y=-t.z,this._z=t.y,this._w=s)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=s),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const s=this.angleTo(t);if(0===s)return this;const i=Math.min(1,e/s);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const s=t._x,i=t._y,r=t._z,n=t._w,a=e._x,o=e._y,h=e._z,u=e._w;return this._x=s*u+n*a+i*h-r*o,this._y=i*u+n*o+r*a-s*h,this._z=r*u+n*h+s*o-i*a,this._w=n*u-s*a-i*o-r*h,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const s=this._x,i=this._y,r=this._z,n=this._w;let a=n*t._w+s*t._x+i*t._y+r*t._z;if(a<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,a=-a):this.copy(t),a>=1)return this._w=n,this._x=s,this._y=i,this._z=r,this;const o=1-a*a;if(o<=Number.EPSILON){const t=1-e;return this._w=t*n+e*this._w,this._x=t*s+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const h=Math.sqrt(o),u=Math.atan2(h,a),c=Math.sin((1-e)*u)/h,l=Math.sin(e*u)/h;return this._w=n*c+this._w*l,this._x=s*c+this._x*l,this._y=i*c+this._y*l,this._z=r*c+this._z*l,this._onChangeCallback(),this}slerpQuaternions(t,e,s){this.copy(t).slerp(e,s)}random(){const t=Math.random(),e=Math.sqrt(1-t),s=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),s*Math.sin(r),s*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}r.prototype.isQuaternion=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,s=0,i=1){this.x=t,this.y=e,this.z=s,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,s,i){return this.x=t,this.y=e,this.z=s,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,s=this.y,i=this.z,r=this.w,n=t.elements;return this.x=n[0]*e+n[4]*s+n[8]*i+n[12]*r,this.y=n[1]*e+n[5]*s+n[9]*i+n[13]*r,this.z=n[2]*e+n[6]*s+n[10]*i+n[14]*r,this.w=n[3]*e+n[7]*s+n[11]*i+n[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,s,i,r;const n=.01,a=.1,o=t.elements,h=o[0],u=o[4],c=o[8],l=o[1],d=o[5],p=o[9],m=o[2],f=o[6],y=o[10];if(Math.abs(u-l)o&&t>g?tg?o1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(s.geometries=e),i.length>0&&(s.materials=i),r.length>0&&(s.textures=r),a.length>0&&(s.images=a),o.length>0&&(s.shapes=o),h.length>0&&(s.skeletons=h),u.length>0&&(s.animations=u)}return s.object=i,s;function n(t){const e=[];for(const s in t){const i=t[s];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,s,i,r,n,a,o,h){const u=this.elements;return u[0]=t,u[1]=i,u[2]=a,u[3]=e,u[4]=r,u[5]=o,u[6]=s,u[7]=n,u[8]=h,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,s=t.elements;return e[0]=s[0],e[1]=s[1],e[2]=s[2],e[3]=s[3],e[4]=s[4],e[5]=s[5],e[6]=s[6],e[7]=s[7],e[8]=s[8],this}extractBasis(t,e,s){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),s.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const s=t.elements,i=e.elements,r=this.elements,n=s[0],a=s[3],o=s[6],h=s[1],u=s[4],c=s[7],l=s[2],d=s[5],p=s[8],m=i[0],f=i[3],y=i[6],g=i[1],x=i[4],b=i[7],M=i[2],w=i[5],v=i[8];return r[0]=n*m+a*g+o*M,r[3]=n*f+a*x+o*w,r[6]=n*y+a*b+o*v,r[1]=h*m+u*g+c*M,r[4]=h*f+u*x+c*w,r[7]=h*y+u*b+c*v,r[2]=l*m+d*g+p*M,r[5]=l*f+d*x+p*w,r[8]=l*y+d*b+p*v,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],s=t[1],i=t[2],r=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8];return e*n*u-e*a*h-s*r*u+s*a*o+i*r*h-i*n*o}invert(){const t=this.elements,e=t[0],s=t[1],i=t[2],r=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8],c=u*n-a*h,l=a*o-u*r,d=h*r-n*o,p=e*c+s*l+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const m=1/p;return t[0]=c*m,t[1]=(i*h-u*s)*m,t[2]=(a*s-i*n)*m,t[3]=l*m,t[4]=(u*e-i*o)*m,t[5]=(i*r-a*e)*m,t[6]=d*m,t[7]=(s*o-h*e)*m,t[8]=(n*e-s*r)*m,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,s,i,r,n,a){const o=Math.cos(r),h=Math.sin(r);return this.set(s*o,s*h,-s*(o*n+h*a)+n+t,-i*h,i*o,-i*(-h*n+o*a)+a+e,0,0,1),this}scale(t,e){const s=this.elements;return s[0]*=t,s[3]*=t,s[6]*=t,s[1]*=e,s[4]*=e,s[7]*=e,this}rotate(t){const e=Math.cos(t),s=Math.sin(t),i=this.elements,r=i[0],n=i[3],a=i[6],o=i[1],h=i[4],u=i[7];return i[0]=e*r+s*o,i[3]=e*n+s*h,i[6]=e*a+s*u,i[1]=-s*r+e*o,i[4]=-s*n+e*h,i[7]=-s*a+e*u,this}translate(t,e){const s=this.elements;return s[0]+=t*s[2],s[3]+=t*s[5],s[6]+=t*s[8],s[1]+=e*s[2],s[4]+=e*s[5],s[7]+=e*s[8],this}equals(t){const e=this.elements,s=t.elements;for(let t=0;t<9;t++)if(e[t]!==s[t])return!1;return!0}fromArray(t,e=0){for(let s=0;s<9;s++)this.elements[s]=t[s+e];return this}toArray(t=[],e=0){const s=this.elements;return t[e]=s[0],t[e+1]=s[1],t[e+2]=s[2],t[e+3]=s[3],t[e+4]=s[4],t[e+5]=s[5],t[e+6]=s[6],t[e+7]=s[7],t[e+8]=s[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=s(15),r=s(1),n=s(3);let a=0;class o extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:a++}),this.uuid=n.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=r.xb,this.side=r.H,this.vertexColors=!1,this.opacity=1,this.format=r.Ib,this.transparent=!1,this.blendSrc=r.Nc,this.blendDst=r.Db,this.blendEquation=r.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=r.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=r.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=r.R,this.stencilZFail=r.R,this.stencilZPass=r.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const s=t[e];if(void 0===s){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=s===r.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(s):i&&i.isVector3&&s&&s.isVector3?i.copy(s):this[e]=s:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const s={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const s in t){const i=t[s];delete i.metadata,e.push(i)}return e}if(s.uuid=this.uuid,s.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(s.name=this.name),this.color&&this.color.isColor&&(s.color=this.color.getHex()),void 0!==this.roughness&&(s.roughness=this.roughness),void 0!==this.metalness&&(s.metalness=this.metalness),void 0!==this.sheen&&(s.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(s.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(s.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(s.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(s.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(s.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(s.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(s.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(s.shininess=this.shininess),void 0!==this.clearcoat&&(s.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(s.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(s.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(s.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(s.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,s.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(s.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(s.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(s.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(s.lightMap=this.lightMap.toJSON(t).uuid,s.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(s.aoMap=this.aoMap.toJSON(t).uuid,s.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(s.bumpMap=this.bumpMap.toJSON(t).uuid,s.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(s.normalMap=this.normalMap.toJSON(t).uuid,s.normalMapType=this.normalMapType,s.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(s.displacementMap=this.displacementMap.toJSON(t).uuid,s.displacementScale=this.displacementScale,s.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(s.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(s.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(s.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(s.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(s.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(s.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(s.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(s.combine=this.combine)),void 0!==this.envMapIntensity&&(s.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(s.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(s.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(s.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(s.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(s.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(s.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(s.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(s.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(s.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(s.size=this.size),null!==this.shadowSide&&(s.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(s.sizeAttenuation=this.sizeAttenuation),this.blending!==r.xb&&(s.blending=this.blending),this.side!==r.H&&(s.side=this.side),this.vertexColors&&(s.vertexColors=!0),this.opacity<1&&(s.opacity=this.opacity),this.format!==r.Ib&&(s.format=this.format),!0===this.transparent&&(s.transparent=this.transparent),s.depthFunc=this.depthFunc,s.depthTest=this.depthTest,s.depthWrite=this.depthWrite,s.colorWrite=this.colorWrite,s.stencilWrite=this.stencilWrite,s.stencilWriteMask=this.stencilWriteMask,s.stencilFunc=this.stencilFunc,s.stencilRef=this.stencilRef,s.stencilFuncMask=this.stencilFuncMask,s.stencilFail=this.stencilFail,s.stencilZFail=this.stencilZFail,s.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(s.rotation=this.rotation),!0===this.polygonOffset&&(s.polygonOffset=!0),0!==this.polygonOffsetFactor&&(s.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(s.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(s.linewidth=this.linewidth),void 0!==this.dashSize&&(s.dashSize=this.dashSize),void 0!==this.gapSize&&(s.gapSize=this.gapSize),void 0!==this.scale&&(s.scale=this.scale),!0===this.dithering&&(s.dithering=!0),this.alphaTest>0&&(s.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(s.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(s.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(s.wireframe=this.wireframe),this.wireframeLinewidth>1&&(s.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(s.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(s.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(s.flatShading=this.flatShading),!1===this.visible&&(s.visible=!1),!1===this.toneMapped&&(s.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(s.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(s.textures=e),r.length>0&&(s.images=r)}return s}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let s=null;if(null!==e){const t=e.length;s=new Array(t);for(let i=0;i!==t;++i)s[i]=e[i].clone()}return this.clippingPlanes=s,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}o.prototype.isMaterial=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=s(28);class r{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const s=this;return new Promise((function(i,r){s.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return I}));var i=s(0),r=s(2),n=s(18),a=s(39),o=s(5),h=s(10),u=s(40),c=s(1),l=s(29),d=s(7);const p=new o.a,m=new a.a,f=new n.a,y=new i.a,g=new i.a,x=new i.a,b=new i.a,M=new i.a,w=new i.a,v=new i.a,_=new i.a,T=new i.a,S=new r.a,A=new r.a,E=new r.a,z=new i.a,R=new i.a;class I extends h.a{constructor(t=new d.a,e=new l.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,s=Object.keys(e);if(s.length>0){const t=e[s[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,s=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const s=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===s.boundingSphere&&s.computeBoundingSphere(),f.copy(s.boundingSphere),f.applyMatrix4(r),!1===t.ray.intersectsSphere(f))return;if(p.copy(r).invert(),m.copy(t.ray).applyMatrix4(p),null!==s.boundingBox&&!1===m.intersectsBox(s.boundingBox))return;let n;if(s.isBufferGeometry){const r=s.index,a=s.attributes.position,o=s.morphAttributes.position,h=s.morphTargetsRelative,u=s.attributes.uv,c=s.attributes.uv2,l=s.groups,d=s.drawRange;if(null!==r)if(Array.isArray(i))for(let s=0,p=l.length;ss.far?null:{distance:u,point:R.clone(),object:t}}(t,e,s,n,y,g,x,z);if(k){l&&(S.fromBufferAttribute(l,p),A.fromBufferAttribute(l,m),E.fromBufferAttribute(l,f),k.uv=u.a.getUV(z,y,g,x,S,A,E,new r.a)),d&&(S.fromBufferAttribute(d,p),A.fromBufferAttribute(d,m),E.fromBufferAttribute(d,f),k.uv2=u.a.getUV(z,y,g,x,S,A,E,new r.a));const t={a:p,b:m,c:f,normal:new 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i=t.elements,r=i[0],n=i[4],o=i[8],h=i[1],u=i[5],c=i[9],l=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(a.d)(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,p),this._z=Math.atan2(-n,r)):(this._x=Math.atan2(d,u),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(a.d)(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,p),this._z=Math.atan2(h,u)):(this._y=Math.atan2(-l,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(a.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-l,p),this._z=Math.atan2(-n,u)):(this._y=0,this._z=Math.atan2(h,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(a.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(h,r)):(this._x=0,this._z=Math.atan2(-n,u));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(a.d)(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(-c,u),this._y=Math.atan2(-l,r)):(this._x=0,this._y=Math.atan2(o,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(a.d)(n,-1,1)),Math.abs(n)<.9999999?(this._x=Math.atan2(d,u),this._y=Math.atan2(o,r)):(this._x=Math.atan2(-c,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: 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this._onChangeCallback=t,this}_onChangeCallback(){}}u.prototype.isEuler=!0,u.DefaultOrder=\\\\\\\"XYZ\\\\\\\",u.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),s.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,s){const i=this;let r,n=!1,a=0,o=0;const h=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=s,this.itemStart=function(t){o++,!1===n&&void 0!==i.onStart&&i.onStart(t,a,o),n=!0},this.itemEnd=function(t){a++,void 0!==i.onProgress&&i.onProgress(t,a,o),a===o&&(n=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return h.push(t,e),this},this.removeHandler=function(t){const e=h.indexOf(t);return-1!==e&&h.splice(e,2),this},this.getHandler=function(t){for(let e=0,s=h.length;e1?null:e.copy(s).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),s=this.distanceToPoint(t.end);return e<0&&s>0||s<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const s=e||o.getNormalMatrix(t),i=this.coplanarPoint(n).applyMatrix4(t),r=this.normal.applyMatrix3(s).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}h.prototype.isPlane=!0},,function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return n}));var i=s(10),r=s(6);class n extends i.a{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new r.a(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}n.prototype.isLight=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return n}));var i=s(23),r=s(1);class n extends i.a{constructor(t=null,e=1,s=1,i,n,a,o,h,u=r.ob,c=r.ob,l,d){super(null,a,o,h,u,c,i,n,l,d),this.image={data:t,width:e,height:s},this.magFilter=u,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}n.prototype.isDataTexture=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=s(41),r=s(0),n=s(4);const a=new r.a,o=new r.a;class h extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,s=[];for(let t=0,i=e.count;t=r)break t;{const a=e[1];t=r)break e}n=s,s=0}}for(;s>>1;t0)if(p=h*c-u,m=h*u-c,y=r*d,p>=0)if(m>=-y)if(m<=y){const t=1/d;p*=t,m*=t,f=p*(p+h*m+2*u)+m*(h*p+m+2*c)+l}else m=r,p=Math.max(0,-(h*m+u)),f=-p*p+m*(m+2*c)+l;else m=-r,p=Math.max(0,-(h*m+u)),f=-p*p+m*(m+2*c)+l;else m<=-y?(p=Math.max(0,-(-h*r+u)),m=p>0?-r:Math.min(Math.max(-r,-c),r),f=-p*p+m*(m+2*c)+l):m<=y?(p=0,m=Math.min(Math.max(-r,-c),r),f=m*(m+2*c)+l):(p=Math.max(0,-(h*r+u)),m=p>0?r:Math.min(Math.max(-r,-c),r),f=-p*p+m*(m+2*c)+l);else m=h>0?-r:r,p=Math.max(0,-(h*m+u)),f=-p*p+m*(m+2*c)+l;return s&&s.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(a).multiplyScalar(m).add(n),f}intersectSphere(t,e){r.subVectors(t.center,this.origin);const s=r.dot(this.direction),i=r.dot(r)-s*s,n=t.radius*t.radius;if(i>n)return null;const a=Math.sqrt(n-i),o=s-a,h=s+a;return o<0&&h<0?null:o<0?this.at(h,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const s=-(this.origin.dot(t.normal)+t.constant)/e;return s>=0?s:null}intersectPlane(t,e){const s=this.distanceToPlane(t);return null===s?null:this.at(s,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let s,i,r,n,a,o;const h=1/this.direction.x,u=1/this.direction.y,c=1/this.direction.z,l=this.origin;return h>=0?(s=(t.min.x-l.x)*h,i=(t.max.x-l.x)*h):(s=(t.max.x-l.x)*h,i=(t.min.x-l.x)*h),u>=0?(r=(t.min.y-l.y)*u,n=(t.max.y-l.y)*u):(r=(t.max.y-l.y)*u,n=(t.min.y-l.y)*u),s>n||r>i?null:((r>s||s!=s)&&(s=r),(n=0?(a=(t.min.z-l.z)*c,o=(t.max.z-l.z)*c):(a=(t.max.z-l.z)*c,o=(t.min.z-l.z)*c),s>o||a>i?null:((a>s||s!=s)&&(s=a),(o=0?s:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,r)}intersectTriangle(t,e,s,i,r){h.subVectors(e,t),u.subVectors(s,t),c.crossVectors(h,u);let n,a=this.direction.dot(c);if(a>0){if(i)return null;n=1}else{if(!(a<0))return null;n=-1,a=-a}o.subVectors(this.origin,t);const l=n*this.direction.dot(u.crossVectors(o,u));if(l<0)return null;const d=n*this.direction.dot(h.cross(o));if(d<0)return null;if(l+d>a)return null;const p=-n*o.dot(c);return p<0?null:this.at(p/a,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return m}));var i=s(0);const r=new i.a,n=new i.a,a=new i.a,o=new i.a,h=new i.a,u=new i.a,c=new i.a,l=new i.a,d=new i.a,p=new i.a;class m{constructor(t=new i.a,e=new i.a,s=new i.a){this.a=t,this.b=e,this.c=s}static getNormal(t,e,s,i){i.subVectors(s,e),r.subVectors(t,e),i.cross(r);const n=i.lengthSq();return n>0?i.multiplyScalar(1/Math.sqrt(n)):i.set(0,0,0)}static getBarycoord(t,e,s,i,o){r.subVectors(i,e),n.subVectors(s,e),a.subVectors(t,e);const h=r.dot(r),u=r.dot(n),c=r.dot(a),l=n.dot(n),d=n.dot(a),p=h*l-u*u;if(0===p)return o.set(-2,-1,-1);const m=1/p,f=(l*c-u*d)*m,y=(h*d-u*c)*m;return o.set(1-f-y,y,f)}static containsPoint(t,e,s,i){return this.getBarycoord(t,e,s,i,o),o.x>=0&&o.y>=0&&o.x+o.y<=1}static getUV(t,e,s,i,r,n,a,h){return this.getBarycoord(t,e,s,i,o),h.set(0,0),h.addScaledVector(r,o.x),h.addScaledVector(n,o.y),h.addScaledVector(a,o.z),h}static isFrontFacing(t,e,s,i){return r.subVectors(s,e),n.subVectors(t,e),r.cross(n).dot(i)<0}set(t,e,s){return this.a.copy(t),this.b.copy(e),this.c.copy(s),this}setFromPointsAndIndices(t,e,s,i){return this.a.copy(t[e]),this.b.copy(t[s]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,s,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,s),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return r.subVectors(this.c,this.b),n.subVectors(this.a,this.b),.5*r.cross(n).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return m.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return m.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,s,i,r){return m.getUV(t,this.a,this.b,this.c,e,s,i,r)}containsPoint(t){return m.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return m.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const s=this.a,i=this.b,r=this.c;let n,a;h.subVectors(i,s),u.subVectors(r,s),l.subVectors(t,s);const o=h.dot(l),m=u.dot(l);if(o<=0&&m<=0)return e.copy(s);d.subVectors(t,i);const f=h.dot(d),y=u.dot(d);if(f>=0&&y<=f)return e.copy(i);const g=o*y-f*m;if(g<=0&&o>=0&&f<=0)return n=o/(o-f),e.copy(s).addScaledVector(h,n);p.subVectors(t,r);const x=h.dot(p),b=u.dot(p);if(b>=0&&x<=b)return e.copy(r);const M=x*m-o*b;if(M<=0&&m>=0&&b<=0)return a=m/(m-b),e.copy(s).addScaledVector(u,a);const w=f*b-x*y;if(w<=0&&y-f>=0&&x-b>=0)return c.subVectors(r,i),a=(y-f)/(y-f+(x-b)),e.copy(i).addScaledVector(c,a);const v=1/(w+M+g);return n=M*v,a=g*v,e.copy(s).addScaledVector(h,n).addScaledVector(u,a)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return y}));var i=s(18),r=s(39),n=s(5),a=s(10),o=s(0),h=s(24),u=s(7),c=s(4);const l=new o.a,d=new o.a,p=new n.a,m=new r.a,f=new i.a;class y extends a.a{constructor(t=new u.a,e=new h.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,s=[0];for(let t=1,i=e.count;th)continue;d.applyMatrix4(this.matrixWorld);const o=t.ray.origin.distanceTo(d);ot.far||e.push({distance:o,point:l.clone().applyMatrix4(this.matrixWorld),index:s,face:null,faceIndex:null,object:this})}}else{for(let s=Math.max(0,n.start),i=Math.min(r.count,n.start+n.count)-1;sh)continue;d.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(d);it.far||e.push({distance:i,point:l.clone().applyMatrix4(this.matrixWorld),index:s,face:null,faceIndex:null,object:this})}}}else s.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,s=Object.keys(e);if(s.length>0){const t=e[s[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,s=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}y.prototype.isLine=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return n}));var i=s(12),r=s(6);class n extends i.a{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new r.a(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}n.prototype.isPointsMaterial=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{static decodeText(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let s=0,i=t.length;s0){const t=e[s[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,s=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function f(t,e,s,i,r,n,a){const h=l.distanceSqToPoint(t);if(hr.far)return;n.push({distance:u,distanceToRay:Math.sqrt(h),point:s,index:e,face:null,object:a})}}m.prototype.isPoints=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=s(26);class r extends i.a{}r.prototype.ValueTypeName=\\\\\\\"number\\\\\\\"},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=s(26);class r extends i.a{}r.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\"},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=s(10);class r extends i.a{constructor(){super(),this.type=\\\\\\\"Bone\\\\\\\"}}r.prototype.isBone=!0},,function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=s(1),r=s(26),n=s(38),a=s(8);class o extends n.a{constructor(t,e,s,i){super(t,e,s,i)}interpolate_(t,e,s,i){const r=this.resultBuffer,n=this.sampleValues,o=this.valueSize,h=(s-e)/(i-e);let u=t*o;for(let t=u+o;u!==t;u+=4)a.a.slerpFlat(r,0,n,u-o,n,u,h);return r}}class h extends r.a{InterpolantFactoryMethodLinear(t){return new o(this.times,this.values,this.getValueSize(),t)}}h.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",h.prototype.DefaultInterpolation=i.P,h.prototype.InterpolantFactoryMethodSmooth=void 0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=s(1),r=s(12),n=s(2),a=s(6);class o extends r.a{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshStandardMaterial\\\\\\\",this.color=new a.a(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new a.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=i.Uc,this.normalScale=new n.a(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}o.prototype.isMeshStandardMaterial=!0},,,function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return m}));var i=s(33),r=s(45),n=s(30),a=s(5),o=s(2),h=s(0),u=s(9);const c=new a.a,l=new h.a,d=new h.a;class p extends r.a{constructor(){super(new n.a(90,1,.5,500)),this._frameExtents=new o.a(4,2),this._viewportCount=6,this._viewports=[new u.a(2,1,1,1),new u.a(0,1,1,1),new u.a(3,1,1,1),new u.a(1,1,1,1),new u.a(3,0,1,1),new u.a(1,0,1,1)],this._cubeDirections=[new h.a(1,0,0),new h.a(-1,0,0),new h.a(0,0,1),new h.a(0,0,-1),new h.a(0,1,0),new h.a(0,-1,0)],this._cubeUps=[new h.a(0,1,0),new h.a(0,1,0),new h.a(0,1,0),new h.a(0,1,0),new h.a(0,0,1),new h.a(0,0,-1)]}updateMatrices(t,e=0){const s=this.camera,i=this.matrix,r=t.distance||s.far;r!==s.far&&(s.far=r,s.updateProjectionMatrix()),l.setFromMatrixPosition(t.matrixWorld),s.position.copy(l),d.copy(s.position),d.add(this._cubeDirections[e]),s.up.copy(this._cubeUps[e]),s.lookAt(d),s.updateMatrixWorld(),i.makeTranslation(-l.x,-l.y,-l.z),c.multiplyMatrices(s.projectionMatrix,s.matrixWorldInverse),this._frustum.setFromProjectionMatrix(c)}}p.prototype.isPointLightShadow=!0;class m extends i.a{constructor(t,e,s=0,i=1){super(t,e),this.type=\\\\\\\"PointLight\\\\\\\",this.distance=s,this.decay=i,this.shadow=new p}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}m.prototype.isPointLight=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=s(2),r=s(55),n=s(6),a=s(3);class o extends r.a{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshPhysicalMaterial\\\\\\\",this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new i.a(1,1),this.clearcoatNormalMap=null,this.ior=1.5,Object.defineProperty(this,\\\\\\\"reflectivity\\\\\\\",{get:function(){return a.d(2.5*(this.ior-1)/(this.ior+1),0,1)},set:function(t){this.ior=(1+.4*t)/(1-.4*t)}}),this.sheenTint=new n.a(0),this.sheenRoughness=1,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new n.a(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new n.a(1,1,1),this.specularTintMap=null,this._sheen=0,this._clearcoat=0,this._transmission=0,this.setValues(t)}get sheen(){return this._sheen}set sheen(t){this._sheen>0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}o.prototype.isMeshPhysicalMaterial=!0},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=s(0),r=s(18),n=s(31);const a=new r.a,o=new i.a;class h{constructor(t=new n.a,e=new n.a,s=new n.a,i=new n.a,r=new n.a,a=new n.a){this.planes=[t,e,s,i,r,a]}set(t,e,s,i,r,n){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(s),a[3].copy(i),a[4].copy(r),a[5].copy(n),this}copy(t){const e=this.planes;for(let s=0;s<6;s++)e[s].copy(t.planes[s]);return this}setFromProjectionMatrix(t){const e=this.planes,s=t.elements,i=s[0],r=s[1],n=s[2],a=s[3],o=s[4],h=s[5],u=s[6],c=s[7],l=s[8],d=s[9],p=s[10],m=s[11],f=s[12],y=s[13],g=s[14],x=s[15];return e[0].setComponents(a-i,c-o,m-l,x-f).normalize(),e[1].setComponents(a+i,c+o,m+l,x+f).normalize(),e[2].setComponents(a+r,c+h,m+d,x+y).normalize(),e[3].setComponents(a-r,c-h,m-d,x-y).normalize(),e[4].setComponents(a-n,c-u,m-p,x-g).normalize(),e[5].setComponents(a+n,c+u,m+p,x+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),a.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(a)}intersectsSprite(t){return a.center.set(0,0,0),a.radius=.7071067811865476,a.applyMatrix4(t.matrixWorld),this.intersectsSphere(a)}intersectsSphere(t){const e=this.planes,s=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(s)0?t.max.x:t.min.x,o.y=i.normal.y>0?t.max.y:t.min.y,o.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(o)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let s=0;s<6;s++)if(e[s].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}},,,function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=s(17),r=s(13),n=s(20);class a extends r.a{constructor(t){super(t)}load(t,e,s,r){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const a=this,o=i.a.get(t);if(void 0!==o)return a.manager.itemStart(t),setTimeout((function(){e&&e(o),a.manager.itemEnd(t)}),0),o;const h=Object(n.b)(\\\\\\\"img\\\\\\\");function u(){h.removeEventListener(\\\\\\\"load\\\\\\\",u,!1),h.removeEventListener(\\\\\\\"error\\\\\\\",c,!1),i.a.add(t,this),e&&e(this),a.manager.itemEnd(t)}function c(e){h.removeEventListener(\\\\\\\"load\\\\\\\",u,!1),h.removeEventListener(\\\\\\\"error\\\\\\\",c,!1),r&&r(e),a.manager.itemError(t),a.manager.itemEnd(t)}return h.addEventListener(\\\\\\\"load\\\\\\\",u,!1),h.addEventListener(\\\\\\\"error\\\\\\\",c,!1),\\\\\\\"data:\\\\\\\"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(h.crossOrigin=this.crossOrigin),a.manager.itemStart(t),h.src=t,h}}},function(t,e,s){\\\\\\\"use strict\\\\\\\";s.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));var i=s(19),r=s(26),n=s(1);class a extends r.a{}a.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",a.prototype.ValueBufferType=Array,a.prototype.DefaultInterpolation=n.O,a.prototype.InterpolantFactoryMethodLinear=void 0,a.prototype.InterpolantFactoryMethodSmooth=void 0;class o extends r.a{}o.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";var h=s(50),u=s(54);class c extends r.a{}c.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",c.prototype.ValueBufferType=Array,c.prototype.DefaultInterpolation=n.O,c.prototype.InterpolantFactoryMethodLinear=void 0,c.prototype.InterpolantFactoryMethodSmooth=void 0;var l=s(51),d=s(3);class p{constructor(t,e=-1,s,i=n.wb){this.name=t,this.tracks=s,this.duration=e,this.blendMode=i,this.uuid=d.h(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],s=t.tracks,i=1/(t.fps||1);for(let t=0,r=s.length;t!==r;++t)e.push(m(s[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],s=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=s.length;t!==i;++t)e.push(r.a.toJSON(s[t]));return i}static CreateFromMorphTargetSequence(t,e,s,r){const n=e.length,a=[];for(let t=0;t1){const t=n[1];let e=i[t];e||(i[t]=e=[]),e.push(s)}}const n=[];for(const t in i)n.push(this.CreateFromMorphTargetSequence(t,i[t],e,s));return n}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const s=function(t,e,s,r,n){if(0!==s.length){const a=[],o=[];i.a.flattenJSON(s,a,o,r),0!==a.length&&n.push(new t(e,a,o))}},r=[],n=t.name||\\\\\\\"default\\\\\\\",a=t.fps||30,o=t.blendMode;let c=t.length||-1;const d=t.hierarchy||[];for(let t=0;t=2.0 are supported.\\\\\\\")));const h=new Wt(o,{path:e||this.resourcePath||\\\\\\\"\\\\\\\",crossOrigin:this.crossOrigin,requestHeader:this.requestHeader,manager:this.manager,ktx2Loader:this.ktx2Loader,meshoptDecoder:this.meshoptDecoder});h.fileLoader.setRequestHeader(this.requestHeader);for(let t=0;t=0&&void 0===a[e]&&console.warn('THREE.GLTFLoader: Unknown extension \\\\\\\"'+e+'\\\\\\\".')}}h.setExtensions(n),h.setPlugins(a),h.parse(s,i)}}function K(){let t={};return{get:function(e){return t[e]},add:function(e,s){t[e]=s},remove:function(e){delete t[e]},removeAll:function(){t={}}}}const Q={KHR_BINARY_GLTF:\\\\\\\"KHR_binary_glTF\\\\\\\",KHR_DRACO_MESH_COMPRESSION:\\\\\\\"KHR_draco_mesh_compression\\\\\\\",KHR_LIGHTS_PUNCTUAL:\\\\\\\"KHR_lights_punctual\\\\\\\",KHR_MATERIALS_CLEARCOAT:\\\\\\\"KHR_materials_clearcoat\\\\\\\",KHR_MATERIALS_IOR:\\\\\\\"KHR_materials_ior\\\\\\\",KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS:\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\",KHR_MATERIALS_SPECULAR:\\\\\\\"KHR_materials_specular\\\\\\\",KHR_MATERIALS_TRANSMISSION:\\\\\\\"KHR_materials_transmission\\\\\\\",KHR_MATERIALS_UNLIT:\\\\\\\"KHR_materials_unlit\\\\\\\",KHR_MATERIALS_VOLUME:\\\\\\\"KHR_materials_volume\\\\\\\",KHR_TEXTURE_BASISU:\\\\\\\"KHR_texture_basisu\\\\\\\",KHR_TEXTURE_TRANSFORM:\\\\\\\"KHR_texture_transform\\\\\\\",KHR_MESH_QUANTIZATION:\\\\\\\"KHR_mesh_quantization\\\\\\\",EXT_TEXTURE_WEBP:\\\\\\\"EXT_texture_webp\\\\\\\",EXT_MESHOPT_COMPRESSION:\\\\\\\"EXT_meshopt_compression\\\\\\\"};class ${constructor(t){this.parser=t,this.name=Q.KHR_LIGHTS_PUNCTUAL,this.cache={refs:{},uses:{}}}_markDefs(){const t=this.parser,e=this.parser.json.nodes||[];for(let s=0,i=e.length;s=0)throw new Error(\\\\\\\"THREE.GLTFLoader: setKTX2Loader must be called before loading KTX2 textures\\\\\\\");return null}return e.loadTextureImage(t,n,a)}}class ot{constructor(t){this.parser=t,this.name=Q.EXT_TEXTURE_WEBP,this.isSupported=null}loadTexture(t){const e=this.name,s=this.parser,i=s.json,r=i.textures[t];if(!r.extensions||!r.extensions[e])return null;const n=r.extensions[e],a=i.images[n.source];let o=s.textureLoader;if(a.uri){const t=s.options.manager.getHandler(a.uri);null!==t&&(o=t)}return this.detectSupport().then((function(r){if(r)return s.loadTextureImage(t,a,o);if(i.extensionsRequired&&i.extensionsRequired.indexOf(e)>=0)throw new Error(\\\\\\\"THREE.GLTFLoader: WebP required by asset but unsupported.\\\\\\\");return s.loadTexture(t)}))}detectSupport(){return this.isSupported||(this.isSupported=new Promise((function(t){const e=new Image;e.src=\\\\\\\"data:image/webp;base64,UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoBAAEADsD+JaQAA3AAAAAA\\\\\\\",e.onload=e.onerror=function(){t(1===e.height)}}))),this.isSupported}}class ht{constructor(t){this.name=Q.EXT_MESHOPT_COMPRESSION,this.parser=t}loadBufferView(t){const e=this.parser.json,s=e.bufferViews[t];if(s.extensions&&s.extensions[this.name]){const t=s.extensions[this.name],i=this.parser.getDependency(\\\\\\\"buffer\\\\\\\",t.buffer),r=this.parser.options.meshoptDecoder;if(!r||!r.supported){if(e.extensionsRequired&&e.extensionsRequired.indexOf(this.name)>=0)throw new Error(\\\\\\\"THREE.GLTFLoader: setMeshoptDecoder must be called before loading compressed files\\\\\\\");return null}return Promise.all([i,r.ready]).then((function(e){const s=t.byteOffset||0,i=t.byteLength||0,n=t.count,a=t.byteStride,o=new ArrayBuffer(n*a),h=new Uint8Array(e[0],s,i);return r.decodeGltfBuffer(new Uint8Array(o),n,a,h,t.mode,t.filter),o}))}return null}}const ut=\\\\\\\"glTF\\\\\\\",ct=1313821514,lt=5130562;class dt{constructor(t){this.name=Q.KHR_BINARY_GLTF,this.content=null,this.body=null;const e=new DataView(t,0,12);if(this.header={magic:_.a.decodeText(new Uint8Array(t.slice(0,4))),version:e.getUint32(4,!0),length:e.getUint32(8,!0)},this.header.magic!==ut)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported glTF-Binary header.\\\\\\\");if(this.header.version<2)throw new Error(\\\\\\\"THREE.GLTFLoader: Legacy binary file detected.\\\\\\\");const s=this.header.length-12,i=new DataView(t,12);let r=0;for(;r\\\\\\\",e).replace(\\\\\\\"#include \\\\\\\",s).replace(\\\\\\\"#include \\\\\\\",i).replace(\\\\\\\"#include \\\\\\\",r).replace(\\\\\\\"#include \\\\\\\",n)},Object.defineProperties(this,{specular:{get:function(){return a.specular.value},set:function(t){a.specular.value=t}},specularMap:{get:function(){return a.specularMap.value},set:function(t){a.specularMap.value=t,t?this.defines.USE_SPECULARMAP=\\\\\\\"\\\\\\\":delete this.defines.USE_SPECULARMAP}},glossiness:{get:function(){return a.glossiness.value},set:function(t){a.glossiness.value=t}},glossinessMap:{get:function(){return a.glossinessMap.value},set:function(t){a.glossinessMap.value=t,t?(this.defines.USE_GLOSSINESSMAP=\\\\\\\"\\\\\\\",this.defines.USE_UV=\\\\\\\"\\\\\\\"):(delete this.defines.USE_GLOSSINESSMAP,delete this.defines.USE_UV)}}}),delete this.metalness,delete this.roughness,delete this.metalnessMap,delete this.roughnessMap,this.setValues(t)}copy(t){return super.copy(t),this.specularMap=t.specularMap,this.specular.copy(t.specular),this.glossinessMap=t.glossinessMap,this.glossiness=t.glossiness,delete this.metalness,delete this.roughness,delete this.metalnessMap,delete this.roughnessMap,this}}class yt{constructor(){this.name=Q.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS,this.specularGlossinessParams=[\\\\\\\"color\\\\\\\",\\\\\\\"map\\\\\\\",\\\\\\\"lightMap\\\\\\\",\\\\\\\"lightMapIntensity\\\\\\\",\\\\\\\"aoMap\\\\\\\",\\\\\\\"aoMapIntensity\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"emissiveIntensity\\\\\\\",\\\\\\\"emissiveMap\\\\\\\",\\\\\\\"bumpMap\\\\\\\",\\\\\\\"bumpScale\\\\\\\",\\\\\\\"normalMap\\\\\\\",\\\\\\\"normalMapType\\\\\\\",\\\\\\\"displacementMap\\\\\\\",\\\\\\\"displacementScale\\\\\\\",\\\\\\\"displacementBias\\\\\\\",\\\\\\\"specularMap\\\\\\\",\\\\\\\"specular\\\\\\\",\\\\\\\"glossinessMap\\\\\\\",\\\\\\\"glossiness\\\\\\\",\\\\\\\"alphaMap\\\\\\\",\\\\\\\"envMap\\\\\\\",\\\\\\\"envMapIntensity\\\\\\\",\\\\\\\"refractionRatio\\\\\\\"]}getMaterialType(){return ft}extendParams(t,e,s){const i=e.extensions[this.name];t.color=new u.a(1,1,1),t.opacity=1;const r=[];if(Array.isArray(i.diffuseFactor)){const e=i.diffuseFactor;t.color.fromArray(e),t.opacity=e[3]}if(void 0!==i.diffuseTexture&&r.push(s.assignTexture(t,\\\\\\\"map\\\\\\\",i.diffuseTexture)),t.emissive=new u.a(0,0,0),t.glossiness=void 0!==i.glossinessFactor?i.glossinessFactor:1,t.specular=new u.a(1,1,1),Array.isArray(i.specularFactor)&&t.specular.fromArray(i.specularFactor),void 0!==i.specularGlossinessTexture){const e=i.specularGlossinessTexture;r.push(s.assignTexture(t,\\\\\\\"glossinessMap\\\\\\\",e)),r.push(s.assignTexture(t,\\\\\\\"specularMap\\\\\\\",e))}return Promise.all(r)}createMaterial(t){const e=new ft(t);return e.fog=!0,e.color=t.color,e.map=void 0===t.map?null:t.map,e.lightMap=null,e.lightMapIntensity=1,e.aoMap=void 0===t.aoMap?null:t.aoMap,e.aoMapIntensity=1,e.emissive=t.emissive,e.emissiveIntensity=1,e.emissiveMap=void 0===t.emissiveMap?null:t.emissiveMap,e.bumpMap=void 0===t.bumpMap?null:t.bumpMap,e.bumpScale=1,e.normalMap=void 0===t.normalMap?null:t.normalMap,e.normalMapType=h.Uc,t.normalScale&&(e.normalScale=t.normalScale),e.displacementMap=null,e.displacementScale=1,e.displacementBias=0,e.specularMap=void 0===t.specularMap?null:t.specularMap,e.specular=t.specular,e.glossinessMap=void 0===t.glossinessMap?null:t.glossinessMap,e.glossiness=t.glossiness,e.alphaMap=null,e.envMap=void 0===t.envMap?null:t.envMap,e.envMapIntensity=1,e.refractionRatio=.98,e}}class gt{constructor(){this.name=Q.KHR_MESH_QUANTIZATION}}class xt extends x.a{constructor(t,e,s,i){super(t,e,s,i)}copySampleValue_(t){const e=this.resultBuffer,s=this.sampleValues,i=this.valueSize,r=t*i*3+i;for(let t=0;t!==i;t++)e[t]=s[r+t];return e}}xt.prototype.beforeStart_=xt.prototype.copySampleValue_,xt.prototype.afterEnd_=xt.prototype.copySampleValue_,xt.prototype.interpolate_=function(t,e,s,i){const r=this.resultBuffer,n=this.sampleValues,a=this.valueSize,o=2*a,h=3*a,u=i-e,c=(s-e)/u,l=c*c,d=l*c,p=t*h,m=p-h,f=-2*d+3*l,y=d-l,g=1-f,x=y-l+c;for(let t=0;t!==a;t++){const e=n[m+t+a],s=n[m+t+o]*u,i=n[p+t+a],h=n[p+t]*u;r[t]=g*e+x*s+f*i+y*h}return r};const bt=new V.a;class Mt extends xt{interpolate_(t,e,s,i){const r=super.interpolate_(t,e,s,i);return bt.fromArray(r).normalize().toArray(r),r}}const wt=0,vt=1,_t=2,Tt=3,St=4,At=5,Et=6,zt={5120:Int8Array,5121:Uint8Array,5122:Int16Array,5123:Uint16Array,5125:Uint32Array,5126:Float32Array},Rt={9728:h.ob,9729:h.V,9984:h.sb,9985:h.Z,9986:h.rb,9987:h.Y},It={33071:h.n,33648:h.kb,10497:h.wc},kt={SCALAR:1,VEC2:2,VEC3:3,VEC4:4,MAT2:4,MAT3:9,MAT4:16},Lt={POSITION:\\\\\\\"position\\\\\\\",NORMAL:\\\\\\\"normal\\\\\\\",TANGENT:\\\\\\\"tangent\\\\\\\",TEXCOORD_0:\\\\\\\"uv\\\\\\\",TEXCOORD_1:\\\\\\\"uv2\\\\\\\",COLOR_0:\\\\\\\"color\\\\\\\",WEIGHTS_0:\\\\\\\"skinWeight\\\\\\\",JOINTS_0:\\\\\\\"skinIndex\\\\\\\"},Ot={scale:\\\\\\\"scale\\\\\\\",translation:\\\\\\\"position\\\\\\\",rotation:\\\\\\\"quaternion\\\\\\\",weights:\\\\\\\"morphTargetInfluences\\\\\\\"},Pt={CUBICSPLINE:void 0,LINEAR:h.P,STEP:h.O},Bt=\\\\\\\"OPAQUE\\\\\\\",Nt=\\\\\\\"MASK\\\\\\\",Ct=\\\\\\\"BLEND\\\\\\\";function Ft(t,e){return\\\\\\\"string\\\\\\\"!=typeof t||\\\\\\\"\\\\\\\"===t?\\\\\\\"\\\\\\\":(/^https?:\\\\/\\\\//i.test(e)&&/^\\\\//.test(t)&&(e=e.replace(/(^https?:\\\\/\\\\/[^\\\\/]+).*/i,\\\\\\\"$1\\\\\\\")),/^(https?:)?\\\\/\\\\//i.test(t)||/^data:.*,.*$/i.test(t)||/^blob:.*$/i.test(t)?t:e+t)}function Vt(t,e,s){for(const i in s.extensions)void 0===t[i]&&(e.userData.gltfExtensions=e.userData.gltfExtensions||{},e.userData.gltfExtensions[i]=s.extensions[i])}function Ht(t,e){void 0!==e.extras&&(\\\\\\\"object\\\\\\\"==typeof e.extras?Object.assign(t.userData,e.extras):console.warn(\\\\\\\"THREE.GLTFLoader: Ignoring primitive type .extras, \\\\\\\"+e.extras))}function Ut(t,e){if(t.updateMorphTargets(),void 0!==e.weights)for(let s=0,i=e.weights.length;s{const s=this.associations.get(t);null!=s&&this.associations.set(e,s);for(const[s,i]of t.children.entries())r(i,e.children[s])};return r(s,i),i.name+=\\\\\\\"_instance_\\\\\\\"+t.uses[e]++,i}_invokeOne(t){const e=Object.values(this.plugins);e.push(this);for(let s=0;s=2&&m.setY(e,l[t*n+1]),n>=3&&m.setZ(e,l[t*n+2]),n>=4&&m.setW(e,l[t*n+3]),n>=5)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported itemSize in sparse BufferAttribute.\\\\\\\")}}return m}))}loadTexture(t){const e=this.json,s=this.options,i=e.textures[t],r=e.images[i.source];let n=this.textureLoader;if(r.uri){const t=s.manager.getHandler(r.uri);null!==t&&(n=t)}return this.loadTextureImage(t,r,n)}loadTextureImage(t,e,s){const i=this,r=this.json,n=this.options,a=r.textures[t],o=(e.uri||e.bufferView)+\\\\\\\":\\\\\\\"+a.sampler;if(this.textureCache[o])return this.textureCache[o];const u=self.URL||self.webkitURL;let c=e.uri||\\\\\\\"\\\\\\\",l=!1;if(void 0!==e.bufferView)c=i.getDependency(\\\\\\\"bufferView\\\\\\\",e.bufferView).then((function(t){l=!0;const s=new Blob([t],{type:e.mimeType});return c=u.createObjectURL(s),c}));else if(void 0===e.uri)throw new Error(\\\\\\\"THREE.GLTFLoader: Image \\\\\\\"+t+\\\\\\\" is missing URI and bufferView\\\\\\\");const d=Promise.resolve(c).then((function(t){return new Promise((function(e,i){let r=e;!0===s.isImageBitmapLoader&&(r=function(t){const s=new W.a(t);s.needsUpdate=!0,e(s)}),s.load(Ft(t,n.path),r,void 0,i)}))})).then((function(e){!0===l&&u.revokeObjectURL(c),e.flipY=!1,a.name&&(e.name=a.name);const s=(r.samplers||{})[a.sampler]||{};return e.magFilter=Rt[s.magFilter]||h.V,e.minFilter=Rt[s.minFilter]||h.Y,e.wrapS=It[s.wrapS]||h.wc,e.wrapT=It[s.wrapT]||h.wc,i.associations.set(e,{textures:t}),e})).catch((function(){return console.error(\\\\\\\"THREE.GLTFLoader: Couldn't load texture\\\\\\\",c),null}));return this.textureCache[o]=d,d}assignTexture(t,e,s){const i=this;return this.getDependency(\\\\\\\"texture\\\\\\\",s.index).then((function(r){if(void 0===s.texCoord||0==s.texCoord||\\\\\\\"aoMap\\\\\\\"===e&&1==s.texCoord||console.warn(\\\\\\\"THREE.GLTFLoader: Custom UV set 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t=i[Q.KHR_MATERIALS_UNLIT];n=t.getMaterialType(),c.push(t.extendParams(a,r,e))}else{const s=r.pbrMetallicRoughness||{};if(a.color=new u.a(1,1,1),a.opacity=1,Array.isArray(s.baseColorFactor)){const t=s.baseColorFactor;a.color.fromArray(t),a.opacity=t[3]}void 0!==s.baseColorTexture&&c.push(e.assignTexture(a,\\\\\\\"map\\\\\\\",s.baseColorTexture)),a.metalness=void 0!==s.metallicFactor?s.metallicFactor:1,a.roughness=void 0!==s.roughnessFactor?s.roughnessFactor:1,void 0!==s.metallicRoughnessTexture&&(c.push(e.assignTexture(a,\\\\\\\"metalnessMap\\\\\\\",s.metallicRoughnessTexture)),c.push(e.assignTexture(a,\\\\\\\"roughnessMap\\\\\\\",s.metallicRoughnessTexture))),n=this._invokeOne((function(e){return e.getMaterialType&&e.getMaterialType(t)})),c.push(Promise.all(this._invokeAll((function(e){return e.extendMaterialParams&&e.extendMaterialParams(t,a)}))))}!0===r.doubleSided&&(a.side=h.z);const l=r.alphaMode||Bt;if(l===Ct?(a.transparent=!0,a.depthWrite=!1):(a.format=h.ic,a.transparent=!1,l===Nt&&(a.alphaTest=void 0!==r.alphaCutoff?r.alphaCutoff:.5)),void 0!==r.normalTexture&&n!==z.a&&(c.push(e.assignTexture(a,\\\\\\\"normalMap\\\\\\\",r.normalTexture)),a.normalScale=new X.a(1,1),void 0!==r.normalTexture.scale)){const t=r.normalTexture.scale;a.normalScale.set(t,t)}return void 0!==r.occlusionTexture&&n!==z.a&&(c.push(e.assignTexture(a,\\\\\\\"aoMap\\\\\\\",r.occlusionTexture)),void 0!==r.occlusionTexture.strength&&(a.aoMapIntensity=r.occlusionTexture.strength)),void 0!==r.emissiveFactor&&n!==z.a&&(a.emissive=(new u.a).fromArray(r.emissiveFactor)),void 0!==r.emissiveTexture&&n!==z.a&&c.push(e.assignTexture(a,\\\\\\\"emissiveMap\\\\\\\",r.emissiveTexture)),Promise.all(c).then((function(){let s;return s=n===ft?i[Q.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS].createMaterial(a):new 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t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,s=this.itemSize;i0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new s).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return 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t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,l)}lookAt(t,e,n){const i=this.elements;return u.subVectors(t,e),0===u.lengthSq()&&(u.z=1),u.normalize(),c.crossVectors(n,u),0===c.lengthSq()&&(1===Math.abs(n.z)?u.x+=1e-4:u.z+=1e-4,u.normalize(),c.crossVectors(n,u)),c.normalize(),h.crossVectors(u,c),i[0]=c.x,i[4]=h.x,i[8]=u.x,i[1]=c.y,i[5]=h.y,i[9]=u.y,i[2]=c.z,i[6]=h.z,i[10]=u.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=r.set(i[0],i[1],i[2]).length();const a=r.set(i[4],i[5],i[6]).length(),l=r.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/s,h=1/a,u=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=h,o.elements[5]*=h,o.elements[6]*=h,o.elements[8]*=u,o.elements[9]*=u,o.elements[10]*=u,e.setFromRotationMatrix(o),n.x=s,n.y=a,n.z=l,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}s.prototype.isMatrix4=!0;const r=new i.a,o=new s,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,h=new i.a,u=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(3);const s={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},r={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class h{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=a(s,i,t+1/3),this.g=a(s,i,t),this.b=a(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=s[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new u.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}s.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),s=n(1),r=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=r.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=s.xb,this.side=s.H,this.vertexColors=!1,this.opacity=1,this.format=s.Ib,this.transparent=!1,this.blendSrc=s.Nc,this.blendDst=s.Db,this.blendEquation=s.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=s.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=s.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=s.R,this.stencilZFail=s.R,this.stencilZPass=s.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===s.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==s.xb&&(n.blending=this.blending),this.side!==s.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==s.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(28);class s{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,s){n.load(t,i,e,s)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),s=n(2),r=n(18),o=n(39),a=n(5),l=n(10),c=n(40),h=n(1),u=n(29),d=n(7);const p=new a.a,_=new o.a,m=new r.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,M=new s.a,E=new s.a,S=new s.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new u.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(s),!1===t.ray.intersectsSphere(m))return;if(p.copy(s).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,r,f,g,v,C);if(O){u&&(M.fromBufferAttribute(u,p),E.fromBufferAttribute(u,_),S.fromBufferAttribute(u,m),O.uv=c.a.getUV(C,f,g,v,M,E,S,new s.a)),d&&(M.fromBufferAttribute(d,p),E.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,M,E,S,new s.a));const t={a:p,b:_,c:m,normal:new 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y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,s=i[0],r=i[4],a=i[8],l=i[1],c=i[5],h=i[9],u=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(o.d)(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-h,p),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-u,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-u,p),this._z=Math.atan2(-r,c)):(this._y=0,this._z=Math.atan2(l,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,s)):(this._x=0,this._z=Math.atan2(-r,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-h,c),this._y=Math.atan2(-u,s)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,s)):(this._x=Math.atan2(-h,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return a.makeRotationFromQuaternion(t),this.setFromRotationMatrix(a,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return l.setFromEuler(this),this.setFromQuaternion(l,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new s.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}c.prototype.isEuler=!0,c.DefaultOrder=\\\\\\\"XYZ\\\\\\\",c.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let s,r=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===r&&void 0!==i.onStart&&i.onStart(t,o,a),r=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(r=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return s?s(t):t},this.setURLModifier=function(t){return s=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e1?null:e.copy(n).multiplyScalar(s).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||a.getNormalMatrix(t),i=this.coplanarPoint(r).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}l.prototype.isPlane=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";function i(t,e,n,i,s){const r=.5*(i-e),o=.5*(s-n),a=t*t;return(2*n-2*i+r+o)*(t*a)+(-3*n+3*i-2*r-o)*a+r*t+n}function s(t,e,n,i){return function(t,e){const n=1-t;return n*n*e}(t,e)+function(t,e){return 2*(1-t)*t*e}(t,n)+function(t,e){return t*t*e}(t,i)}function r(t,e,n,i,s){return function(t,e){const n=1-t;return n*n*n*e}(t,e)+function(t,e){const n=1-t;return 3*n*n*t*e}(t,n)+function(t,e){return 3*(1-t)*t*t*e}(t,i)+function(t,e){return t*t*t*e}(t,s)}n.d(e,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return s})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return r}))},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(10),s=n(6);class r extends i.a{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new s.a(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}r.prototype.isLight=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(23),s=n(1);class r extends i.a{constructor(t=null,e=1,n=1,i,r,o,a,l,c=s.ob,h=s.ob,u,d){super(null,o,a,l,c,h,i,r,u,d),this.image={data:t,width:e,height:n},this.magFilter=c,this.minFilter=h,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}r.prototype.isDataTexture=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(41),s=n(0),r=n(4);const o=new s.a,a=new s.a;class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;t0)if(p=l*h-c,_=l*c-h,f=s*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*h)+u}else _=s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;else _=-s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;else _<=-f?(p=Math.max(0,-(-l*s+c)),_=p>0?-s:Math.min(Math.max(-s,-h),s),m=-p*p+_*(_+2*h)+u):_<=f?(p=0,_=Math.min(Math.max(-s,-h),s),m=_*(_+2*h)+u):(p=Math.max(0,-(l*s+c)),_=p>0?s:Math.min(Math.max(-s,-h),s),m=-p*p+_*(_+2*h)+u);else _=l>0?-s:s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(r),m}intersectSphere(t,e){s.subVectors(t.center,this.origin);const n=s.dot(this.direction),i=s.dot(s)-n*n,r=t.radius*t.radius;if(i>r)return null;const o=Math.sqrt(r-i),a=n-o,l=n+o;return a<0&&l<0?null:a<0?this.at(l,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,s)}intersectTriangle(t,e,n,i,s){l.subVectors(e,t),c.subVectors(n,t),h.crossVectors(l,c);let r,o=this.direction.dot(h);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}a.subVectors(this.origin,t);const u=r*this.direction.dot(c.crossVectors(a,c));if(u<0)return null;const d=r*this.direction.dot(l.cross(a));if(d<0)return null;if(u+d>o)return null;const p=-r*a.dot(h);return p<0?null:this.at(p/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const s=new i.a,r=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,h=new i.a,u=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),s.subVectors(t,e),i.cross(s);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){s.subVectors(i,e),r.subVectors(n,e),o.subVectors(t,e);const l=s.dot(s),c=s.dot(r),h=s.dot(o),u=r.dot(r),d=r.dot(o),p=l*u-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(u*h-c*d)*_,f=(l*d-c*h)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,s,r,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(s,a.x),l.addScaledVector(r,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return s.subVectors(n,e),r.subVectors(t,e),s.cross(r).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return s.subVectors(this.c,this.b),r.subVectors(this.a,this.b),.5*s.cross(r).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return _.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;l.subVectors(i,n),c.subVectors(s,n),u.subVectors(t,n);const a=l.dot(u),_=c.dot(u);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return r=a/(a-m),e.copy(n).addScaledVector(l,r);p.subVectors(t,s);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(s);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return h.subVectors(s,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(h,o);const w=1/(b+x+g);return r=x*w,o=g*w,e.copy(n).addScaledVector(l,r).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),s=n(39),r=n(5),o=n(10),a=n(0),l=n(24),c=n(7),h=n(4);const u=new a.a,d=new a.a,p=new r.a,_=new s.a,m=new i.a;class f extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;tl)continue;d.applyMatrix4(this.matrixWorld);const a=t.ray.origin.distanceTo(d);at.far||e.push({distance:a,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;nl)continue;d.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(d);it.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. 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e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return s.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}r.prototype.isBox2=!0;var o=n(7),a=n(22),l=n(4),c=n(13),h=n(11),u=n(47),d=n(46),p=n(48),_=n(53),m=n(0);class f extends c.a{constructor(t){super(t),this.defaultDPI=90,this.defaultUnit=\\\\\\\"px\\\\\\\"}load(t,e,n,i){const s=this,r=new a.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(n))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e=this;function n(t,e,n,i,r,o,a,l){if(0==e||0==n)return void t.lineTo(l.x,l.y);i=i*Math.PI/180,e=Math.abs(e),n=Math.abs(n);const c=(a.x-l.x)/2,h=(a.y-l.y)/2,u=Math.cos(i)*c+Math.sin(i)*h,d=-Math.sin(i)*c+Math.cos(i)*h;let p=e*e,_=n*n;const m=u*u,f=d*d,g=m/p+f/_;if(g>1){const t=Math.sqrt(g);p=(e*=t)*e,_=(n*=t)*n}const v=p*f+_*m,y=(p*_-v)/v;let x=Math.sqrt(Math.max(0,y));r===o&&(x=-x);const b=x*e*d/n,w=-x*n*u/e,T=Math.cos(i)*b-Math.sin(i)*w+(a.x+l.x)/2,A=Math.sin(i)*b+Math.cos(i)*w+(a.y+l.y)/2,M=s(1,0,(u-b)/e,(d-w)/n),E=s((u-b)/e,(d-w)/n,(-u-b)/e,(-d-w)/n)%(2*Math.PI);t.currentPath.absellipse(T,A,e,n,M,M+E,0===o,i)}function s(t,e,n,i){const s=t*n+e*i,r=Math.sqrt(t*t+e*e)*Math.sqrt(n*n+i*i);let o=Math.acos(Math.max(-1,Math.min(1,s/r)));return t*i-e*n<0&&(o=-o),o}function r(t,e){e=Object.assign({},e);let n={};if(t.hasAttribute(\\\\\\\"class\\\\\\\")){const e=t.getAttribute(\\\\\\\"class\\\\\\\").split(/\\\\s/).filter(Boolean).map((t=>t.trim()));for(let t=0;t=0;t--){const s=i[t].trim();if(\\\\\\\"\\\\\\\"===s)continue;const r=s.indexOf(\\\\\\\"(\\\\\\\"),o=s.length;if(r>0&&r=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.translate(t,i)}break;case\\\\\\\"rotate\\\\\\\":if(e.length>=1){let t=0,i=0,s=0;t=-e[0]*Math.PI/180,e.length>=3&&(i=e[1],s=e[2]),b.identity().translate(-i,-s),w.identity().rotate(t),T.multiplyMatrices(w,b),b.identity().translate(i,s),n.multiplyMatrices(b,T)}break;case\\\\\\\"scale\\\\\\\":if(e.length>=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.scale(t,i)}break;case\\\\\\\"skewX\\\\\\\":1===e.length&&n.set(1,Math.tan(e[0]*Math.PI/180),0,0,1,0,0,0,1);break;case\\\\\\\"skewY\\\\\\\":1===e.length&&n.set(1,0,0,Math.tan(e[0]*Math.PI/180),1,0,0,0,1);break;case\\\\\\\"matrix\\\\\\\":6===e.length&&n.set(e[0],e[2],e[4],e[1],e[3],e[5],0,0,1)}}e.premultiply(n)}}return e}(t);y.length>0&&e.premultiply(y[y.length-1]);return E.copy(e),y.push(e),e}(e);let c=!0,m=null;switch(e.nodeName){case\\\\\\\"svg\\\\\\\":break;case\\\\\\\"style\\\\\\\":!function(t){if(!t.sheet||!t.sheet.cssRules||!t.sheet.cssRules.length)return;for(let e=0;et.trim()));for(let t=0;t0&&(s.copy(l),e.currentPath.currentPoint.copy(s),c=!0);break;default:console.warn(i)}h=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"x\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||t.getAttribute(\\\\\\\"ry\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new p.a;l.moveTo(e+i,n),l.lineTo(e+r-i,n),(0!==i||0!==s)&&l.bezierCurveTo(e+r-i*a,n,e+r,n+s*a,e+r,n+s);l.lineTo(e+r,n+o-s),(0!==i||0!==s)&&l.bezierCurveTo(e+r,n+o-s*a,e+r-i*a,n+o,e+r-i,n+o);l.lineTo(e+i,n+o),(0!==i||0!==s)&&l.bezierCurveTo(e+i*a,n+o,e,n+o-s*a,e,n+o-s);l.lineTo(e,n+s),(0!==i||0!==s)&&l.bezierCurveTo(e,n+s*a,e+i*a,n,e+i,n);return l}(e);break;case\\\\\\\"polygon\\\\\\\":s=r(e,s),m=function(t){function e(t,e,n){const r=d(e),o=d(n);0===s?i.moveTo(r,o):i.lineTo(r,o),s++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let s=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!0,i}(e);break;case\\\\\\\"polyline\\\\\\\":s=r(e,s),m=function(t){function e(t,e,n){const r=d(e),o=d(n);0===s?i.moveTo(r,o):i.lineTo(r,o),s++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let s=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),s=new u.a;s.absarc(e,n,i,0,2*Math.PI);const r=new p.a;return r.subPaths.push(s),r}(e);break;case\\\\\\\"ellipse\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=new u.a;r.absellipse(e,n,i,s,0,2*Math.PI);const o=new p.a;return o.subPaths.push(r),o}(e);break;case\\\\\\\"line\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),r=new p.a;return r.moveTo(e,n),r.lineTo(i,s),r.currentPath.autoClose=!1,r}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":s=r(e,s);const l=e.href.baseVal.substring(1),h=e.viewportElement.getElementById(l);h?t(h,s):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==s.fill&&\\\\\\\"none\\\\\\\"!==s.fill&&m.color.setStyle(s.fill),function(t,e){function n(t){M.set(t.x,t.y,1).applyMatrix3(e),t.set(M.x,M.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),s=t.subPaths;for(let t=0,r=s.length;t0?E.copy(y[y.length-1]):E.identity())}(S.documentElement,{fill:\\\\\\\"#000\\\\\\\",fillOpacity:1,strokeOpacity:1,strokeWidth:1,strokeLineJoin:\\\\\\\"miter\\\\\\\",strokeLineCap:\\\\\\\"butt\\\\\\\",strokeMiterLimit:4});return{paths:g,xml:S.documentElement}}static createShapes(t){const e=999999999,n=0,s=1,o=2,a=3,l=4,c=5,h=6,p={loc:n,t:0};function m(t,e,i,s){const r=t.x,a=e.x,l=i.x,c=s.x,h=t.y,u=e.y,d=i.y,_=s.y,m=(c-l)*(h-d)-(_-d)*(r-l),g=(_-d)*(a-r)-(c-l)*(u-h),v=m/g,y=((a-r)*(h-d)-(u-h)*(r-l))/g;if(0===g&&0!==m||v<=0||v>=1||y<0||y>1)return null;if(0===m&&0===g){for(let l=0;l<2;l++){if(f(0===l?i:s,t,e),p.loc==n){const t=0===l?i:s;return{x:t.x,y:t.y,t:p.t}}if(p.loc==o){return{x:+(r+p.t*(a-r)).toPrecision(10),y:+(h+p.t*(u-h)).toPrecision(10),t:p.t}}}return null}for(let r=0;r<2;r++)if(f(0===r?i:s,t,e),p.loc==n){const t=0===r?i:s;return{x:t.x,y:t.y,t:p.t}}return{x:+(r+v*(a-r)).toPrecision(10),y:+(h+v*(u-h)).toPrecision(10),t:v}}function f(t,e,i){const r=i.x-e.x,u=i.y-e.y,d=t.x-e.x,_=t.y-e.y,m=r*_-d*u;if(t.x===e.x&&t.y===e.y)return p.loc=n,void(p.t=0);if(t.x===i.x&&t.y===i.y)return p.loc=s,void(p.t=1);if(m<-Number.EPSILON)return void(p.loc=a);if(m>Number.EPSILON)return void(p.loc=l);if(r*d<0||u*_<0)return void(p.loc=c);if(Math.sqrt(r*r+u*u){if(e.boundingBox.containsPoint(s)){(function(t,e){const n=[],s=[];for(let 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null!=this._nodes_by_id.get(t.graphNodeId())}addNode(t){this._nodes_by_id.set(t.graphNodeId(),t),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(t.graphNodeId(),t)}removeNode(t){this._nodes_by_id.delete(t.graphNodeId()),this._successors.delete(t.graphNodeId()),this._predecessors.delete(t.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(t.graphNodeId())}nodesCount(){return this._nodesCount}connect(t,e,n=!0){const i=t.graphNodeId(),s=e.graphNodeId();if(this.hasNode(t)&&this.hasNode(e)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let e=!1;return n&&(e=this._hasPredecessor(i,s)),!e&&(this._createConnection(i,s),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${s}`),!1}disconnect(t,e){this._remove_connection(t.graphNodeId(),e.graphNodeId()),t.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(t){const e=this.predecessors(t);for(let n of e)this.disconnect(n,t)}disconnectSuccessors(t){const e=this.successors(t);for(let n of e)this.disconnect(t,n)}predecessorIds(t){const e=this._predecessors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}predecessors(t){const e=this.predecessorIds(t.graphNodeId());return this.nodesFromIds(e)}successorIds(t){const e=this._successors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}successors(t){const e=this.successorIds(t.graphNodeId())||[];return this.nodesFromIds(e)}allPredecessorIds(t){return this.allNextIds(t,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(t){return this.allNextIds(t,\\\\\\\"successorIds\\\\\\\")}allPredecessors(t){const e=this.allPredecessorIds(t);return this.nodesFromIds(e)}allSuccessors(t){const 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c{constructor(t){this._node=t,this._cooks_count=0,this._total_cook_time=0,this._total_inputs_time=0,this._total_params_time=0}update_cook_data(t){this._cooks_count+=1,this._total_cook_time+=t.cookTime,this._total_inputs_time+=t.inputsTime,this._total_params_time+=t.paramsTime}total_time(){return this._total_cook_time+this._total_inputs_time+this._total_params_time}total_cook_time(){return this._total_cook_time}cook_time_per_iteration(){return this._cooks_count>0?this._total_cook_time/this._cooks_count:0}total_inputs_time(){return this._total_inputs_time}inputs_time_per_iteration(){return this._cooks_count>0?this._total_inputs_time/this._cooks_count:0}total_params_time2(){return this._total_params_time}params_time_per_iteration2(){return this._cooks_count>0?this._total_params_time/this._cooks_count:0}cooks_count(){return 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NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( 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softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t// y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim := ( 4 , 2 )\\\\n\\\\t\\\\t// # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",k=\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\";const U={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:z,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:k,transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\n\\\\t// modulation\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshnormal_vert:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",meshnormal_frag:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\nuniform float opacity;\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n#endif\\\\n}\\\\n\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};var G=n(11);const V={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new G.a},uv2Transform:{value:new G.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}}},H={basic:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.fog]),vertexShader:U.meshbasic_vert,fragmentShader:U.meshbasic_frag},lambert:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshlambert_vert,fragmentShader:U.meshlambert_frag},phong:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.fog,V.lights,{emissive:{value:new D.a(0)},specular:{value:new 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Tt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new at({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#define 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s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Nt(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Ct(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?ct:0,ct,ct),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=pt[w.U]:t.value=pt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Nt()):null==this._equirectShader&&(this._equirectShader=Ct());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new B.a(mt[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),St(e,0,0,3*ct,2*ct),n.setRenderTarget(e),n.render(s,_t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;edt&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,_t)}}function At(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function Mt(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=ht[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Q(3*ct,3*ct,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function St(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function Ct(){const t=new d.a(1,1);return new at({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return new at({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Pt(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===w.D||r===w.E,a=r===w.o||r===w.p;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const s=n.morphAttributes;for(const n in s){const i=s[n];for(let n=0,s=i.length;ne.maxTextureSize&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const p=new Float32Array(h*u*4*i),_=new zt(p,h,u,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const s=e*n;let r=Jt[s];if(void 0===r&&(r=new Float32Array(s),Jt[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function ne(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function on(t){return t.replace(rn,an)}function an(t,e){const n=U[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return on(n)}const ln=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,cn=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function hn(t){return t.replace(cn,dn).replace(ln,un)}function un(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),dn(t,e,n,i)}function dn(t,e,n,i){let s=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.skinning?\\\\\\\"#define USE_SKINNING\\\\\\\":\\\\\\\"\\\\\\\",n.useVertexTexture?\\\\\\\"#define BONE_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets?\\\\\\\"#define USE_MORPHTARGETS\\\\\\\":\\\\\\\"\\\\\\\",n.morphNormals&&!1===n.flatShading?\\\\\\\"#define USE_MORPHNORMALS\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\"+n.morphTargetsCount:\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.sizeAttenuation?\\\\\\\"#define USE_SIZEATTENUATION\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoat?\\\\\\\"#define USE_CLEARCOAT\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",n.toneMapping!==w.vb?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?U.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?tn(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===w.ic?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",U.encodings_pars_fragment,n.map?Qe(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Qe(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Qe(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Qe(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?Qe(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Qe(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Ke(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")),o=on(o),o=nn(o,n),o=sn(o,n),a=on(a),a=nn(a,n),a=sn(a,n),o=hn(o),a=hn(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+f+o,x=v+g+a,b=Ye(s,s.VERTEX_SHADER,y),T=Ye(s,s.FRAGMENT_SHADER,x);if(s.attachShader(m,b),s.attachShader(m,T),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(b).trim(),n=s.getShaderInfoLog(T).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,s.LINK_STATUS)){i=!1;const e=Ze(s,b,\\\\\\\"vertex\\\\\\\"),n=Ze(s,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,s.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info 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0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||gn),s.length>1&&s.sort(e||vn),r.length>1&&r.sort(e||vn)}}}function xn(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new yn(t),e.set(n,[s])):i>=e.get(n).length?(s=new yn(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function bn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let wn=0;function Tn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function An(t,e){const n=new bn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new p.a);const r=new p.a,o=new A.a,a=new A.a;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(Tn);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_FLOAT_1,s.rectAreaLTC2=V.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_HALF_1,s.rectAreaLTC2=V.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=wn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new Mn(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Sn.prototype.isMeshDepthMaterial=!0;class Cn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Cn.prototype.isMeshDistanceMaterial=!0;function Nn(t,e,n){let i=new T.a;const s=new d.a,r=new d.a,o=new _.a,a=new Sn({depthPacking:w.Hb}),l=new Cn,c={},h=n.maxTextureSize,u={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new B.a(f,p),v=this;function y(n,i){const s=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,m,g,null)}function x(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return 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l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new Q(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),b(n,a,u.camera,c,this.type)}u.isPointLightShadow||this.type!==w.gd||y(u,a),u.needsUpdate=!1}v.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function Ln(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new _.a;let 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function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(n){e||(n?k(t.STENCIL_TEST):U(t.STENCIL_TEST))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,T=null,A=null,M=null,E=null;const S=t.getParameter(t.MAX_COMBINED_TEXTURE_IMAGE_UNITS);let C=!1,N=0;const L=t.getParameter(t.VERSION);-1!==L.indexOf(\\\\\\\"WebGL\\\\\\\")?(N=parseFloat(/^WebGL (\\\\d)/.exec(L)[1]),C=N>=1):-1!==L.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(N=parseFloat(/^OpenGL ES (\\\\d)/.exec(L)[1]),C=N>=2);let O=null,P={};const 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0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function Un(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(It.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,P=e.width,R=e.height,I=1,F=null,D=null;const B=new _.a(0,0,P,R),z=new _.a(0,0,P,R);let k=!1;const U=[],G=new T.a;let V=!1,H=!1,Y=null;const J=new A.a,Z=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ht,ut,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Mt=n;function Et(t,n){for(let i=0;i0&&function(t,e,n){if(null===Y){const t=!0===o&&!0===nt.isWebGL2;Y=new(t?Ht:Q)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget(Y),g.clear();const s=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=s,at.updateMultisampleRenderTarget(Y),at.updateRenderTargetMipmap(Y),g.setRenderTarget(i)}(s,e,n),i&&it.viewport(N.copy(i)),s.length>0&&Xt(s,e,n),r.length>0&&Xt(r,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?f[f.length-1]:null,m.pop(),u=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 0===ot.get(t).__webglFramebuffer&&at.setupRenderTarget(t);let i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(B).multiplyScalar(I).floor(),L.copy(z).multiplyScalar(I).floor(),O=k;if(it.bindFramebuffer(Mt.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Mt.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&Mt.readPixels(e,n,i,s,Tt.convert(a),Tt.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Mt.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),s=Math.floor(e.image.width*i),r=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Mt.RGB&&(o=Mt.RGB8),o===Mt.RGBA&&(o=Mt.RGBA8)),at.setTexture2D(e,0),Mt.copyTexImage2D(Mt.TEXTURE_2D,n,o,t.x,t.y,s,r,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,n.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?Mt.compressedTexSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Mt.generateMipmap(Mt.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let h;if(i.isDataTexture3D)at.setTexture3D(i,0),h=Mt.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),h=Mt.TEXTURE_2D_ARRAY}Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,i.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,i.unpackAlignment);const u=Mt.getParameter(Mt.UNPACK_ROW_LENGTH),d=Mt.getParameter(Mt.UNPACK_IMAGE_HEIGHT),p=Mt.getParameter(Mt.UNPACK_SKIP_PIXELS),_=Mt.getParameter(Mt.UNPACK_SKIP_ROWS),m=Mt.getParameter(Mt.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,f.width),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,f.height),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,t.min.x),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,t.min.y),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Mt.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,f.data)):Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f),Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,u),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,d),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,p),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,_),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,m),0===s&&i.generateMipmaps&&Mt.generateMipmap(h),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Gn={};var Vn,Hn,jn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Vn||(Vn={}));class Wn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Vn.WEBGL2))}createWebGLRenderer(t){const e=new Un(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Vn.WEBGL2:Vn.WEBGL;let i=t.getContext(n,Gn);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Vn.EXPERIMENTAL_WEBGL2:Vn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Gn)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Ht(t,e,n):new Q(t,e,n)}}class qn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class Xn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}static type(t){return this.filterType(t.type())}static filterType(t){return t.toLowerCase()}register(t,e,n){const i=t.context(),s=Xn.type(t);let r=this._node_register.get(i);r||(r=new Map,this._node_register.set(i,r));if(r.get(s))console.error(`node ${i}/${s} already registered`);else{if(r.set(s,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(s,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(s,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,s;e=Xn.filterType(e),null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(s=this._node_register_options.get(t))||void 0===s||s.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&(e=Xn.filterType(e),null!=n.get(e))}nodeOptions(t,e){var n;return e=Xn.filterType(e),null===(n=this._node_register_options.get(t))||void 0===n?void 0:n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const s=Xn.type(n),r=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(s);if(r){const n=r.only,i=r.except,s=`${t}/${e}`;return n?n.includes(s):!i||!i.includes(s)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[Xn.type(t)]=t}return n}registeredCategory(t,e){var n;return e=Xn.filterType(e),null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e)}map(){return this._node_register}}class Yn{constructor(t){this.poly=t,this._operation_register=new Map}static type(t){return this.filterType(t.type())}static filterType(t){return t.toLowerCase()}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=Yn.type(t);if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return e=Yn.filterType(e),n.get(e)}}class $n extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(jn||(jn={}));class Zn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Qn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Kn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var ti=n(86);class ei{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){li.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}deregisterUrl(t){this._blobUrlsByStoredUrl.delete(t)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(ti.a)(t)}}class ni{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],s=this._map[n];return s?i?`${s}?${i}`:s:void 0}}var ii=n(91),si=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:s,unzippedData:r}=e,o=Object.keys(s);for(let t of o){const e=r[`assets/${s[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:li.blobs.createBlobUrl(n)};li.blobs.registerBlobUrl(i)}li.setPlayerMode(!0),li.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=li;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,r)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[si.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let s=document.getElementById(t.scriptElementId);const r=[];return r.push(`import {configurePolygonjs, configureScene} from '${i}';`),r.push(`configurePolygonjs(window.${t.Poly});`),r.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),r.push(`delete window.${t.loadSceneArgs};`),s||(s=document.createElement(\\\\\\\"script\\\\\\\"),s.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),s.text=r.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(s)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const s=new n(e),r=await s.scene();i&&i(r);const o=r.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);r.play(),t.scene=r,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ii.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(ti.a)(i)}}class oi{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class ai{constructor(){this.renderersController=new Wn,this.nodesRegister=new Xn(this),this.operationsRegister=new Yn(this),this.expressionsRegister=new $n,this.modulesRegister=new Jn,this.assemblersRegister=new Zn,this.pluginsRegister=new Qn(this),this.camerasRegister=new Kn(this),this.blobs=new ei,this.assetUrls=new ni,this.selfContainedScenesLoader=new ri,this.performance=new oi,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new ai;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new qn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}log(t,...e){var n;null===(n=this._logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this._logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this._logger)||void 0===n||n.error(t,...e)}}const li=ai._instance_();class ci{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=li.performance.performanceManager(),i=n.now();e();const s=n.now()-i;console.log(`${t}: ${s}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=li.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const s=t[e];n.push(s),null==i[s]&&(i[s]=[]),i[s].push(e)}n.sort(((t,e)=>t-e));const s=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const r=[];for(let t of s){const n=i[t];for(let i of n){const n=e[i],s={duration:t,name:i,count:n,duration_per_iteration:t/n};r.push(s)}}return console.table(r),r}}class hi{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}registerParam(t){this._params_by_id.set(t.graphNodeId(),t)}deregisterParam(t){this._params_by_id.delete(t.graphNodeId())}regenerateReferringExpressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class di{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class pi{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var _i;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(_i||(_i={}));const mi=[_i.LOADED,_i.PLAY,_i.PAUSE,_i.TICK];class fi extends pi{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}}class gi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(_i.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolveMissingReferences(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const vi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=bi.findNode(t,this._path)}clone(){const t=new yi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class xi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=bi.findParam(t,this._path)}clone(){const t=new xi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class bi{static split_parent_child(t){const e=t.split(bi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(bi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR).filter((t=>t.length>0)),s=i[0];let r=null;if(e[0]!==bi.SEPARATOR){switch(s){case bi.PARENT:null==n||n.add_path_element(s),r=t.parent();break;case bi.CURRENT:null==n||n.add_path_element(s),r=t;break;default:r=t.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const t=i.slice(1).join(bi.SEPARATOR);r=this.findNode(r,t,n)}return r}{const i=e.substr(1);r=this.findNode(t.root(),i,n)}return r}static findParam(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(bi.SEPARATOR),s=this.findNode(t,e,n);if(null!=s){const t=i[i.length-1],e=s.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let s=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(bi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)r[l]||a.push(t),l++;return`${s}${a.join(bi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),s=Math.min(n.length,i.length);let r=null;for(let t=0;t{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolveMissingDependencies()}_is_reference_resolvable(t){const e=t.absolutePath();if(e){if(this.scene.node(e))return!0;{const t=bi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}checkForMissingReferences(t){this._checkForMissingReferencesForNode(t);for(let e of t.params.all)this._checkForMissingReferencesForParam(e)}_checkForMissingReferencesForNode(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matchesPath(t.path())&&(s=!0,e.resolveMissingDependencies());s&&this.references.delete(e)}))}_checkForMissingReferencesForParam(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matchesPath(t.path())&&(s=!0,e.resolveMissingDependencies());s&&this.references.delete(e)}))}}class Ai{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=li.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Mi{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ai(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Si{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Ci{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ni extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Li extends Ci{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Oi extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Pi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Li(this.node)}hasDisplay(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ni(this.node)}hasBypass(){return!0}}}function Fi(t){return class extends t{constructor(){super(...arguments),this.optimize=new Oi(this.node)}hasOptimize(){return!0}}}class Di extends(Ri(Pi)){}class Bi extends(Ii(Pi)){}class zi extends(Ii(Ri(Pi))){}class ki extends(Fi(Ii(Pi))){}class Ui extends(Fi(Ii(Ri(Pi)))){}class Gi{constructor(t){this.node=t}}class Vi extends Gi{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Hi extends Gi{set(t){this._message!=t&&(t&&li.error(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class ji{constructor(t){this.node=t,this.timeDependent=new Vi(this.node),this.error=new Hi(this.node)}}class Wi{constructor(t){this.node=t,this._graph_node=new Mi(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creationCompleted()&&(this.node.scene().missingExpressionReferencesController.checkForMissingReferences(this.node),this.node.scene().expressionsController.regenerateReferringExpressions(this.node)),this.node.scene().referencesController.notifyNameUpdated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class qi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(Wi.base_name(this.node)))}firstAncestorWithContext(t){return this._parent?this._parent.context()==t?this._parent:this._parent.parentController.firstAncestorWithContext(t):null}path(t){const e=bi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==bi.CURRENT||t==bi.CURRENT_WITH_SLASH)return this.node;if(t==bi.PARENT||t==bi.PARENT_WITH_SLASH)return this.node.parent();const e=bi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return bi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const Xi=/[, ]/,Yi=/\\\\d+$/,$i=/^0+/,Ji=/,| /,Zi=/^-?\\\\d+\\\\.?\\\\d*$/;var Qi,Ki,ts,es,ns,is;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Qi||(Qi={}));class ss{static isBoolean(t){return t==Qi.TRUE||t==Qi.FALSE}static toBoolean(t){return t==Qi.TRUE}static isNumber(t){return Zi.test(t)}static tailDigits(t){const e=t.match(Yi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Yi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const s=n.match($i);s&&(i=s[0]);const r=parseInt(n);0==r&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${r+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)ss.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(Xi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let s=0;return n.forEach((t=>{i[s]=t,s++})),i}static indices(t){const e=t.split(Ji);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class rs{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Ki||(Ki={}));class os{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class as{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return os.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=1:e;if(m.isString(e)){if(n){if(n instanceof yi)return n.set_path(e);if(n instanceof xi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new ls(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t{n[i]=t[e]}));for(let i=0;i=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class us{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}setCreationCompleted(){this._creation_completed||(this._creation_completed=!0)}creationCompleted(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(ts||(ts={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(es||(es={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(ns||(ns={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(is||(is={}));const ds=[is.ORTHOGRAPHIC,is.PERSPECTIVE];var ps,_s;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ps||(ps={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.PLAYER=\\\\\\\"playerControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(_s||(_s={}));const ms=[_s.DEVICE_ORIENTATION,_s.MAP,_s.ORBIT,_s.FIRST_PERSON,_s.MOBILE_JOYSTICK];class fs{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var gs=n(69);class vs extends K.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}vs.prototype.isScene=!0;var ys=n(49),xs=n(52),bs=n(42),ws=n(55),Ts=n(62),As=n(24),Ms=n(35);const Es=new p.a,Ss=new p.a;class Cs extends K.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t0){let n,i;for(n=1,i=e.length;n0){Es.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Es);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Es.setFromMatrixPosition(t.matrixWorld),Ss.setFromMatrixPosition(this.matrixWorld);const n=Es.distanceTo(Ss)/t.zoom;let i,s;for(e[0].object.visible=!0,i=1,s=e.length;i=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;in?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,s){return(t-e)/(n-e)*(s-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*rr}static radians_to_degrees(t){return t/rr}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),s=this.deg2rad(e.lat-t.lat),r=this.deg2rad(e.lng-t.lng),o=Math.sin(s/2)*Math.sin(s/2)+Math.cos(n)*Math.cos(i)*Math.sin(r/2)*Math.sin(r/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}or.Easing=sr,or.fract=t=>t-Math.floor(t),or._vec={x:0,y:136574},or._triangle_mid=new p.a,or._triangle_mid_to_corner=new p.a;class ar{constructor(t,e){this._coreGeometry=t,this._index=e,this._geometry=this._coreGeometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Ks(this._coreGeometry,e[n+0]),new Ks(this._coreGeometry,e[n+1]),new Ks(this._coreGeometry,e[n+2])]}positions(){return this._positions=this._positions||this._getPositions()}_getPositions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new tr.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._getDeltas()}_getDeltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}randomPosition(t){let e=[or.randFloat(t),or.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attribValueAtPosition(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),s=this._geometry.attributes[t].itemSize,r=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(s){case 1:a=0;for(let t of r)a+=t*i[l],l++;o=a;break;default:for(let t of r){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolatedValue(t,e,n,i){const s=[e.a,e.b,e.c],r=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=s.map((t=>new p.a(r[3*t+0],r[3*t+1],r[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=s.map((t=>l[t]));break;case 2:c=s.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=s.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const h=s.map(((t,e)=>n.distanceTo(o[e]))),u=f.sum([h[0]*h[1],h[0]*h[2],h[1]*h[2]]),_=[h[1]*h[2]/u,h[0]*h[2]/u,h[0]*h[1]/u];let m;switch(a){case 1:m=f.sum(s.map(((t,e)=>_[e]*c[e])));break;default:var g=s.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class lr{from_points(t){t=this._filter_points(t);const e=new S.a,n=new fr(e),i=t[0];if(null!=i){const s=i.geometry(),r=i.coreGeometry(),o={};for(let e=0;et.indexedAttribValue(i)))),r={};s.forEach(((t,e)=>r[t]=e)),n.userDataAttribs()[i]=s;const o=[];for(let e of t){const t=r[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,s=new Array(t.length*n);switch(n){case 1:for(let e=0;enew fr(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},s=[];for(let i of e){const e=i.points();for(let i of e){s.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const r=Object.keys(n);for(let e of s){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=r}const s=ur(t),r=new fr(s);return Object.keys(i).forEach((t=>{const e=i[t];r.setIndexedAttributeValues(t,e)})),s&&delete s.userData.mergedUserData,s}}const mr=\\\\\\\"indexed_attrib_values\\\\\\\";class fr{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return fr.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){return this._geometry.userData[mr]=this._geometry.userData[mr]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=Xs.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=Xs.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=Xs.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?zs.STRING:zs.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return fr.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=ss.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)ss.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=Xs.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let s=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew ar(this,t)))}}var gr;fr._mesh_builder=new class extends lr{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],s=i.length;let r,o,a;for(let t=0;t{};class yr{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,s,r,o,a)=>{n(e,i,s,r,o,a,t)})}t.onBeforeRender=vr}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,s=n.customMaterials[i];s&&(t[i]=s,s.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e]=r.uniforms[e]||n)}}}const xr=\\\\\\\"name\\\\\\\";class br extends Ys{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new fr(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=ss.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),s=i[0];if(\\\\\\\"@\\\\\\\"==s[0]){const t=s.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,s=t.userData;s.attributes=s.attributes||{},s.attributes[e]=i}addAttribute(t,e){br.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=Xs.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==xr)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==xr?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return br.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return br.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(xr)}humanType(){return js.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?zs.STRING:zs.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:Xs.attribSizeFromValue(e)}clone(){return br.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return br.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const s=n.get(e),r=e;if(r.geometry){const t=s.geometry;r.geometry=fr.clone(t);const e=r.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(r.material){r.material=s.material,yr.applyCustomMaterials(e,r.material);const t=r.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(s.userData));const o=s;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=s,h=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=h.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new fr(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(t=!1){return this._content.boundingBox(t)}center(){return this._content.center()}size(){return this._content.size()}}};class Tr{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=wr[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const Ar=li.performance.performanceManager();class Mr{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=Ar.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=Ar.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=Ar.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=Ar.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=Ar.now())}recordCookEnd(){this.active()&&(this._data.cookTime=Ar.now()-this._cook_start,this._cooksCount+=1)}}class Er{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new Mr(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),li.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?li.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(li.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let s;for(let r=0;rt.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Cr,Nr;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Cr||(Cr={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Nr||(Nr={}));const Lr=\\\\\\\"dependentOnFoundNode\\\\\\\",Or=\\\\\\\"visibleIf\\\\\\\";var Pr,Rr;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Pr||(Pr={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Rr||(Rr={}));class Ir{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Lr in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:tObject.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Mi(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Nr.VISIBLE_UPDATED)}}}class Fr{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Dr{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Br{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class zr{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&li.warn(this.param.path(),this._message),this.param.emitController.emit(Nr.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class kr{constructor(t){this.param=t,this.timeDependent=new Br(this.param),this.error=new zr(this.param)}}class Ur extends Mi{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.updateFromMethodDependencyNameChange()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const s=m.isNumber(e),r=new Ur(t,e,i);if(n)r.set_resolved_graph_node(n);else if(!s){const t=e;r.set_unresolved_path(t)}return r}}const Gr=[];class Vr extends Mi{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Ir(this),this._emit_controller=new Fr(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Ir(this)}get emitController(){return this._emit_controller=this._emit_controller||new Fr(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Dr(this)}get states(){return this._states=this._states||new kr(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof Ur&&t.dispose();this.scene().missingExpressionReferencesController.deregisterParam(this),null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Cr.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Nr.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=bi.relativePath(t,this.node);return e.length>0?`${e}${bi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return Gr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Hr=n(94),jr=n.n(Hr);jr.a.addUnaryOp(\\\\\\\"@\\\\\\\");jr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Wr{constructor(){}parse_expression(t){try{this.reset(),this.node=jr()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Wr.string_value_elements(t),n=[];for(let t=0;t0){const e=\\\\\\\"const coreGeometry = entities[0].coreGeometry();\\\\\\\";t.push(e)}for(let e of this._attributeNames)t.push(Xr.assignItemSizeLine(e)),t.push(Xr.assignArrayLine(e));return t.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attributePresenceCheckLine(){if(this._attributeNames){const t=[];for(let e of this._attributeNames){const n=Xr._varAttribute(e);t.push(n)}if(t.length>0)return t.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attributeNames=this._attributeNames||new Set,this._attributeNames.add(t)}static assignAttributeLine(t){return`const ${this._varAttribute(t)} = entities[0].geometry().attributes['${t}']`}static assignItemSizeLine(t){const e=this._varAttribute(t);return`const ${this._varAttribSize(t)} = ${e}.itemSize`}static assignArrayLine(t){const e=this._varAttribute(t);return`const ${this._varArray(t)} = ${`coreGeometry.isAttribIndexed('${t}')`} ? ${`entities.map(e=>e.indexedAttribValue('${t}'))`} : ${`${e}.array`}`}static _varAttribute(t){return`attrib_${t}`}static _varAttribSize(t){return`attrib_size_${t}`}static _varArray(t){return`array_${t}`}varAttributeSize(t){return Xr._varAttribSize(t)}varArray(t){return Xr._varArray(t)}}const Yr={math_random:\\\\\\\"random\\\\\\\"},$r=Object.keys(sr),Jr={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`})),Object.keys(Yr).forEach((t=>{const e=Yr[t];Jr[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Jr[t]=`Core.Math.${t}`})),$r.forEach((t=>{Jr[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Jr[t]=`Core.String.${t}`}));const Zr={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},Qr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{Qr[t]=`Math.${t}`}));const Kr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class to extends qr{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new Xr,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller=new Xr,t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{const t=this._functionBody();this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}_functionBody(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// assign_attributes_lines\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assignAttributesLines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// check if attributes are present\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attributePresenceCheckLine()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// assign_arrays_lines\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assignArraysLines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}evalAllowed(){return null!=this.function}evalFunction(){if(this.function){this.clear_error();const t={Math:or,String:ss};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=Zr[n];if(i)return i(e);const s=`${e.join(\\\\\\\", \\\\\\\")}`,r=Jr[n];if(r)return`${r}(${s})`;const o=li.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],r=`return ${e[0]}`;let o,a=[];try{o=new Function(r),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${s}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;li.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,s=t.object,r=t.property;e=s.name,n=r.name;break}}if(e){if(e=Xs.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.varAttributeSize(e),i=this._attribute_requirements_controller.varArray(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Kr[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=Qr[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=li.expressionsRegister,s=i.getMethod(t);if(!s){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void li.warn(e)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const t=r.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,e)}}}class eo extends qr{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class no{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class io{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Wr,this._functionGenerator=new to(this.param),this.dependencies_controller=new no(this.param)}parseExpression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Wr,this.reset(),this.param.type()==Cr.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this._functionGenerator.parse_tree(this.parsed_tree),null==this._functionGenerator.error_message()&&(this.dependencies_controller.update(this._functionGenerator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async computeFunction(){if(!this._computeAllowed())return new Promise(((t,e)=>{t(null)}));try{return await this._functionGenerator.evalFunction()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this._functionGenerator.reset()}is_errored(){return this._functionGenerator.is_errored()}error_message(){return this._functionGenerator.error_message()}_computeAllowed(){return this._functionGenerator.evalAllowed()}updateFromMethodDependencyNameChange(){this.expression_string_generator=this.expression_string_generator||new eo(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class so{constructor(t){this.param=t}dispose(){this.param.scene().expressionsController.deregisterParam(this.param),this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregisterParam(this.param),this.param.scene().expressionsController.deregisterParam(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new io(this.param),this._manager.parseExpression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}updateFromMethodDependencyNameChange(){this._manager&&this.active()&&this._manager.updateFromMethodDependencyNameChange()}async computeExpression(){if(this._manager&&this.active()){return await this._manager.computeFunction()}}async computeExpressionForEntities(t,e){var n,i;this.set_entities(t,e),await this.computeExpression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}computeExpressionForPoints(t,e){return this.computeExpressionForEntities(t,e)}computeExpressionForObjects(t,e){return this.computeExpressionForEntities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class ro extends Vr{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Nr.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new so(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Nr.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.computeExpression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Nr.VALUE_UPDATED),this.removeDirtyState()}}class oo extends ro{static type(){return Cr.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&ss.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(ss.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=oo.convert(t);return e?this.options.ensureInRange(e):e}}class ao extends Vr{constructor(){super(...arguments),this._components_contructor=oo}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;ee))}}processRawInput(){const t=this.scene().cooker;t.block();const e=this.components;for(let t of e)t.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let t=0;t linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(lo||(lo={}));lo.NONE,lo.GAMMA_TO_LINEAR,lo.LINEAR_TO_GAMMA,lo.SRGB_TO_LINEAR,lo.LINEAR_TO_SRGB;class co{static set_hsv(t,e,n,i){t=Object(On.f)(t,1),e=Object(On.d)(e,0,1),n=Object(On.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const ho=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class uo extends ro{static type(){return Cr.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&ss.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(ss.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=uo.convert(t);return e?this.options.ensureInRange(e):e}}class po{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(bi.SEPARATOR);const n=e[0];return n&&(bi.NON_LETTER_PREFIXES.includes(n)||(e=`${bi.SEPARATOR}${e}`)),e}}class _o extends Vr{constructor(){super(...arguments),this.decomposed_path=new po}_handleReferences(t,e){this.scene().referencesController.setNamedNodesFromParam(this),t?this.scene().referencesController.setReferenceFromParam(this,t):this.scene().missingExpressionReferencesController.register(this,e)}}var mo;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(mo||(mo={}));class fo extends _o{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Cr.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Nr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,s=this.options.paramSelectionOptions()?mo.PARAM:mo.NODE;this.scene().referencesController.resetReferenceFromParam(this),this.decomposed_path.reset(),i&&(s==mo.PARAM?n=bi.findParam(this.node,t,this.decomposed_path):e=bi.findNode(this.node,t,this.decomposed_path));const r=s==mo.PARAM?this._found_param:this._found_node,o=s==mo.PARAM?n:e;if(this._handleReferences(e,t),(null==r?void 0:r.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),s==mo.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notifyPathRebuildRequired(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notifyTargetParamOwnerParamsUpdated(t){this.setDirty()}}var go,vo=n(34),yo=n(70);class xo{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}position(){return this._position}value(){return this._value}copy(t){this._position=t.position(),this._value=t.value()}clone(){const t=new xo;return t.copy(this),t}isEqual(t){return this._position==t.position()&&this._value==t.value()}isEqualJSON(t){return this._position==t.position&&this._value==t.value}fromJSON(t){this._position=t.position,this._value=t.value}static areEqualJSON(t,e){return t.position==e.position&&t.value==e.value}static fromJSON(t){return new xo(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(go||(go={}));class bo{constructor(t=go.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(On.h)()}uuid(){return this._uuid}interpolation(){return this._interpolation}points(){return this._points}static fromJSON(t){const e=[];for(let n of t.points)e.push(xo.fromJSON(n));return new bo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new bo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation();let e=0;for(let n of t.points()){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation())return!1;const e=t.points();if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.isEqual(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.isEqualJSON(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!xo.areEqualJSON(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.fromJSON(n):this._points.push(xo.fromJSON(n)),e+=1}}}const wo=1024,To=255;class Ao extends Vr{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new vo.a(this._texture_data,wo,1,w.ic)}static type(){return Cr.RAMP}defaultValueSerialized(){return this._default_value instanceof bo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof bo?t.clone():bo.fromJSON(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof bo?this._raw_input.toJSON():bo.fromJSON(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof bo?e instanceof bo?t.is_equal(e):t.is_equal_json(e):e instanceof bo?e.is_equal_json(t):bo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof bo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof bo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=bo.fromJSON(this._raw_input),this._resetRampInterpolant(),this._updateRampTexture(),this.options.executeCallback(),this.emitController.emit(Nr.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_resetRampInterpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_updateRampTexture(){this._updateRampTextureData(),this.rampTexture().needsUpdate=!0}_updateRampTextureData(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/wo,n=this.valueAtPosition(e),n<=0?(this._texture_data[t+0]=(Object(On.d)(n,-1,0)+1)*To,this._texture_data[t+1]=0,this._texture_data[t+2]=0):n<=1?(this._texture_data[t+0]=To,this._texture_data[t+1]=Object(On.d)(n,0,1)*To,this._texture_data[t+2]=0):(this._texture_data[t+0]=To,this._texture_data[t+1]=To,this._texture_data[t+2]=(Object(On.d)(n,1,2)-1)*To)}static createInterpolant(t,e){const n=new Float32Array(1);return new yo.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._createInterpolant()}_createInterpolant(){const t=this.value.points(),e=f.sortBy(t,(t=>t.position())),n=new Float32Array(e.length),i=new Float32Array(e.length);let s=0;for(let t of e)n[s]=t.position(),i[s]=t.value(),s++;return Ao.createInterpolant(n,i)}valueAtPosition(t){return this.interpolant().evaluate(t)[0]}}Ao.DEFAULT_VALUE=new bo(go.LINEAR,[new xo(0,0),new xo(1,1)]),Ao.DEFAULT_VALUE_JSON=Ao.DEFAULT_VALUE.toJSON();class Mo extends Vr{static type(){return Cr.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new so(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Nr.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Nr.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Nr.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.computeExpression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Nr.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Wr.string_value_elements(t)}}const Eo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const So=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Co=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const No={[Cr.BOOLEAN]:class extends ro{static type(){return Cr.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(ss.isBoolean(t))return ss.toBoolean(t);if(ss.isNumber(t)){return parseFloat(t)>=1}}return null}},[Cr.BUTTON]:class extends Vr{static type(){return Cr.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Cr.COLOR]:class extends ao{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Cr.COLOR}componentNames(){return ho}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=lo.NONE){switch(t){case lo.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case lo.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case lo.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case lo.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}os.unreachable(t)}this._value_serialized_dirty=!0}},[Cr.FLOAT]:oo,[Cr.FOLDER]:class extends Vr{static type(){return Cr.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Cr.INTEGER]:uo,[Cr.OPERATOR_PATH]:fo,[Cr.PARAM_PATH]:class extends _o{static type(){return Cr.PARAM_PATH}initialize_param(){this._value=new xi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Nr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.resetReferenceFromParam(this),this.decomposed_path.reset(),n&&(e=bi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),s=e;if(this._handleReferences(e,t),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notifyPathRebuildRequired(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notifyTargetParamOwnerParamsUpdated(t){this.setDirty()}},[Cr.NODE_PATH]:class extends _o{static type(){return Cr.NODE_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Nr.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.resetReferenceFromParam(this),this.decomposed_path.reset(),n&&(e=bi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),s=e;if(this._handleReferences(e,t),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notifyPathRebuildRequired(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notifyTargetParamOwnerParamsUpdated(t){this.setDirty()}},[Cr.RAMP]:Ao,[Cr.STRING]:Mo,[Cr.VECTOR2]:class extends ao{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Cr.VECTOR2}componentNames(){return Eo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Cr.VECTOR3]:class extends ao{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Cr.VECTOR3}componentNames(){return So}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Cr.VECTOR4]:class extends ao{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Cr.VECTOR4}componentNames(){return Co}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class Lo{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Cr.STRING:return this._handle_string_param(t);case Cr.OPERATOR_PATH:return this._handle_operator_path_param(t);case Cr.NODE_PATH:return this._handle_node_path_param(t);case Cr.PARAM_PATH:return this._handle_param_path_param(t);case Cr.FLOAT:case Cr.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class Oo{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new Lo}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Mi(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notifyParamsUpdated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let s;this.node.params_init_value_overrides&&(s=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,s)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):li.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Cr.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Cr.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Cr.FLOAT)}get_operator_path(t){return this.param_with_type(t,Cr.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Cr.BOOLEAN)}float(t){return this.value_with_type(t,Cr.FLOAT)}integer(t){return this.value_with_type(t,Cr.INTEGER)}string(t){return this.value_with_type(t,Cr.STRING)}vector2(t){return this.value_with_type(t,Cr.VECTOR2)}vector3(t){return this.value_with_type(t,Cr.VECTOR3)}color(t){return this.value_with_type(t,Cr.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(li.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},s){const r=i.spare||!1;!1!==this._param_create_mode||r||li.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&li.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=No[t];if(null!=o){const a=this._params_by_name[e];a&&(r?a.type()!=t&&this._deleteParam(a.name()):li.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==s)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=s.raw_input)l.set(s.raw_input);else if(null!=s.simple_data)l.set(s.simple_data);else if(null!=s.complex_data){const t=s.complex_data.raw_input;t?l.set(t):l.set(n);const e=s.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void li.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?li.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class Po{constructor(){}}class Ro{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=Ro._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}Ro._next_id=0;class Io{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return os.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.setMinCount(0),this.setMaxCount(t.length),this._initGraphNodeInputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}_initGraphNodeInputs(){for(let t=0;t0)for(let e=0;e0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let t=0;tt.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const r=this._graph_node_inputs[i];if(null==r){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&s==l||(null!=o&&this._depends_on_inputs&&r.removeGraphInput(o),null!=e?r.addGraphInput(e)?(this._depends_on_inputs||r.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new Ro(e,this.node,s,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),r.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Io(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Do{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const s=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&s.push(n)}return s}return[]}}class Bo{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class zo{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class ko{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Uo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Uo||(Uo={}));const Go=[Uo.BOOL,Uo.INT,Uo.FLOAT,Uo.VEC2,Uo.VEC3,Uo.VEC4],Vo={[Uo.BOOL]:Cr.BOOLEAN,[Uo.INT]:Cr.INTEGER,[Uo.FLOAT]:Cr.FLOAT,[Uo.VEC2]:Cr.VECTOR2,[Uo.VEC3]:Cr.VECTOR3,[Uo.VEC4]:Cr.VECTOR4,[Uo.SAMPLER_2D]:Cr.RAMP,[Uo.SSS_MODEL]:Cr.STRING},Ho={[Cr.BOOLEAN]:Uo.BOOL,[Cr.COLOR]:Uo.VEC3,[Cr.INTEGER]:Uo.INT,[Cr.FLOAT]:Uo.FLOAT,[Cr.FOLDER]:void 0,[Cr.VECTOR2]:Uo.VEC2,[Cr.VECTOR3]:Uo.VEC3,[Cr.VECTOR4]:Uo.VEC4,[Cr.BUTTON]:void 0,[Cr.OPERATOR_PATH]:void 0,[Cr.PARAM_PATH]:void 0,[Cr.NODE_PATH]:void 0,[Cr.RAMP]:void 0,[Cr.STRING]:void 0},jo={[Uo.BOOL]:!1,[Uo.INT]:0,[Uo.FLOAT]:0,[Uo.VEC2]:[0,0],[Uo.VEC3]:[0,0,0],[Uo.VEC4]:[0,0,0,0],[Uo.SAMPLER_2D]:Ao.DEFAULT_VALUE_JSON,[Uo.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Wo={[Uo.BOOL]:1,[Uo.INT]:1,[Uo.FLOAT]:1,[Uo.VEC2]:2,[Uo.VEC3]:3,[Uo.VEC4]:4,[Uo.SAMPLER_2D]:1,[Uo.SSS_MODEL]:1};class qo extends ko{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||jo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Vo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Xo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(Xo||(Xo={}));const Yo=[Xo.BOOL,Xo.INT,Xo.FLOAT,Xo.VEC2,Xo.VEC3,Xo.VEC4],$o={[Xo.BOOL]:Cr.BOOLEAN,[Xo.INT]:Cr.INTEGER,[Xo.FLOAT]:Cr.FLOAT,[Xo.VEC2]:Cr.VECTOR2,[Xo.VEC3]:Cr.VECTOR3,[Xo.VEC4]:Cr.VECTOR4},Jo={[Cr.BOOLEAN]:Xo.BOOL,[Cr.COLOR]:Xo.VEC3,[Cr.INTEGER]:Xo.INT,[Cr.FLOAT]:Xo.FLOAT,[Cr.FOLDER]:void 0,[Cr.VECTOR2]:Xo.VEC2,[Cr.VECTOR3]:Xo.VEC3,[Cr.VECTOR4]:Xo.VEC4,[Cr.BUTTON]:void 0,[Cr.OPERATOR_PATH]:void 0,[Cr.PARAM_PATH]:void 0,[Cr.NODE_PATH]:void 0,[Cr.RAMP]:void 0,[Cr.STRING]:void 0},Zo={[Xo.BOOL]:!1,[Xo.INT]:0,[Xo.FLOAT]:0,[Xo.VEC2]:[0,0],[Xo.VEC3]:[0,0,0],[Xo.VEC4]:[0,0,0,0]};Xo.BOOL,Xo.INT,Xo.FLOAT,Xo.VEC2,Xo.VEC3,Xo.VEC4;class Qo extends ko{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Zo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return $o[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Ko;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}(Ko||(Ko={}));class ta extends ko{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Cr.FLOAT}are_types_matched(t,e){return e==Ko.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const ea={[ts.ANIM]:void 0,[ts.COP]:void 0,[ts.EVENT]:Ko.BASE,[ts.GL]:Uo.FLOAT,[ts.JS]:Xo.FLOAT,[ts.MANAGER]:void 0,[ts.MAT]:void 0,[ts.OBJ]:void 0,[ts.POST]:void 0,[ts.ROP]:void 0,[ts.SOP]:void 0};function na(t,e,n){switch(t){case ts.EVENT:return new ta(e,n);case ts.GL:return new qo(e,n);case ts.JS:return new Qo(e,n);default:return}}class ia{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case ts.EVENT:return;case ts.GL:return Ho;case ts.JS:return Jo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=na(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let s=t.init_value;const r=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(s=null!=o?o:null!=r?r:t.init_value,m.isArray(t.init_value))if(m.isNumber(s)){const e=new Array(t.init_value.length);e.fill(s),s=e}else m.isArray(s)&&s.length==t.init_value.length&&null!=r&&(s=t.init_value);null!=s&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(s),raw_input:b.clone(s),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class sa{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new ia(this.node,this._context)}default_connection_type(){return ea[this._context]}create_connection_point(t,e){return na(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creationCompleted()&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(ns.INPUT),n=this.node.nodesByType(ns.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;enull==t?void 0:t.type())),e=this.node.io.outputs.namedOutputConnectionPoints().map((t=>null==t?void 0:t.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=t.length)return!1;if(i.length!=e.length)return!1;for(let e=0;et.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class ra{constructor(t){this.node=t,this._connections=new Bo(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Fo(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Do(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new sa(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new zo(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class oa{constructor(){}}class aa extends Mi{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new Tr(this),this.pv=new Po,this.p=new oa,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new qi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new hs(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Si(this)}get states(){return this._states=this._states||new ji(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new us(this)}get serializer(){return this._serializer=this._serializer||new Sr(this)}get cookController(){return this._cook_controller=this._cook_controller||new Er(this)}get io(){return this._io=this._io||new ra(this)}get nameController(){return this._name_controller=this._name_controller||new Wi(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new Oo(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}firstAncestorWithContext(t){return this.parentController.firstAncestorWithContext(t)}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var s;return null===(s=this._params_controller)||void 0===s?void 0:s.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class la extends aa{static context(){return ts.MANAGER}}class ca{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class ha{static BUTTON(t,e){return new ca(Cr.BUTTON,t,e)}static BOOLEAN(t,e){return new ca(Cr.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new ca(Cr.COLOR,t,e)}static FLOAT(t,e){return new ca(Cr.FLOAT,t,e)}static FOLDER(t=null,e){return new ca(Cr.FOLDER,t,e)}static INTEGER(t,e){return new ca(Cr.INTEGER,t,e)}static RAMP(t=Ao.DEFAULT_VALUE,e){return new ca(Cr.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new ca(Cr.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new ca(Cr.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new ca(Cr.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new ca(Cr.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new ca(Cr.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new ca(Cr.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new ca(Cr.PARAM_PATH,t,e)}}class ua{}class da{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),s=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(ss.matchMask(s,t))return i;const r=this.findObjectByMaskInObject(t,i,s);if(r)return r}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let s of e.children){const e=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;ss.matchMask(r,t)&&n.push(s),this.objectsByMaskInObject(t,s,n,r)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const pa={computeOnDirty:!1,callback:t=>{ma.update(t)}};function _a(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=ha.BOOLEAN(1,pa)}}}_a(ua);class ma{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var fa;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(fa||(fa={}));const ga=[fa.NONE,fa.COLOR,fa.TEXTURE],va={computeOnDirty:!1,callback:t=>{xa.update(t)}};function ya(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=ha.INTEGER(ga.indexOf(fa.NONE),{menu:{entries:ga.map(((t,e)=>({name:t,value:e})))},...va}),this.bgColor=ha.COLOR([0,0,0],{visibleIf:{backgroundMode:ga.indexOf(fa.COLOR)},...va}),this.bgTexture=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:ga.indexOf(fa.TEXTURE)},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...va})}}}ya(ua);class xa{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==ga.indexOf(fa.NONE))t.background=null;else if(e.backgroundMode==ga.indexOf(fa.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const ba={computeOnDirty:!1,callback:t=>{Ta.update(t)}};function wa(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=ha.BOOLEAN(0,ba),this.environment=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...ba})}}}wa(ua);class Ta{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class Aa{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new Aa(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}Aa.prototype.isFog=!0;class Ma{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new Ma(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}Ma.prototype.isFogExp2=!0;const Ea={computeOnDirty:!1,callback:t=>{La.update(t)}};var Sa;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Sa||(Sa={}));const Ca=[Sa.LINEAR,Sa.EXPONENTIAL];function Na(t){return class extends t{constructor(){super(...arguments),this.useFog=ha.BOOLEAN(0,Ea),this.fogType=ha.INTEGER(Ca.indexOf(Sa.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Ca.map(((t,e)=>({name:t,value:e})))},...Ea}),this.fogColor=ha.COLOR([1,1,1],{visibleIf:{useFog:1},...Ea}),this.fogNear=ha.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ca.indexOf(Sa.LINEAR)},...Ea}),this.fogFar=ha.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ca.indexOf(Sa.LINEAR)},...Ea}),this.fogDensity=ha.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Ca.indexOf(Sa.EXPONENTIAL)},...Ea})}}}Na(ua);class La{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Ca.indexOf(Sa.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new Aa(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new Ma(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Oa={computeOnDirty:!1,callback:t=>{Ra.update(t)}};function Pa(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=ha.BOOLEAN(0,Oa),this.overrideMaterial=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,...Oa})}}}Pa(ua);class Ra{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(ts.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class Ia extends(Pa(wa(Na(ya(_a(ua)))))){}const Fa=new Ia;class Da extends la{constructor(){super(...arguments),this.paramsConfig=Fa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new ma(this),this.sceneBackgroundController=new xa(this),this.sceneEnvController=new Ta(this),this.sceneFogController=new La(this),this.sceneMaterialOverrideController=new Ra(this),this._children_controller_context=ts.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new vs;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const s=`_____${n.renderOrder}__${n.path()}`;e.push(s),t.set(s,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Ba{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Da(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();ss.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class za{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new Sr(i);e[i.graphNodeId()]=s.toJSON(t);const r=i.params.all;for(let t of r)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var ka;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(ka||(ka={}));const Ua=[ka.auxclick,ka.click,ka.contextmenu,ka.dblclick,ka.mousedown,ka.mouseenter,ka.mouseleave,ka.mousemove,ka.mouseover,ka.mouseout,ka.mouseup,ka.pointerlockchange,ka.pointerlockerror,ka.select,ka.wheel];class Ga extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ua.map((t=>`${t}`))}}class Va extends aa{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return ts.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,h.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var Ha;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(Ha||(Ha={}));const ja=[Ha.CANVAS,Ha.DOCUMENT];class Wa{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const s=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,s),i.set(n.type,{listener:s,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==Ha.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const qa={visibleIf:{active:1},callback:t=>{Ya.PARAM_CALLBACK_updateRegister(t)}};class Xa extends Va{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:ja[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class Ya extends Xa{acceptedEventTypes(){return[]}}const $a=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:t=>{Ja.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(ja.indexOf(Ha.CANVAS),{menu:{entries:ja.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=ha.BOOLEAN(0,qa),this.click=ha.BOOLEAN(0,qa),this.contextmenu=ha.BOOLEAN(0,qa),this.dblclick=ha.BOOLEAN(0,qa),this.mousedown=ha.BOOLEAN(1,qa),this.mouseenter=ha.BOOLEAN(0,qa),this.mouseleave=ha.BOOLEAN(0,qa),this.mousemove=ha.BOOLEAN(1,qa),this.mouseover=ha.BOOLEAN(0,qa),this.mouseout=ha.BOOLEAN(0,qa),this.mouseup=ha.BOOLEAN(1,qa),this.pointerlockchange=ha.BOOLEAN(0,qa),this.pointerlockerror=ha.BOOLEAN(0,qa),this.select=ha.BOOLEAN(0,qa),this.wheel=ha.BOOLEAN(0,qa),this.ctrlKey=ha.BOOLEAN(0,{...qa,separatorBefore:!0}),this.altKey=ha.BOOLEAN(0,qa),this.shiftKey=ha.BOOLEAN(0,qa),this.metaKey=ha.BOOLEAN(0,qa)}};class Ja extends Xa{constructor(){super(...arguments),this.paramsConfig=$a}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ua.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ua.map((t=>new ta(t,Ko.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Za;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Za||(Za={}));const Qa=[Za.pointerdown,Za.pointermove,Za.pointerup];class Ka extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Qa.map((t=>`${t}`))}}const tl=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:t=>{el.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(ja.indexOf(Ha.CANVAS),{menu:{entries:ja.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=ha.BOOLEAN(1,qa),this.pointermove=ha.BOOLEAN(0,qa),this.pointerup=ha.BOOLEAN(0,qa),this.ctrlKey=ha.BOOLEAN(0,{...qa,separatorBefore:!0}),this.altKey=ha.BOOLEAN(0,qa),this.shiftKey=ha.BOOLEAN(0,qa),this.metaKey=ha.BOOLEAN(0,qa)}};class el extends Xa{constructor(){super(...arguments),this.paramsConfig=tl}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Qa.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Qa.map((t=>new ta(t,Ko.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var nl,il;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(nl||(nl={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(il||(il={}));const sl=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:(t,e)=>{rl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(0,{hidden:!0}),this.sceneLoaded=ha.BOOLEAN(1,qa),this.play=ha.BOOLEAN(1,qa),this.pause=ha.BOOLEAN(1,qa),this.tick=ha.BOOLEAN(1,{separatorAfter:!0,...qa}),this.treachedTime=ha.BOOLEAN(0,{callback:t=>{rl.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=ha.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=ha.INTEGER(1,{range:[0,100]}),this.setFrame=ha.BUTTON(null,{callback:t=>{rl.PARAM_CALLBACK_setFrame(t)}})}};class rl extends Xa{constructor(){super(...arguments),this.paramsConfig=sl}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(nl.SET_FRAME,Ko.BASE,this._onSetFrame.bind(this)),new ta(_i.PLAY,Ko.BASE,this._play.bind(this)),new ta(_i.PAUSE,Ko.BASE,this._pause.bind(this))]);const t=mi.map((t=>new ta(t,Ko.BASE)));t.push(new ta(il.TIME_REACHED,Ko.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}_onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}_play(t){this.scene().play()}_pause(t){this.scene().pause()}_onFrameUpdate(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(il.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Mi(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this._onFrameUpdate.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t._onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var ol;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(ol||(ol={}));const al=[ol.keydown,ol.keypress,ol.keyup];class ll extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return al.map((t=>`${t}`))}}const cl=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:(t,e)=>{hl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(ja.indexOf(Ha.CANVAS),{menu:{entries:ja.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=ha.BOOLEAN(1,qa),this.keypress=ha.BOOLEAN(0,qa),this.keyup=ha.BOOLEAN(0,qa),this.keyCodes=ha.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",qa),this.ctrlKey=ha.BOOLEAN(0,qa),this.altKey=ha.BOOLEAN(0,qa),this.shiftKey=ha.BOOLEAN(0,qa),this.metaKey=ha.BOOLEAN(0,qa)}};class hl extends Xa{constructor(){super(...arguments),this.paramsConfig=cl}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return al.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(al.map((t=>new ta(t,Ko.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var ul;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(ul||(ul={}));const dl=[ul.resize];class pl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return dl.map((t=>`${t}`))}}const _l=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:t=>{ml.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(0,{hidden:!0}),this.resize=ha.BOOLEAN(1,qa)}};class ml extends Xa{constructor(){super(...arguments),this.paramsConfig=_l}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return dl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(dl.map((t=>new ta(t,Ko.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var fl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(fl||(fl={}));const gl=[fl.dragover];class vl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}}var yl;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(yl||(yl={}));const xl=[yl.touchstart,yl.touchmove,yl.touchend];class bl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return xl.map((t=>`${t}`))}}const wl=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:t=>{Tl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(ja.indexOf(Ha.CANVAS),{menu:{entries:ja.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=ha.BOOLEAN(1,qa),this.ctrlKey=ha.BOOLEAN(0,{...qa,separatorBefore:!0}),this.altKey=ha.BOOLEAN(0,qa),this.shiftKey=ha.BOOLEAN(0,qa),this.metaKey=ha.BOOLEAN(0,qa)}};class Tl extends Xa{constructor(){super(...arguments),this.paramsConfig=wl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(gl.map((t=>new ta(t,Ko.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const Al=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(!0,{callback:t=>{Ml.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=ha.INTEGER(ja.indexOf(Ha.CANVAS),{menu:{entries:ja.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=ha.BOOLEAN(1,qa),this.touchmove=ha.BOOLEAN(0,qa),this.touchend=ha.BOOLEAN(0,qa)}};class Ml extends Xa{constructor(){super(...arguments),this.paramsConfig=Al}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return xl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(xl.map((t=>new ta(t,Ko.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class El{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case hl.type():return this.keyboardEventsController;case Ja.type():return this.mouseEventsController;case Tl.type():return this.dragEventsController;case el.type():return this.pointerEventsController;case rl.type():return this.sceneEventsController;case Ml.type():return this.touchEventsController;case ml.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(ll)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(Ga)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(vl)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(Ka)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(fi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(pl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(bl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Sl{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}setReferenceFromParam(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),h.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}setNamedNodesFromParam(t){const e=t.decomposed_path.named_nodes();for(let n of e)h.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}resetReferenceFromParam(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){h.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)h.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Cr.OPERATOR_PATH,Cr.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,s=[];for(let t of n)this._check_param(t,i,s);for(let t of s)i.set(t.node.graphNodeId(),t.node);const r=[];return i.forEach((t=>{r.push(t)})),r}_check_param(t,e,n){if(t instanceof fo){const i=t.found_node(),s=t.found_param();return i&&e.set(i.graphNodeId(),i),void(s&&n.push(s))}}notifyNameUpdated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e){const n=f.shallowClone(e);for(let e of n)e.notifyPathRebuildRequired(t)}}notifyParamsUpdated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e){const n=f.shallowClone(e);for(let e of n)e.options.isSelectingParam()&&e.notifyTargetParamOwnerParamsUpdated(t)}}}var Cl;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Cl||(Cl={}));class Nl{constructor(t){this.scene=t,this._frame=0,this._time=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._delta=0,this._graph_node=new Mi(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(_i.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(_i.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(_i.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Cl.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Cl.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Cl.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){var n,i;if(t!=this._time){if(this._time=t,null===(n=this._onBeforeTickCallbacks)||void 0===n||n.forEach((t=>t(this._delta))),e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}this.scene.dispatchController.dispatch(this._graph_node,Cl.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),null===(i=this._onAfterTickCallbacks)||void 0===i||i.forEach((t=>t(this._delta)))}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(Nl.START_FRAME,!0)}incrementTimeIfPlaying(t){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime(t))}incrementTime(t){if(this._realtimeState){this._delta=t;const e=this._time+this._delta;this.setTime(e)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return Nl.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Ol{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Pl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,li.renderersController.setRequireWebGL2())}}class Rl{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Mi(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Il{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Fl{constructor(){this._cameras_controller=new r(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new Ti(this),this._expressions_controller=new ui,this._nodes_controller=new Ba(this),this._objects_controller=new da(this),this._references_controller=new Sl(this),this._time_controller=new Nl(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=ss.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Il}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new hi(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new El(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new di(this)}get loadingController(){return this._loading_controller=this._loading_controller||new gi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new ci}get viewersRegister(){return this._viewers_register=this._viewers_register||new Ol(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new za(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Ll(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Pl}get windowController(){return this._windowController=this._windowController||new Rl(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbacks(){return this.timeController.registeredBeforeTickCallbacks()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbacks(){return this.timeController.registeredAfterTickCallbacks()}}class Dl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let s of t)i=e[s],i&&(n[s]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let s;for(let r of i)if(s=e[r],null!=s){const e=s.options,i=s.overriden_options;if(e||i){const o=s;e&&e.spare==t?null!=o.raw_input&&(n[r]={complex_data:o}):i&&(n[r]={complex_data:o})}else{const t=s;(i||null!=t)&&(n[r]={simple_data:t})}}}return n}}const Bl=\\\\\\\"operationsComposer\\\\\\\";class zl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}zl.DEFAULT_PARAMS={},zl.INPUT_CLONED_STATE=[];class kl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,s;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:r}=kl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of r)kl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let r of this._optimized_root_node_names){const o=e[r],a=this._node.createNode(Bl);if(a){a.setName(r),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(s=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,s;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const r=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of r)(null===(s=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===s?void 0:s.active())||o++;return this.is_optimized_root_node_generic({outputs_count:r.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let s of n)if(s!=e){const n=t[s].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(s)}}return i}_create_operation_container(t,e,n,i){const s=Dl.non_spare_params_data_value(n.params),r=kl.operation_type(n),o=this._node.create_operation_container(r,i,s);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return kl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class Ul{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:s}=kl.child_names_by_optimized_state(e),r=[];for(let n of s){const i=e[n],s=i.type.toLowerCase(),o=Dl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(s,o);t&&(t.setName(n),r.push(t))}catch(e){console.error(`error importing node: cannot create with type ${s}`,e);const i=ss.camelCase(s);try{const t=this._node.createNode(i,o);t&&(t.setName(n),r.push(t))}catch(e){const a=`${s}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),r.push(t))}catch(e){const n=`failed to create node with type '${s}', '${i}' or '${a}'`;t.report.addWarning(n),li.warn(n,e)}}}}if(i.length>0){const i=new kl(this._node);if(i.process_data(t,e),this._node.childrenController.context==ts.SOP){const t=Object.keys(e);let s;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const t=r.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(s)){const t=`node '${`${this._node.path()}/${s}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of r){if(e[n.name()]){const i=Yl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else li.warn(`possible import error for node ${n.name()}`)}for(let t of r){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const Gl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class Vl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;if(null!=e){const t=this._node.io.inputs.minCount();this._node.io.inputs.setCount(t,e)}this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(li.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const s=e.comment;s&&n.setComment(s),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new Ul(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,s,r,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(s=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(l));const c=a.optimize;null!=c&&(null===(o=null===(r=this._node.flags)||void 0===r?void 0:r.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n0,s=n.toAdd&&n.toAdd.length>0;if(i||s){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Yl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of Gl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Hl extends Dl{add_main(t){}}const jl=/\\\\\\\\n+/g;class Wl extends Dl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(jl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class ql extends Dl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class Xl extends Vl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Yl{static dispatch_node(t){return t.polyNodeController?new Xl(t):new Vl(t)}static dispatch_param(t){return t instanceof ao?new Hl(t):t instanceof Mo?new Wl(t):t instanceof Ao?new ql(t):new Dl(t)}}class $l{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Jl{constructor(t){this._data=t,this.report=new $l(this)}static async loadData(t){const e=new Jl(t);return await e.scene()}async scene(){const t=new Fl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const s=e.realtimeState;null!=s&&t.timeController.setRealtimeState(s),t.setFrame(e.frame||Nl.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Yl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Zl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),s=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;s.push(i)}const r=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();r.push(`${n}/ui.json?t=${t}`)}for(let t of s)r.push(t);let o=0;const a=r.length,l=r.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),h=[];for(let t of c)h.push(await t.json());const u={root:h[0],properties:h[1]};let d=2;t.editorMode&&(u.ui=h[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=h[t+d];p[e]=n}return this.assemble(u,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{r.matrixAutoUpdate=!0},c.onComplete=()=>{r.matrixAutoUpdate=t,r.matrixAutoUpdate||r.updateMatrix()}}if(o instanceof hh.a&&this._targetValue instanceof hh.a){const t={value:0},e=o,n=(new hh.a).copy(o),i=this._targetValue;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._targetValue)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._targetValue,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._targetValue[t],i);a&&this._startTimeline(e,n,c,a)}}}_sceneGraphProps(t,e){const n=e.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=t[e];let i=null;const s=[];return m.isNumber(n)?(i=t,s.push(e)):(i=n,this._targetValue instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._targetValue instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._targetValue instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._targetValue,hh.a),{targetProperty:n,toTarget:i,propertyNames:s}}{const e=t[n.shift()];if(e){const t=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(e,t)}}}_populateWithNode(t,e,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",t]);const i=t.p[this._propertyName];this._printDebug([\\\\\\\"targetParam\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):li.warn(`${this._propertyName} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Cr.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Cr.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Cr.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Cr.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Cr.COLOR:return this._populateVarsForParamColor(t,e,n);case Cr.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}li.warn(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._targetValue))return void li.warn(`TimelineBuilderProperty error: cannot animate integer param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._targetValue,r),this._startTimeline(e,n,i,s)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._targetValue))return void li.warn(`TimelineBuilderProperty error: cannot animate float param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._targetValue,r),this._startTimeline(e,n,i,s)}_populateVarsForParamVector2(t,e,n){if(!(this._targetValue instanceof d.a))return void li.warn(`TimelineBuilderProperty error: cannot animate vector2 param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s=t.value.clone(),r=[0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._targetValue.x,o),i.y=this.withOp(t.value.y,this._targetValue.y,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector3(t,e,n){if(!(this._targetValue instanceof p.a))return void li.warn(`TimelineBuilderProperty error: cannot animate vector3 param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._targetValue.x,o),i.y=this.withOp(t.value.y,this._targetValue.y,o),i.z=this.withOp(t.value.z,this._targetValue.z,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector4(t,e,n){if(!(this._targetValue instanceof _.a))return void li.warn(`TimelineBuilderProperty error: cannot animate vector4 param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._targetValue.x,o),i.y=this.withOp(t.value.y,this._targetValue.y,o),i.z=this.withOp(t.value.z,this._targetValue.z,o),i.w=this.withOp(t.value.w,this._targetValue.w,o),this._startTimeline(e,n,i,s)}_populateVarsForParamColor(t,e,n){if(!(this._targetValue instanceof p.a))return void li.warn(`TimelineBuilderProperty error: cannot animate color param '${t.path()}' with targetValue`,this._targetValue);const i=this._commonVars(e),s=t.valuePreConversion(),r=new p.a(s.r,s.g,s.b),o=[0,0,0];i.onUpdate=()=>{r.toArray(o),t.set(o)};const a=e.operation();i.x=this.withOp(t.value.r,this._targetValue.x,a),i.y=this.withOp(t.value.g,this._targetValue.y,a),i.z=this.withOp(t.value.b,this._targetValue.z,a),this._startTimeline(e,n,i,r)}withOp(t,e,n){switch(n){case I_.SET:return e;case I_.ADD:return t+e;case I_.SUBSTRACT:return t-e}os.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||ch.NONE;n&&(e.ease=n);const i=t.delay();null!=i&&(e.delay=i);const s=t.repeatParams();return s&&(e.repeat=s.count,e.repeatDelay=s.delay,e.yoyo=s.yoyo),e}_startTimeline(t,e,n,i){const s=t.position(),r=s?s.toParameter():void 0;e.to(i,n,r)}}function mh(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function fh(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var gh,vh,yh,xh,bh,wh,Th,Ah,Mh,Eh,Sh,Ch,Nh,Lh={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Oh={duration:.5,overwrite:!1,delay:0},Ph=1e8,Rh=1e-8,Ih=2*Math.PI,Fh=Ih/4,Dh=0,Bh=Math.sqrt,zh=Math.cos,kh=Math.sin,Uh=function(t){return\\\\\\\"string\\\\\\\"==typeof t},Gh=function(t){return\\\\\\\"function\\\\\\\"==typeof t},Vh=function(t){return\\\\\\\"number\\\\\\\"==typeof 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oh{constructor(){super(...arguments),this.paramsConfig=rm}static type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(X_[this.pv.mode]){case q_.RELATIVE:return this._relative_label();case q_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=$_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new B_,n=new J_;n.setMode(X_[this.pv.mode]),n.setRelativeTo($_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const am=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"position\\\\\\\")}};class lm extends oh{constructor(){super(...arguments),this.paramsConfig=am}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new B_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var cm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(cm||(cm={}));const hm=[cm.CUSTOM,cm.FROM_SCENE_GRAPH,cm.FROM_NODE],um=hm.indexOf(cm.CUSTOM),dm=hm.indexOf(cm.FROM_SCENE_GRAPH),pm=hm.indexOf(cm.FROM_NODE);const _m=new class extends ua{constructor(){super(...arguments),this.mode=ha.INTEGER(um,{menu:{entries:hm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:pm}}),this.objectMask=ha.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:dm}}),this.printResolve=ha.BUTTON(null,{visibleIf:{mode:dm},callback:t=>{mm.PARAM_CALLBACK_printResolve(t)}}),this.overridePropertyName=ha.BOOLEAN(0,{visibleIf:[{mode:dm},{mode:pm}]}),this.propertyName=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:dm},{overridePropertyName:!0,mode:pm}]}),this.size=ha.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:um}}),this.value1=ha.FLOAT(0,{visibleIf:{mode:um,size:1}}),this.value2=ha.VECTOR2([0,0],{visibleIf:{mode:um,size:2}}),this.value3=ha.VECTOR3([0,0,0],{visibleIf:{mode:um,size:3}}),this.value4=ha.VECTOR4([0,0,0,0],{visibleIf:{mode:um,size:4}})}};class mm extends oh{constructor(){super(...arguments),this.paramsConfig=_m}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new B_;await this._prepareTimelineBuilder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(hm.indexOf(t))}async _prepareTimelineBuilder(t){const e=hm[this.pv.mode];switch(e){case cm.CUSTOM:return this._prepareTimebuilderCustom(t);case cm.FROM_SCENE_GRAPH:return this._prepareTimebuilderFromSceneGraph(t);case cm.FROM_NODE:return await this._prepareTimebuilderFromNode(t)}os.unreachable(e)}_prepareTimebuilderCustom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepareTimebuilderFromSceneGraph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof hh.a)&&t.setPropertyValue(i)}}async _prepareTimebuilderFromNode(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const s=i.value;s&&(m.isNumber(s)||m.isVector(s))&&t.setPropertyValue(s)}static PARAM_CALLBACK_printResolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const fm=new class extends ua{constructor(){super(...arguments),this.unlimited=ha.BOOLEAN(0),this.count=ha.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=ha.FLOAT(0),this.yoyo=ha.BOOLEAN(0)}};class gm extends oh{constructor(){super(...arguments),this.paramsConfig=fm}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new B_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const vm=new class extends ua{constructor(){super(...arguments),this.input=ha.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class ym extends oh{constructor(){super(...arguments),this.paramsConfig=vm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class xm{constructor(t,e){this._scene=t,this._options=e}clone(){return new xm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class bm{constructor(){this._update_matrix=!1}clone(){const t=new bm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var wm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(wm||(wm={}));const Tm=[wm.SCENE_GRAPH,wm.NODE],Am=Tm.indexOf(wm.SCENE_GRAPH),Mm=Tm.indexOf(wm.NODE);const Em=new class extends ua{constructor(){super(...arguments),this.type=ha.INTEGER(Am,{menu:{entries:Tm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:Mm}}),this.objectMask=ha.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:Am}}),this.updateMatrix=ha.BOOLEAN(0,{visibleIf:{type:Am}}),this.printResolve=ha.BUTTON(null,{callback:(t,e)=>{Sm.PARAM_CALLBACK_print_resolve(t)}})}};class Sm extends oh{constructor(){super(...arguments),this.paramsConfig=Em}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=Tm[this.pv.type];switch(t){case wm.NODE:return this.pv.nodePath;case wm.SCENE_GRAPH:return this.pv.objectMask}os.unreachable(t)}))}))}))}cook(t){const e=t[0]||new B_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(Tm.indexOf(t))}_create_target(t){const e=Tm[this.pv.type];switch(e){case wm.NODE:return new xm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case wm.SCENE_GRAPH:return new xm(this.scene(),{objectMask:this.pv.objectMask})}os.unreachable(e)}_set_update_callback(t){const e=Tm[this.pv.type];let n=t.updateCallback();switch(e){case wm.NODE:return;case wm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new bm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}os.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=Tm[this.pv.type],e=new B_,n=this._create_target(e);switch(t){case wm.NODE:return console.log(n.node());case wm.SCENE_GRAPH:return console.log(n.objects())}}}class Cm extends aa{static context(){return ts.ANIM}cook(){this.cookController.endCook()}}class Nm extends Cm{}class Lm extends Nm{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Om extends Nm{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Pm extends Nm{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Rm extends Nm{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Im={dependsOnDisplayNode:!0};class Fm{constructor(t,e,n=Im){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Mi(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Dm{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Bm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Bm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Bm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var zm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class km{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Um=new ot.a(-1,1,1,-1,0,1),Gm=new S.a;Gm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Gm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class Vm{constructor(t){this._mesh=new B.a(Gm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Um)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class Hm extends km{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new Vm(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class jm extends km{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),s=t.state;let r,o;s.buffers.color.setMask(!1),s.buffers.depth.setMask(!1),s.buffers.color.setLocked(!0),s.buffers.depth.setLocked(!0),this.inverse?(r=0,o=1):(r=1,o=0),s.buffers.stencil.setTest(!0),s.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),s.buffers.stencil.setFunc(i.ALWAYS,r,4294967295),s.buffers.stencil.setClear(o),s.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),s.buffers.color.setLocked(!1),s.buffers.depth.setLocked(!1),s.buffers.stencil.setLocked(!1),s.buffers.stencil.setFunc(i.EQUAL,1,4294967295),s.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),s.buffers.stencil.setLocked(!0)}}class Wm extends km{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class qm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Q(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===zm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===Hm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new Hm(zm),this.clock=new Dm}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e({name:Object.keys(t)[0],value:Object.values(t)[0]}))),tf=Jm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class ef extends ua{constructor(){super(...arguments),this.prependRenderPass=ha.BOOLEAN(1),this.useRenderTarget=ha.BOOLEAN(1),this.tmagFilter=ha.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=ha.INTEGER(Zm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Km}}),this.tminFilter=ha.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=ha.INTEGER(Qm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:tf}}),this.stencilBuffer=ha.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=ha.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class nf{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new qm(e,t)}else n=new qm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=li.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Ym(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class sf extends Cm{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rf extends Nm{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var of=n(44);const af=\\\\\\\"input texture\\\\\\\",lf=[af,af,af,af];for(var cf=new Uint16Array(32),hf=0;hf<32;hf++)cf[hf]=28898;const uf=new vo.a(cf,32,1,w.gb,w.M);class df extends aa{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ki(this)}static context(){return ts.COP}static displayedInputNames(){return lf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(uf)}}class pf extends df{}class _f{constructor(){this._id=_f.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}_f.__next_id=0;class mf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${ss.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${ss.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${ss.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${ss.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=ss.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=ss.ensureFloat(t)),m.isNumber(e)&&(e=ss.ensureFloat(e)),m.isNumber(n)&&(n=ss.ensureFloat(n)),m.isNumber(i)&&(i=ss.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=ss.ensureFloat(t)),m.isNumber(e)&&(e=ss.ensureFloat(e)),m.isNumber(n)&&(n=ss.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=ss.ensureFloat(t)),m.isNumber(e)&&(e=ss.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return ss.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:ss.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return ss.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:ss.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const ff=/\\\\/+/g;class gf extends aa{static context(){return ts.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==ts.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(ff,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return mf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return mf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const vf=new class extends ua{};class yf extends gf{constructor(){super(...arguments),this.paramsConfig=vf}}const xf=[Uo.FLOAT,Uo.VEC2,Uo.VEC3,Uo.VEC4];const bf=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(0,{menu:{entries:xf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=ha.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=ha.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class wf extends gf{constructor(){super(...arguments),this.paramsConfig=bf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ps.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[xf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>wf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[xf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return wf.INPUT_NAME}get output_name(){return wf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(xf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(wf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(wf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(wf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}wf.INPUT_NAME=\\\\\\\"in\\\\\\\",wf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class Tf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var Af,Mf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(Af||(Af={}));class Ef{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new Tf}}class Sf extends Ef{constructor(t,e,n){super(Af.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Cf extends Ef{constructor(t,e){super(Af.FUNCTION,Uo.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Nf extends Ef{constructor(t,e,n){super(Af.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Lf extends Ef{constructor(t,e,n){super(Af.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(Mf||(Mf={}));const Of={position:\\\\\\\"vec3( position )\\\\\\\"};class Pf extends _f{handle_globals_node(t,e,n){var i,s;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(!r)return;const o=t.glVarName(e),a=r.type(),l=new Lf(t,a,o);n.addDefinitions(t,[l]);const c=null===(s=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!c)return;const h=c.shader_config(n.current_shader_name);if(!h)return;const u=h.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of u)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==u.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Of[t]}variable_config_default(t){return Pf.variable_config_default(t)}read_attribute(t,e,n,i){return Pf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var s,r;Pf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new Sf(t,e,n)],Mf.VERTEX);const o=i.current_shader_name;switch(o){case Mf.VERTEX:return n;case Mf.FRAGMENT:{if(!(t instanceof wf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Lf(t,e,a),c=new Map;c.set(Mf.FRAGMENT,[]);const u=new Map;u.set(Mf.FRAGMENT,[]),h.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(r=null===(s=t.material_node)||void 0===s?void 0:s.assemblerController)||void 0===r?void 0:r.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)h.pushOnArrayAtEntry(c,t,l),h.pushOnArrayAtEntry(u,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),u.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Pf.read_attribute(t,e,n,i)}}Pf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Pf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Rf=[Uo.FLOAT,Uo.VEC2,Uo.VEC3,Uo.VEC4];const If=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(0,{menu:{entries:Rf.map(((t,e)=>({name:t,value:e})))}})}};class Ff extends gf{constructor(){super(...arguments),this.paramsConfig=If,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Rf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Ff.INPUT_NAME}setLines(t){if(t.current_shader_name==Mf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Lf(this,e,n),s=`${n} = ${mf.any(this.variableForInput(Ff.INPUT_NAME))}`;t.addDefinitions(this,[i],Mf.VERTEX),t.addBodyLines(this,[s],Mf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Rf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Ff.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Df{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Ff.type())}static findAttributeExportNodes(t){return t.nodesByType(wf.type()).filter((t=>t.isExporting()))}}class Bf{static overlay(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.max(t.width,e.width),s.height=Math.max(t.height,e.height);let r=s.getContext(\\\\\\\"2d\\\\\\\");r.drawImage(t,0,0,t.width,t.height),r.drawImage(e,0,0,e.width,e.height);const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=t,s.height=e;let r=s.getContext(\\\\\\\"2d\\\\\\\");r.beginPath(),r.rect(0,0,t,e),r.fillStyle=\\\\\\\"white\\\\\\\",r.fill();const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const s=Math.min(t.width,t.height),r=t.width/t.height;i.width=s,i.height=s;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=r>1,l=a?(t.width-s)/2:(t.height-s)/2;a?o.drawImage(t,l,0,s,s,0,0,s,s):o.drawImage(t,0,l,s,s,0,0,s,s);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),h=new Image;h.onload=()=>{e(h)},h.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var zf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(zf||(zf={}));class kf{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Bf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,s=this._data_texture.image.data,r=4*n;for(let t=0;t0:s.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(s.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":s.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":s.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":s.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":s.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":s.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":s.uniforms[e].value=(new G.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":s.uniforms[e].value=(new A.a).fromArray(i.value);break;default:s.uniforms[e].value=i.value}}if(void 0!==t.defines&&(s.defines=t.defines),void 0!==t.vertexShader&&(s.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(s.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)s.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(s.flatShading=1===t.shading),void 0!==t.size&&(s.size=t.size),void 0!==t.sizeAttenuation&&(s.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(s.map=n(t.map)),void 0!==t.matcap&&(s.matcap=n(t.matcap)),void 0!==t.alphaMap&&(s.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(s.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(s.bumpScale=t.bumpScale),void 0!==t.normalMap&&(s.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(s.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),s.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(s.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(s.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(s.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(s.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(s.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(s.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(s.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(s.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(s.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(s.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(s.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(s.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(s.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(s.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(s.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(s.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(s.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(s.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(s.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(s.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(s.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(s.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(s.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(s.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(s.thicknessMap=n(t.thicknessMap)),s}setTextures(t){return this.textures=t,this}}class Qf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new Zf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new G.a;for(let t=0;t{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(ts.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class eg extends Qf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,rg.handleDependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{tg.callback(n,i)}})}}}class ng{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const ig=[256,256];const sg=new class extends ua{constructor(){super(...arguments),this.resolution=ha.VECTOR2(ig),this.useCameraRenderer=ha.BOOLEAN(0)}};class rg extends df{constructor(){super(...arguments),this.paramsConfig=sg,this.persisted_config=new eg(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new B.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new vs,this._texture_camera=new of.a,this._children_controller_context=ts.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._boundRenderOnTarget=this.renderOnTarget.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return jn.GL_TEXTURE}_create_assembler_controller(){const t=li.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Pf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Df.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),s=t.assembler.uniforms();i&&s&&(this._fragment_shader=i,this._uniforms=s),rg.handleDependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handleDependencies(t,e,n){const i=t.scene();if(e){n&&i.uniformsController.addTimeDependentUniformOwner(t._uniformCallbackName(),n);const e=t._callbackName();i.registeredBeforeTickCallbacks().has(e)||i.registerOnBeforeTick(e,t._boundRenderOnTarget)}else t._removeCallbacks()}_callbackName(){return`cop/builder_${this.graphNodeId()}`}_uniformCallbackName(){return`cop/builder_uniforms_${this.graphNodeId()}`}dispose(){super.dispose(),this._removeCallbacks()}_removeCallbacks(){const t=this.scene();t.uniformsController.removeTimeDependentUniformOwner(this._uniformCallbackName()),t.unRegisterOnBeforeTick(this._callbackName())}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Uf(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new kf(zf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,type:ng.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const og=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ag=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],lg=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],cg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],hg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function ug(t){return{cook:!1,callback:e=>{Sg[t](e)}}}const dg={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},pg=ug(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),_g=ug(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),mg=ug(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),fg=ug(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),gg=ug(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),vg=ug(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),yg=ug(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),xg=ug(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),bg=ug(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),wg=ug(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),Tg=ug(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),Ag=ug(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function Mg(t){return class extends t{constructor(){super(...arguments),this.tencoding=ha.BOOLEAN(0,{...pg}),this.encoding=ha.INTEGER(dg.ENCODING,{visibleIf:{tencoding:1},menu:{entries:og.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...pg}),this.tmapping=ha.BOOLEAN(0,{..._g}),this.mapping=ha.INTEGER(dg.MAPPING,{visibleIf:{tmapping:1},menu:{entries:lg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},..._g}),this.twrap=ha.BOOLEAN(0,{...mg}),this.wrapS=ha.INTEGER(dg.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ag.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...mg}),this.wrapT=ha.INTEGER(dg.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ag.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...mg}),this.tminFilter=ha.BOOLEAN(0,{...fg}),this.minFilter=ha.INTEGER(Qm,{visibleIf:{tminFilter:1},menu:{entries:tf},...fg}),this.tmagFilter=ha.BOOLEAN(0,{...fg}),this.magFilter=ha.INTEGER(Zm,{visibleIf:{tmagFilter:1},menu:{entries:Km},...fg}),this.tanisotropy=ha.BOOLEAN(0,{...gg}),this.useRendererMaxAnisotropy=ha.BOOLEAN(0,{visibleIf:{tanisotropy:1},...gg}),this.anisotropy=ha.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...gg}),this.tflipY=ha.BOOLEAN(0,{...vg}),this.flipY=ha.BOOLEAN(0,{visibleIf:{tflipY:1},...vg}),this.ttransform=ha.BOOLEAN(0,{...yg}),this.offset=ha.VECTOR2([0,0],{visibleIf:{ttransform:1},...bg}),this.repeat=ha.VECTOR2([1,1],{visibleIf:{ttransform:1},...xg}),this.rotation=ha.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...wg}),this.center=ha.VECTOR2([0,0],{visibleIf:{ttransform:1},...Tg}),this.tadvanced=ha.BOOLEAN(0,{...Ag}),this.tformat=ha.BOOLEAN(0,{visibleIf:{tadvanced:1},...Ag}),this.format=ha.INTEGER(dg.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:hg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...Ag}),this.ttype=ha.BOOLEAN(0,{visibleIf:{tadvanced:1},...Ag}),this.type=ha.INTEGER(dg.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:cg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...Ag})}}}class Eg extends(Mg(ua)){}new Eg;class Sg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=dg.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=dg.FORMAT,e.ttype?t.type=e.type:t.type=dg.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=dg.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=dg.WRAPPING,t.wrapT=dg.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Uf(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of og){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of lg){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ag){const s=Object.keys(i)[0];i[s]==e[n]&&t.push(`${n}: ${s}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Jm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of $m){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Cg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}Cg.prototype.isCanvasTexture=!0;class Ng extends(Mg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=ha.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=ha.BUTTON(null,{cook:!1,callback:t=>{Og.PARAM_CALLBACK_update(t)}})}}}(ua))){}const Lg=new Ng;class Og extends df{constructor(){super(...arguments),this.paramsConfig=Lg,this.textureParamsController=new Sg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Cg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Pg=new class extends ua{constructor(){super(...arguments),this.resolution=ha.VECTOR2([256,256],{callback:t=>{Rg.PARAM_CALLBACK_reset(t)}}),this.color=ha.COLOR([1,1,1])}};class Rg extends df{constructor(){super(...arguments),this.paramsConfig=Pg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),s=255*i[0],r=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t({name:t,value:e})))}})}};class kg extends df{constructor(){super(...arguments),this.paramsConfig=zg}static type(){return Fg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(ts.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Bg[this.pv.mode]==Dg.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Ug;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Ug||(Ug={}));const Gg=[Ug.REFLECTION,Ug.REFRACTION];const Vg=new class extends ua{constructor(){super(...arguments),this.useCameraRenderer=ha.BOOLEAN(1),this.mode=ha.INTEGER(0,{menu:{entries:Gg.map(((t,e)=>({name:t,value:e})))}})}};class Hg extends df{constructor(){super(...arguments),this.paramsConfig=Vg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Uf(this);const e=await this._renderer_controller.renderer();if(e){const n=new Tt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new kf(zf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Gg[this.pv.mode]==Ug.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class jg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==r?r:w.V,this.magFilter=void 0!==s?s:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}jg.prototype.isVideoTexture=!0;var Wg=n(80);const qg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Xg=n(28);const Yg=new Xg.b;Yg.setURLModifier((t=>{const e=li.assetUrls.remapedUrl(t);if(e)return e;const n=li.blobs.blobUrl(t);return n||t}));class $g{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Yg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return $g.extension(this._url)}async _urlToLoad(){const{storedUrl:t,fullUrl:e}=this._urlData();this._node&&await li.blobs.fetchBlobForNode({storedUrl:t,fullUrl:e,node:this._node});return li.blobs.blobUrl(t)||e}deregisterUrl(){const{storedUrl:t}=this._urlData();li.blobs.deregisterUrl(t)}_urlData(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}return{fullUrl:t,storedUrl:e}}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,s=n.fullUrl,r=t.node;e.push(li.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:s,node:r}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}$g.loadingManager=Yg;const Jg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\",\\\\\\\"ogg\\\\\\\"];var Zg;!function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(Zg||(Zg={}));const Qg=[Zg.JPEG,Zg.JPG,Zg.PNG,Zg.EXR,Zg.BASIS,Zg.HDR];function Kg(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");return e[e.length-1]}class tv extends $g{constructor(t,e,n,i,s){super(t,i,n),this._param=e,this._node=n,this._scene=i,this._forceVideo=!1,this._forceImage=!1,this._forceVideo=(null==s?void 0:s.forceVideo)||this._forceVideo,this._forceImage=(null==s?void 0:s.forceImage)||this._forceImage}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=bi.findNode(this._node,t),n)if(n instanceof pf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),s=await this._urlToLoad();if(this._forceVideo||Jg.includes(i)){e(await this._loadVideo(s))}else if(this._forceImage||Qg.includes(i))try{e(await this._loadImage(s,t))}catch(t){n()}}))}_loadImage(t,e){return new Promise((async(n,i)=>{const s=this.extension();this.loader_for_ext(s,e).then((async e=>{e?(tv.incrementInProgressLoadsCount(),await tv.waitForMaxConcurrentLoadsQueueFreed(),e.load(t,(e=>{tv.decrementInProgressLoadsCount();const i=tv._onTextureLoadedCallback;i&&i(t,e),n(e)}),void 0,(t=>{tv.decrementInProgressLoadsCount(),li.warn(\\\\\\\"error\\\\\\\",t),i()}))):i()}))}))}_loadVideo(t){return new Promise((async(e,n)=>{tv.incrementInProgressLoadsCount(),await tv.waitForMaxConcurrentLoadsQueueFreed();const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const n=new jg(i);tv.decrementInProgressLoadsCount();const s=tv._onTextureLoadedCallback;s&&s(t,n),e(n)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=$g.extension(t);let o=tv.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||tv._default_video_source_type(t),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(s);let a=t;a=\\\\\\\"mp4\\\\\\\"==r?tv.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):tv.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=$g.extension(a);o=tv.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||tv._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static module_names(t){switch(t){case Zg.EXR:return[Hn.EXRLoader];case Zg.HDR:return[Hn.RGBELoader];case Zg.BASIS:return[Hn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case Zg.EXR:return await this._exr_loader(e);case Zg.HDR:return await this._hdr_loader(e);case Zg.BASIS:return await tv._basis_loader(this._node)}return new Wg.a(this.loadingManager)}async _exr_loader(t){const e=await li.modulesRegister.module(Hn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await li.modulesRegister.module(Hn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await li.modulesRegister.module(Hn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.BASISPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const r=await li.renderersController.waitForRenderer();return r?n.detectSupport(r):li.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}static _default_video_source_type(t){return`video/${$g.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():ng.isChrome()?10:4}static _init_concurrent_loads_delay(){return ng.isChrome()?0:10}static incrementInProgressLoadsCount(){this.in_progress_loads_count++}static decrementInProgressLoadsCount(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async waitForMaxConcurrentLoadsQueueFreed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}tv.PARAM_DEFAULT=`${qg}/textures/uv.jpg`,tv.PARAM_ENV_DEFAULT=`${qg}/textures/piz_compressed.exr`,tv.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},tv.MAX_CONCURRENT_LOADS_COUNT=tv._init_max_concurrent_loads_count(),tv.CONCURRENT_LOADS_DELAY=tv._init_concurrent_loads_delay(),tv.in_progress_loads_count=0,tv._queue=[];var ev=n(114);class nv extends(Mg(function(t){return class extends t{constructor(){super(...arguments),this.url=ha.STRING(tv.PARAM_DEFAULT,{fileBrowse:{type:[Rr.TEXTURE_IMAGE]}}),this.reload=ha.BUTTON(null,{callback:(t,e)=>{sv.PARAM_CALLBACK_reload(t)}}),this.play=ha.BOOLEAN(1,{cook:!1,callback:t=>{sv.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=ha.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{sv.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(ua))){}const iv=new nv;class sv extends df{constructor(){super(...arguments),this.paramsConfig=iv,this.textureParamsController=new Sg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=$g.extension(this.pv.url||\\\\\\\"\\\\\\\");return tv.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(\\\\\\\"gif\\\\\\\"!=Kg(this.pv.url).toLowerCase())this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{tv.incrementInProgressLoadsCount(),await tv.waitForMaxConcurrentLoadsQueueFreed();const t=await fetch(this.pv.url),e=await t.arrayBuffer(),n=await Object(ev.parseGIF)(e),i=!0;this._parsedFrames=await Object(ev.decompressFrames)(n,i);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),tv.decrementInProgressLoadsCount(),this._gifCanvasElement){const t=new Cg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}function rv(t){return class extends t{constructor(){super(...arguments),this.checkFileType=ha.BOOLEAN(!0)}}}class ov extends(rv(Mg(function(t){return class extends t{constructor(){super(...arguments),this.url=ha.STRING(tv.PARAM_DEFAULT,{fileBrowse:{type:[Rr.TEXTURE_IMAGE]}}),this.reload=ha.BUTTON(null,{callback:(t,e)=>{lv.PARAM_CALLBACK_reload(t,e)}})}}}(ua)))){}const av=new ov;class lv extends df{constructor(){super(...arguments),this.paramsConfig=av,this.textureParamsController=new Sg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=$g.extension(this.pv.url||\\\\\\\"\\\\\\\");return tv.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Qg.includes(Kg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{const e=await this._loadTexture();if(e){const n=t[0];n&&Sg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}var e}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.clearLoadedBlob(),this.p.url.setDirty()}_loader(){return new tv(this.pv.url,this.p.url,this,this.scene(),{forceImage:!this.pv.checkFileType})}async _loadTexture(){let t=null;try{t=await this._loader().load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),t&&(t.matrixAutoUpdate=!1)}catch(t){}return t||this.states.error.set(`could not load texture '${this.pv.url}'`),t}clearLoadedBlob(){this._loader().deregisterUrl()}}var cv=n(33);const hv=.005;class uv{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new vs,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:hv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new hh.a,this._s=new p.a;const n=ng.isAndroid()||ng.isiOS()?w.M:w.G;this.progressiveLightMap1=new Q(this.res,this.res,{type:n}),this.progressiveLightMap2=new Q(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const s=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,s)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(s-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const r={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=r.previousShadowMap,i.uniforms.pixelOffset=r.pixelOffset,n.uniforms.previousShadowMap=r.previousShadowMap,n.uniforms.pixelOffset=r.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new qf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:hv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:hv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const dv=new class extends ua{constructor(){super(...arguments),this.update=ha.BUTTON(null,{callback:t=>{pv.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=ha.BOOLEAN(1),this.lightMapRes=ha.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=ha.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=ha.FLOAT(hv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=ha.BOOLEAN(1),this.blurAmount=ha.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=ha.FLOAT(1,{range:[0,10]}),this.objectsMask=ha.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=ha.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=ha.BUTTON(null,{callback:t=>{pv.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class pv extends df{constructor(){super(...arguments),this.paramsConfig=dv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await li.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new uv(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new kf(zf.Float32Array),this._renderer_controller=this._renderer_controller||new Uf(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof cv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof cv.a||!n.has(e)&&e instanceof B.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const _v=new ua;class mv extends df{constructor(){super(...arguments),this.paramsConfig=_v}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}class fv extends(Mg(function(t){return class extends t{constructor(){super(...arguments),this.camera=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:ds}}),this.resolution=ha.VECTOR2([1024,1024]),this.useCameraRenderer=ha.BOOLEAN(1),this.render=ha.BUTTON(null,{callback:t=>{vv.PARAM_CALLBACK_render(t)}})}}}(ua))){}const gv=new fv;class vv extends df{constructor(){super(...arguments),this.paramsConfig=gv,this.textureParamsController=new Sg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(ts.OBJ),this._camera_node&&ds.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Uf(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new kf(zf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,generateMipmaps:!0,type:ng.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const yv=new class extends ua{constructor(){super(...arguments),this.input=ha.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class xv extends df{constructor(){super(...arguments),this.paramsConfig=yv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class bv extends(Mg(ua)){}const wv=new bv;class Tv extends df{constructor(){super(...arguments),this.paramsConfig=wv,this.textureParamsController=new Sg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class Av extends(rv(Mg(function(t){return class extends t{constructor(){super(...arguments),this.url=ha.STRING(tv.PARAM_DEFAULT,{fileBrowse:{type:[Rr.TEXTURE_VIDEO]}}),this.reload=ha.BUTTON(null,{callback:(t,e)=>{Ev.PARAM_CALLBACK_reload(t,e)}}),this.play=ha.BOOLEAN(1,{cook:!1,callback:t=>{Ev.PARAM_CALLBACK_video_update_play(t)}}),this.muted=ha.BOOLEAN(1,{cook:!1,callback:t=>{Ev.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=ha.BOOLEAN(1,{cook:!1,callback:t=>{Ev.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=ha.FLOAT(0,{cook:!1}),this.setVideoTime=ha.BUTTON(null,{cook:!1,callback:t=>{Ev.PARAM_CALLBACK_video_update_time(t)}})}}}(ua)))){}const Mv=new Av;class Ev extends df{constructor(){super(...arguments),this.paramsConfig=Mv,this.textureParamsController=new Sg(this)}static type(){return Fg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=$g.extension(this.pv.url||\\\\\\\"\\\\\\\");return tv.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Jg.includes(Kg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&Sg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}var e}dispose(){var t;super.dispose(),this._video&&(null===(t=this._video.parentElement)||void 0===t||t.removeChild(this._video))}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new tv(t,n,this,this.scene(),{forceVideo:!this.pv.checkFileType});try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class Sv extends(Mg(function(t){return class extends t{constructor(){super(...arguments),this.res=ha.VECTOR2([1024,1024])}}}(ua))){}const Cv=new Sv;class Nv extends df{constructor(){super(...arguments),this.paramsConfig=Cv,this.textureParamsController=new Sg(this)}static type(){return Fg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new jg(this._video),n=t[0];if(n&&Sg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Lv extends aa{static context(){return ts.COP}cook(){this.cookController.endCook()}}class Ov extends Lv{}class Pv extends Ov{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Rv extends Ov{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Iv extends Ov{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Fv extends Ov{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Dv extends Lv{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Bv extends Ov{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var zv,kv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(zv||(zv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(kv||(kv={}));const Uv=new class extends ua{constructor(){super(...arguments),this.animation=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.ANIM},dependentOnFoundNode:!1}),this.play=ha.BUTTON(null,{callback:t=>{Gv.PARAM_CALLBACK_play(t)}}),this.pause=ha.BUTTON(null,{callback:t=>{Gv.PARAM_CALLBACK_pause(t)}})}};class Gv extends Va{constructor(){super(...arguments),this.paramsConfig=Uv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(zv.START,Ko.BASE,this._play.bind(this)),new ta(zv.STOP,Ko.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(kv.START,Ko.BASE),new ta(kv.COMPLETE,Ko.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(ts.ANIM);if(!n)return;const i=await n.compute();i&&(this._timelineBuilder=i.coreContent(),this._timelineBuilder&&(this._timeline&&this._timeline.kill(),this._timeline=F_.timeline(),this._timelineBuilder.populate(this._timeline),this._timeline.vars.onStart=()=>{this._triggerAnimationStarted(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this._triggerAnimationCompleted(t)}))}_pause(){this._timeline&&this._timeline.pause()}_triggerAnimationStarted(t){this.dispatchEventToOutput(kv.START,t)}_triggerAnimationCompleted(t){this.dispatchEventToOutput(kv.COMPLETE,t)}}const Vv=\\\\\\\"event\\\\\\\";const Hv=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(1),this.inputsCount=ha.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class jv extends Va{constructor(){super(...arguments),this.paramsConfig=Hv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>Vv)),this.io.connection_points.set_expected_output_types_function((()=>[Ko.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill(Ko.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Vv,t)}}const Wv=new class extends ua{constructor(){super(...arguments),this.blocking=ha.BOOLEAN(1)}};class qv extends Va{constructor(){super(...arguments),this.paramsConfig=Wv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(\\\\\\\"in\\\\\\\",Ko.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(qv.OUTPUT,Ko.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(qv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Xv;qv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Xv||(Xv={}));const Yv=new class extends ua{constructor(){super(...arguments),this.dispatch=ha.BUTTON(null,{callback:t=>{$v.PARAM_CALLBACK_execute(t)}})}};class $v extends Va{constructor(){super(...arguments),this.paramsConfig=Yv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ta(Xv.OUT,Ko.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Xv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class Jv extends Va{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const s=await this.createControlsInstance(t,i),r=this._controls_by_viewer.get(e);r&&r.dispose(),this._controls_by_viewer.set(e,s);const o=li.performance.performanceManager().now();return s.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setupControls(s),s}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Zv=n(27);const Qv=new p.a(0,0,1),Kv=new Zv.a,ty=new hh.a,ey=new hh.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),ny={type:\\\\\\\"change\\\\\\\"};class iy extends J.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new hh.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},s=function(){e.screenOrientation=window.orientation||0};this.connect=function(){s(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?On.e(t.alpha)+e.alphaOffset:0,s=t.beta?On.e(t.beta):0,r=t.gamma?On.e(t.gamma):0,o=e.screenOrientation?On.e(e.screenOrientation):0;!function(t,e,n,i,s){Kv.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler(Kv),t.multiply(ey),t.multiply(ty.setFromAxisAngle(Qv,-s))}(e.object.quaternion,i,s,r,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(ny))}},this.dispose=function(){e.disconnect()},this.connect()}}const sy=new class extends ua{constructor(){super(...arguments),this.enabled=ha.BOOLEAN(1)}};class ry extends Jv{constructor(){super(...arguments),this.paramsConfig=sy,this._controls_by_element_id=new Map}static type(){return _s.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async createControlsInstance(t,e){const n=new iy(t);return this._controls_by_element_id.set(e.id,n),n}setupControls(t){t.enabled=this.pv.enabled}updateRequired(){return!0}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class oy{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(On.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const ay={type:\\\\\\\"change\\\\\\\"},ly={type:\\\\\\\"start\\\\\\\"},cy={type:\\\\\\\"end\\\\\\\"};class hy extends J.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(ay),n.update(),s=i.NONE},this.update=function(){const e=new p.a,u=(new hh.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=u.clone().invert(),_=new p.a,m=new hh.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(u),o.setFromVector3(e),n.autoRotate&&s===i.NONE&&E(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;tMath.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(h||_.distanceToSquared(n.object.position)>r||8*(1-m.dot(n.object.quaternion))>r)&&(n.dispatchEvent(ay),_.copy(n.object.position),m.copy(n.object.quaternion),h=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let s=i.NONE;const r=1e-6,o=new oy,a=new oy;let l=1;const c=new p.a;let h=!1;const u=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function M(){return Math.pow(.95,n.zoomSpeed)}function E(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const s=n.domElement;if(n.object.isPerspectiveCamera){const r=n.object.position;t.copy(r).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/s.clientHeight,n.object.matrix),N(2*i*o/s.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/s.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/s.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){u.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)u.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);u.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function B(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function z(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,u).multiplyScalar(n.rotateSpeed);const e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_)}function k(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,s=t.pageY-e.y,r=Math.sqrt(i*i+s*s);x.set(0,r),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),s=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),s=i.TOUCH_PAN;break;default:s=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&B(),n.enablePan&&D(),s=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&B(),n.enableRotate&&F(),s=i.TOUCH_DOLLY_ROTATE;break;default:s=i.NONE}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(ly)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),s=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),s=i.PAN}else{if(!1===n.enableRotate)return;R(t),s=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;R(t),s=i.ROTATE}else{if(!1===n.enablePan)return;I(t),s=i.PAN}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(ly)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),s){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;z(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;k(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&k(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&z(t)}(t),n.update();break;default:s=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(s){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,u).multiplyScalar(n.rotateSpeed);var e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(M()):b.y<0&&P(M()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(cy),s=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||s!==i.NONE&&s!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(ly),function(t){t.deltaY<0?P(M()):t.deltaY>0&&O(M()),n.update()}(t),n.dispatchEvent(cy))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:E(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:E(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e{gy.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=ha.BOOLEAN(0),this.keysMode=ha.INTEGER(my.indexOf(_y.PAN),{visibleIf:{enableKeys:1},menu:{entries:my.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=ha.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:my.indexOf(_y.PAN)}}),this.keysRotateSpeedVertical=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:my.indexOf(_y.ROTATE)}}),this.keysRotateSpeedHorizontal=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:my.indexOf(_y.ROTATE)}})}};class gy extends Jv{constructor(){super(...arguments),this.paramsConfig=fy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return _s.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ta(dy,Ko.BASE),new ta(py,Ko.BASE),new ta(\\\\\\\"end\\\\\\\",Ko.BASE)])}async createControlsInstance(t,e){const n=new hy(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(dy,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(py,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setupControls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=my[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}updateRequired(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class vy extends gy{static type(){return _s.MAP}async create_controls_instance(t,e){const n=new uy(t,e);return this._bind_listeners_to_controls_instance(n),n}}const yy=new class extends ua{constructor(){super(...arguments),this.delay=ha.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class xy extends Va{constructor(){super(...arguments),this.paramsConfig=yy}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(\\\\\\\"in\\\\\\\",Ko.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(\\\\\\\"out\\\\\\\",Ko.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const by={type:\\\\\\\"change\\\\\\\"},wy={type:\\\\\\\"lock\\\\\\\"},Ty={type:\\\\\\\"unlock\\\\\\\"},Ay=Math.PI/2,My=new p.a,Ey=new oy;class Sy extends J.a{constructor(t,e,n){super(),this.camera=t,this.domElement=e,this.player=n,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.rotateSpeed=1,this.euler=new Zv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._azimuthalAngle=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e*this.rotateSpeed,this.euler.x-=.002*n*this.rotateSpeed,this.euler.x=Math.max(Ay-this.maxPolarAngle,Math.min(Ay-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this._computeAzimuthalAngle(),this.dispatchEvent(by)}}_computeAzimuthalAngle(){this.camera.updateMatrixWorld(),My.set(0,0,1),this.camera.localToWorld(My),My.sub(this.camera.position),Ey.setFromVector3(My),this._azimuthalAngle=Ey.theta}onPointerlockChange(){this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(wy),this.isLocked=!0):(this.dispatchEvent(Ty),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}}var Cy=n(16);const Ny=new p.a,Ly=new p.a;class Oy{constructor(t=new p.a,e=new p.a){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){Ny.subVectors(t,this.start),Ly.subVectors(this.end,this.start);const n=Ly.dot(Ly);let i=Ly.dot(Ny)/n;return e&&(i=On.d(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}const Py=new p.a;function Ry(t,e,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),l=Math.PI/4;Py.copy(e),Py[i]=0,Py.normalize();const c=.5*o/(o+a),h=1-Py.angleTo(t)/l;if(1===Math.sign(Py[n]))return h*c;return a/(o+a)+c+c*(1-h)}class Iy extends N{constructor(t=1,e=1,n=1,i=2,s=.1){if(i=2*i+1,s=Math.min(t/2,e/2,n/2,s),super(1,1,1,i,i,i),1===i)return;const r=this.toNonIndexed();this.index=null,this.attributes.position=r.attributes.position,this.attributes.normal=r.attributes.normal,this.attributes.uv=r.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(s),c=this.attributes.position.array,h=this.attributes.normal.array,u=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,r=0;ithis.object.position.y<-25,this.object=t.object,this.object.matrixAutoUpdate=!0,this.collider=t.collider,t.meshName&&(this._mesh=new B.a,this._mesh.geometry=Fy({radius:this.capsuleInfo.radius,height:1}),this._mesh.name=t.meshName,this._mesh.receiveShadow=!0,this._mesh.castShadow=!0)}setCollider(t){this.collider=t}setCapsule(t){this.capsuleInfo.radius=t.radius,this.capsuleInfo.segment.end.y=-t.height,this._mesh&&(this._mesh.geometry=Fy(t))}setUsePlayerMesh(t){t?(this._mesh=this._mesh||this._createMesh(),this.object.add(this._mesh)):this._mesh&&this.object.remove(this._mesh)}_createMesh(){const t=new B.a;return t.geometry=Fy({radius:this.capsuleInfo.radius,height:1}),t.name=this._meshName||\\\\\\\"defaultPlayerMeshName\\\\\\\",t.receiveShadow=!0,t.castShadow=!0,t}setMaterial(t){this._mesh&&(this._mesh.material=t)}reset(){this.stop(),this.object.position.copy(this.startPosition),Hy.copy(this.startRotation).multiplyScalar(On.a),this.object.rotation.setFromVector3(Hy)}stop(){this._pressed.forward=!1,this._pressed.backward=!1,this._pressed.left=!1,this._pressed.right=!1,this._running=!1}setResetRequiredCallback(t){this._resetRequiredCallback=t}setAzimuthalAngle(t){this._azimuthalAngle=t}update(t){const e=Math.min(t,.1);for(let t=0;tt.intersectsBox(Uy),intersectsTriangle:t=>{const e=zy,n=ky,s=t.closestPointToSegment(Vy,e,n);if(sMath.abs(t*this._velocity.y*.25);const o=Math.max(0,r.length()-1e-5);r.normalize().multiplyScalar(o),this.object.position.add(r),this._onGround?this._velocity.set(0,0,0):(r.normalize(),this._velocity.addScaledVector(r,-r.dot(this._velocity))),this._resetRequiredCallback()&&this.reset()}setForward(t){this._pressed.forward=t}setBackward(t){this._pressed.backward=t}setLeft(t){this._pressed.left=t}setRight(t){this._pressed.right=t}jump(){this._onGround&&this.jumpAllowed&&(this._velocity.y=this.jumpStrength)}setRun(t){t?this._onGround&&this.runAllowed&&(this._running=!0):this._running=!1}running(){return this._running}}function Wy(t){t.preventDefault()}class qy{constructor(t){this.player=t,this._bounds={keydown:this._onKeyDown.bind(this),keyup:this._onKeyUp.bind(this)}}_onKeyDown(t){if(!t.ctrlKey)switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!0),Wy(t);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!0),Wy(t);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!0),Wy(t);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!0),Wy(t);break;case\\\\\\\"Space\\\\\\\":this.player.jump(),Wy(t);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!0),Wy(t)}}_onKeyUp(t){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!1);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!1),Wy(t)}}addEvents(){document.addEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}removeEvents(){document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}}Xy=ua;var Xy;class Yy extends Va{static PARAM_CALLBACK_updateCollider(t){}}class $y{constructor(t){this.node=t}_colliderNodeGraphNode(){return this.__colliderNodeGraphNode=this.__colliderNodeGraphNode||new Mi(this.node.scene(),\\\\\\\"colliderGraphNode\\\\\\\")}async getCollider(){var t;const e=this.node.pv.colliderObject.nodeWithContext(ts.SOP);if(!e)return void this.node.states.error.set(\\\\\\\"collider node not found\\\\\\\");(null===(t=this._colliderNode)||void 0===t?void 0:t.graphNodeId())!=e.graphNodeId()&&(this._colliderNode&&this._colliderNodeGraphNode().removeGraphInput(this._colliderNode),this._colliderNodeGraphNode().addGraphInput(e),this._colliderNodeGraphNode().addPostDirtyHook(\\\\\\\"onColliderDirty\\\\\\\",(()=>{this.updateCollider()})),this._colliderNode=e);const n=(await e.compute()).coreContent();if(!n)return void this.node.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return n.objects()[0]}async updateCollider(){var t;const e=await this.getCollider();e?null===(t=this.node.player())||void 0===t||t.setCollider(e):this.node.states.error.set(\\\\\\\"invalid collider\\\\\\\")}}const Jy=\\\\\\\"lock\\\\\\\",Zy=\\\\\\\"change\\\\\\\",Qy=\\\\\\\"unlock\\\\\\\";function Ky(){return{cook:!1,callback:t=>{ex.PARAM_CALLBACK_updatePlayerParams(t)}}}const tx=new class extends ua{constructor(){super(...arguments),this.main=ha.FOLDER(),this.colliderObject=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},dependentOnFoundNode:!1,callback:t=>{ex.PARAM_CALLBACK_updateCollider(t)}}),this.lock=ha.BUTTON(null,{callback:t=>{ex.PARAM_CALLBACK_lockControls(t)}}),this.unlock=ha.BUTTON(null,{callback:t=>{ex.PARAM_CALLBACK_unlockControls(t)}}),this.capsuleRadius=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...Ky()}),this.capsuleHeight=ha.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...Ky()}),this.physics=ha.FOLDER(),this.physicsSteps=ha.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...Ky()}),this.gravity=ha.VECTOR3([0,-30,0],{...Ky()}),this.translateSpeed=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...Ky()}),this.rotateSpeed=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.jumpAllowed=ha.BOOLEAN(!0,{...Ky()}),this.jumpStrength=ha.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...Ky()}),this.runAllowed=ha.BOOLEAN(!0,{...Ky()}),this.runSpeedMult=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...Ky()}),this.updateCollider=ha.BUTTON(null,{callback:t=>{ex.PARAM_CALLBACK_updateCollider(t)}}),this.init=ha.FOLDER(),this.startPosition=ha.VECTOR3([0,2,0],{...Ky()}),this.startRotation=ha.VECTOR3([0,0,0],{...Ky()}),this.reset=ha.BUTTON(null,{callback:t=>{ex.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=ha.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=ha.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]})}};class ex extends Jv{constructor(){super(...arguments),this.paramsConfig=tx,this._controls_by_element_id=new Map}static type(){return _s.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}collisionController(){return this._collisionController=this._collisionController||new $y(this)}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(Jy,Ko.BASE,this.lockControls.bind(this)),new ta(ex.INPUT_UPDATE_COLLIDER,Ko.BASE,this._updateCollider.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(Jy,Ko.BASE),new ta(Zy,Ko.BASE),new ta(Qy,Ko.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new Sy(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}player(){return this._player}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.startRotation.copy(this.pv.startRotation),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this.collisionController().getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new jy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _updateCollider(){await this.collisionController().updateCollider()}_bind_listeners_to_controls_instance(t){t.addEventListener(Jy,(()=>{this.dispatchEventToOutput(Jy,{})})),t.addEventListener(Zy,(()=>{this.dispatchEventToOutput(Zy,{})})),t.addEventListener(Qy,(()=>{this.dispatchEventToOutput(Qy,{})}))}updateRequired(){return!0}setupControls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.rotateSpeed=this.pv.rotateSpeed}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}unlockControls(){const t=this._firstControls();t&&t.unlock()}lockControls(){const t=this._firstControls();if(t){if(this._player){this._corePlayerKeyEvents=this._corePlayerKeyEvents||new qy(this._player),this._corePlayerKeyEvents.addEvents();const e=()=>{var n,i;t&&t.domElement.ownerDocument.pointerLockElement!=t.domElement&&(t.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",e),null===(n=this._corePlayerKeyEvents)||void 0===n||n.removeEvents(),null===(i=this._player)||void 0===i||i.stop())};t.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",e)}t.lock()}}_firstControls(){let t;return this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t}static PARAM_CALLBACK_lockControls(t){t.lockControls()}static PARAM_CALLBACK_unlockControls(t){t.unlockControls()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var nx,ix;ex.INPUT_UPDATE_COLLIDER=\\\\\\\"updateCollider\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}(nx||(nx={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(ix||(ix={}));const sx=new class extends ua{constructor(){super(...arguments),this.maxCount=ha.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=ha.BUTTON(null,{callback:t=>{rx.PARAM_CALLBACK_reset(t)}})}};class rx extends Va{constructor(){super(...arguments),this.paramsConfig=sx,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(nx.TRIGGER,Ko.BASE,this.processEventTrigger.bind(this)),new ta(nx.RESET,Ko.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(ix.OUT,Ko.BASE),new ta(ix.LAST,Ko.BASE)])}processEvent(t){}processEventTrigger(t){this._process_count(t.preventDefault(),!1);class cx{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",lx)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",lx)}}const hx=100,ux=301,dx=302,px=303,_x=304,mx=306,fx=307,gx=1e3,vx=1001,yx=1002,xx=1003,bx=1004,wx=1005,Tx=1006,Ax=1007,Mx=1008,Ex=1009,Sx=1012,Cx=1014,Nx=1015,Lx=1016,Ox=1020,Px=1022,Rx=1023,Ix=1026,Fx=1027,Dx=2300,Bx=2301,zx=2302,kx=2400,Ux=2401,Gx=2402,Vx=2500,Hx=3e3,jx=3001,Wx=3007,qx=3002,Xx=7680,Yx=35044,$x=35048,Jx=\\\\\\\"300 es\\\\\\\";class Zx{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e>8&255]+eb[t>>16&255]+eb[t>>24&255]+\\\\\\\"-\\\\\\\"+eb[255&e]+eb[e>>8&255]+\\\\\\\"-\\\\\\\"+eb[e>>16&15|64]+eb[e>>24&255]+\\\\\\\"-\\\\\\\"+eb[63&n|128]+eb[n>>8&255]+\\\\\\\"-\\\\\\\"+eb[n>>16&255]+eb[n>>24&255]+eb[255&i]+eb[i>>8&255]+eb[i>>16&255]+eb[i>>24&255]).toUpperCase()}function sb(t,e,n){return Math.max(e,Math.min(n,t))}function rb(t,e){return(t%e+e)%e}function ob(t,e,n){return(1-n)*t+n*e}function ab(t){return 0==(t&t-1)&&0!==t}function lb(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function cb(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var hb=Object.freeze({__proto__:null,DEG2RAD:Kx,RAD2DEG:tb,generateUUID:ib,clamp:sb,euclideanModulo:rb,mapLinear:function(t,e,n,i,s){return i+(t-e)*(s-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:ob,damp:function(t,e,n,i){return ob(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(rb(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(Qx=t%2147483647),Qx=16807*Qx%2147483647,(Qx-1)/2147483646},degToRad:function(t){return t*Kx},radToDeg:function(t){return t*tb},isPowerOfTwo:ab,ceilPowerOfTwo:lb,floorPowerOfTwo:cb,setQuaternionFromProperEuler:function(t,e,n,i,s){const r=Math.cos,o=Math.sin,a=r(n/2),l=o(n/2),c=r((e+i)/2),h=o((e+i)/2),u=r((e-i)/2),d=o((e-i)/2),p=r((i-e)/2),_=o((i-e)/2);switch(s){case\\\\\\\"XYX\\\\\\\":t.set(a*h,l*u,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*h,l*u,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*u,l*d,a*h,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*h,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*h,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*h,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+s)}}});class ub{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),s=this.x-t.x,r=this.y-t.y;return this.x=s*n-r*i+t.x,this.y=s*i+r*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}ub.prototype.isVector2=!0;class db{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function pb(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;ne&&(e=t[n]);return e}db.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function _b(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let mb;class fb{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===mb&&(mb=_b(\\\\\\\"canvas\\\\\\\")),mb.width=t.width,mb.height=t.height;const n=mb.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=mb}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let gb=0;class vb extends Zx{constructor(t=vb.DEFAULT_IMAGE,e=vb.DEFAULT_MAPPING,n=1001,i=1001,s=1006,r=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:gb++}),this.uuid=ib(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=s,this.minFilter=r,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new ub(0,0),this.repeat=new ub(1,1),this.center=new ub(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new db,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=ib()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t1)switch(this.wrapS){case gx:t.x=t.x-Math.floor(t.x);break;case vx:t.x=t.x<0?0:1;break;case yx:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case gx:t.y=t.y-Math.floor(t.y);break;case vx:t.y=t.y<0?0:1;break;case yx:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function yb(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?fb.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}vb.DEFAULT_IMAGE=void 0,vb.DEFAULT_MAPPING=300,vb.prototype.isTexture=!0;class xb{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(sb(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Tb.prototype.isQuaternion=!0;class Ab{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(Eb.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Eb.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[3]*n+s[6]*i,this.y=s[1]*e+s[4]*n+s[7]*i,this.z=s[2]*e+s[5]*n+s[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=t.elements,r=1/(s[3]*e+s[7]*n+s[11]*i+s[15]);return this.x=(s[0]*e+s[4]*n+s[8]*i+s[12])*r,this.y=(s[1]*e+s[5]*n+s[9]*i+s[13])*r,this.z=(s[2]*e+s[6]*n+s[10]*i+s[14])*r,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,s=t.x,r=t.y,o=t.z,a=t.w,l=a*e+r*i-o*n,c=a*n+o*e-s*i,h=a*i+s*n-r*e,u=-s*e-r*n-o*i;return this.x=l*a+u*-s+c*-o-h*-r,this.y=c*a+u*-r+h*-s-l*-o,this.z=h*a+u*-o+l*-r-c*-s,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i,this.y=s[1]*e+s[5]*n+s[9]*i,this.z=s[2]*e+s[6]*n+s[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return Mb.copy(this).projectOnVector(t),this.sub(Mb)}reflect(t){return this.sub(Mb.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(sb(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}Ab.prototype.isVector3=!0;const Mb=new Ab,Eb=new Tb;class Sb{constructor(t=new Ab(1/0,1/0,1/0),e=new Ab(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.length;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.count;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Nb),Nb.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Bb),zb.subVectors(this.max,Bb),Ob.subVectors(t.a,Bb),Pb.subVectors(t.b,Bb),Rb.subVectors(t.c,Bb),Ib.subVectors(Pb,Ob),Fb.subVectors(Rb,Pb),Db.subVectors(Ob,Rb);let e=[0,-Ib.z,Ib.y,0,-Fb.z,Fb.y,0,-Db.z,Db.y,Ib.z,0,-Ib.x,Fb.z,0,-Fb.x,Db.z,0,-Db.x,-Ib.y,Ib.x,0,-Fb.y,Fb.x,0,-Db.y,Db.x,0];return!!Gb(e,Ob,Pb,Rb,zb)&&(e=[1,0,0,0,1,0,0,0,1],!!Gb(e,Ob,Pb,Rb,zb)&&(kb.crossVectors(Ib,Fb),e=[kb.x,kb.y,kb.z],Gb(e,Ob,Pb,Rb,zb)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Nb.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Nb).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(Cb[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Cb[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Cb[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Cb[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Cb[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Cb[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Cb[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Cb[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Cb)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Sb.prototype.isBox3=!0;const Cb=[new Ab,new Ab,new Ab,new Ab,new Ab,new Ab,new Ab,new Ab],Nb=new Ab,Lb=new Sb,Ob=new Ab,Pb=new Ab,Rb=new Ab,Ib=new Ab,Fb=new Ab,Db=new Ab,Bb=new Ab,zb=new Ab,kb=new Ab,Ub=new Ab;function Gb(t,e,n,i,s){for(let r=0,o=t.length-3;r<=o;r+=3){Ub.fromArray(t,r);const o=s.x*Math.abs(Ub.x)+s.y*Math.abs(Ub.y)+s.z*Math.abs(Ub.z),a=e.dot(Ub),l=n.dot(Ub),c=i.dot(Ub);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Vb=new Sb,Hb=new Ab,jb=new Ab,Wb=new Ab;class qb{constructor(t=new Ab,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Vb.setFromPoints(t).getCenter(n);let i=0;for(let e=0,s=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Wb.subVectors(t,this.center);const e=Wb.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Wb.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return jb.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Hb.copy(t.center).add(jb)),this.expandByPoint(Hb.copy(t.center).sub(jb)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Xb=new Ab,Yb=new Ab,$b=new Ab,Jb=new Ab,Zb=new Ab,Qb=new Ab,Kb=new Ab;class tw{constructor(t=new Ab,e=new Ab(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Xb)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Xb.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Xb.copy(this.direction).multiplyScalar(e).add(this.origin),Xb.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Yb.copy(t).add(e).multiplyScalar(.5),$b.copy(e).sub(t).normalize(),Jb.copy(this.origin).sub(Yb);const s=.5*t.distanceTo(e),r=-this.direction.dot($b),o=Jb.dot(this.direction),a=-Jb.dot($b),l=Jb.lengthSq(),c=Math.abs(1-r*r);let h,u,d,p;if(c>0)if(h=r*a-o,u=r*o-a,p=s*c,h>=0)if(u>=-p)if(u<=p){const t=1/c;h*=t,u*=t,d=h*(h+r*u+2*o)+u*(r*h+u+2*a)+l}else u=s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u=-s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u<=-p?(h=Math.max(0,-(-r*s+o)),u=h>0?-s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l):u<=p?(h=0,u=Math.min(Math.max(-s,-a),s),d=u*(u+2*a)+l):(h=Math.max(0,-(r*s+o)),u=h>0?s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l);else u=r>0?-s:s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(h).add(this.origin),i&&i.copy($b).multiplyScalar(u).add(Yb),d}intersectSphere(t,e){Xb.subVectors(t.center,this.origin);const n=Xb.dot(this.direction),i=Xb.dot(Xb)-n*n,s=t.radius*t.radius;if(i>s)return null;const r=Math.sqrt(s-i),o=n-r,a=n+r;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Xb)}intersectTriangle(t,e,n,i,s){Zb.subVectors(e,t),Qb.subVectors(n,t),Kb.crossVectors(Zb,Qb);let r,o=this.direction.dot(Kb);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}Jb.subVectors(this.origin,t);const a=r*this.direction.dot(Qb.crossVectors(Jb,Qb));if(a<0)return null;const l=r*this.direction.dot(Zb.cross(Jb));if(l<0)return null;if(a+l>o)return null;const c=-r*Jb.dot(Kb);return c<0?null:this.at(c/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ew{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new ew).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/nw.setFromMatrixColumn(t,0).length(),s=1/nw.setFromMatrixColumn(t,1).length(),r=1/nw.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*s,e[5]=n[5]*s,e[6]=n[6]*s,e[7]=0,e[8]=n[8]*r,e[9]=n[9]*r,e[10]=n[10]*r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,s=t.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(s),h=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(sw,t,rw)}lookAt(t,e,n){const i=this.elements;return lw.subVectors(t,e),0===lw.lengthSq()&&(lw.z=1),lw.normalize(),ow.crossVectors(n,lw),0===ow.lengthSq()&&(1===Math.abs(n.z)?lw.x+=1e-4:lw.z+=1e-4,lw.normalize(),ow.crossVectors(n,lw)),ow.normalize(),aw.crossVectors(lw,ow),i[0]=ow.x,i[4]=aw.x,i[8]=lw.x,i[1]=ow.y,i[5]=aw.y,i[9]=lw.y,i[2]=ow.z,i[6]=aw.z,i[10]=lw.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=nw.set(i[0],i[1],i[2]).length();const r=nw.set(i[4],i[5],i[6]).length(),o=nw.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],iw.copy(this);const a=1/s,l=1/r,c=1/o;return iw.elements[0]*=a,iw.elements[1]*=a,iw.elements[2]*=a,iw.elements[4]*=l,iw.elements[5]*=l,iw.elements[6]*=l,iw.elements[8]*=c,iw.elements[9]*=c,iw.elements[10]*=c,e.setFromRotationMatrix(iw),n.x=s,n.y=r,n.z=o,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}ew.prototype.isMatrix4=!0;const nw=new Ab,iw=new ew,sw=new Ab(0,0,0),rw=new Ab(1,1,1),ow=new Ab,aw=new Ab,lw=new Ab,cw=new ew,hw=new Tb;class uw{constructor(t=0,e=0,n=0,i=uw.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,s=i[0],r=i[4],o=i[8],a=i[1],l=i[5],c=i[9],h=i[2],u=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(sb(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(u,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-sb(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-h,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(sb(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(-h,d),this._z=Math.atan2(-r,l)):(this._y=0,this._z=Math.atan2(a,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-sb(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(u,d),this._z=Math.atan2(a,s)):(this._x=0,this._z=Math.atan2(-r,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(sb(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-h,s)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-sb(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(u,l),this._y=Math.atan2(o,s)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return cw.makeRotationFromQuaternion(t),this.setFromRotationMatrix(cw,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return hw.setFromEuler(this),this.setFromQuaternion(hw,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new Ab(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}uw.prototype.isEuler=!0,uw.DefaultOrder=\\\\\\\"XYZ\\\\\\\",uw.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class dw{constructor(){this.mask=1}set(t){this.mask=1<1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,s){Sw.subVectors(i,e),Cw.subVectors(n,e),Nw.subVectors(t,e);const r=Sw.dot(Sw),o=Sw.dot(Cw),a=Sw.dot(Nw),l=Cw.dot(Cw),c=Cw.dot(Nw),h=r*l-o*o;if(0===h)return s.set(-2,-1,-1);const u=1/h,d=(l*a-o*c)*u,p=(r*c-o*a)*u;return s.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Lw),Lw.x>=0&&Lw.y>=0&&Lw.x+Lw.y<=1}static getUV(t,e,n,i,s,r,o,a){return this.getBarycoord(t,e,n,i,Lw),a.set(0,0),a.addScaledVector(s,Lw.x),a.addScaledVector(r,Lw.y),a.addScaledVector(o,Lw.z),a}static isFrontFacing(t,e,n,i){return Sw.subVectors(n,e),Cw.subVectors(t,e),Sw.cross(Cw).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return Sw.subVectors(this.c,this.b),Cw.subVectors(this.a,this.b),.5*Sw.cross(Cw).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Bw.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Bw.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return Bw.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return Bw.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Bw.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;Ow.subVectors(i,n),Pw.subVectors(s,n),Iw.subVectors(t,n);const a=Ow.dot(Iw),l=Pw.dot(Iw);if(a<=0&&l<=0)return e.copy(n);Fw.subVectors(t,i);const c=Ow.dot(Fw),h=Pw.dot(Fw);if(c>=0&&h<=c)return e.copy(i);const u=a*h-c*l;if(u<=0&&a>=0&&c<=0)return r=a/(a-c),e.copy(n).addScaledVector(Ow,r);Dw.subVectors(t,s);const d=Ow.dot(Dw),p=Pw.dot(Dw);if(p>=0&&d<=p)return e.copy(s);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(Pw,o);const m=c*p-d*h;if(m<=0&&h-c>=0&&d-p>=0)return Rw.subVectors(s,i),o=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Rw,o);const f=1/(m+_+u);return r=_*f,o=u*f,e.copy(n).addScaledVector(Ow,r).addScaledVector(Pw,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let zw=0;class kw extends Zx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:zw++}),this.uuid=ib(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=Rx,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=hx,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Xx,this.stencilZFail=Xx,this.stencilZPass=Xx,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==Rx&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}kw.prototype.isMaterial=!0;const Uw={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},Gw={h:0,s:0,l:0},Vw={h:0,s:0,l:0};function Hw(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function jw(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Ww(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class qw{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=rb(t,1),e=sb(e,0,1),n=sb(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=Hw(s,i,t+1/3),this.g=Hw(s,i,t),this.b=Hw(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Uw[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=jw(t.r),this.g=jw(t.g),this.b=jw(t.b),this}copyLinearToSRGB(t){return this.r=Ww(t.r),this.g=Ww(t.g),this.b=Ww(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?Qw:Zw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new db).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return eT.makeRotationFromQuaternion(t),this.applyMatrix4(eT),this}rotateX(t){return eT.makeRotationX(t),this.applyMatrix4(eT),this}rotateY(t){return eT.makeRotationY(t),this.applyMatrix4(eT),this}rotateZ(t){return eT.makeRotationZ(t),this.applyMatrix4(eT),this}translate(t,e,n){return eT.makeTranslation(t,e,n),this.applyMatrix4(eT),this}scale(t,e,n){return eT.makeScale(t,e,n),this.applyMatrix4(eT),this}lookAt(t){return nT.lookAt(t),nT.updateMatrix(),this.applyMatrix4(nT.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(iT).negate(),this.translate(iT.x,iT.y,iT.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),hT.copy(n.boundingSphere),hT.applyMatrix4(s),!1===t.ray.intersectsSphere(hT))return;if(lT.copy(s).invert(),cT.copy(t.ray).applyMatrix4(lT),null!==n.boundingBox&&!1===cT.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:AT.clone(),object:t}}(t,e,n,i,uT,dT,pT,TT);if(p){a&&(xT.fromBufferAttribute(a,c),bT.fromBufferAttribute(a,h),wT.fromBufferAttribute(a,u),p.uv=Bw.getUV(TT,uT,dT,pT,xT,bT,wT,new ub)),l&&(xT.fromBufferAttribute(l,c),bT.fromBufferAttribute(l,h),wT.fromBufferAttribute(l,u),p.uv2=Bw.getUV(TT,uT,dT,pT,xT,bT,wT,new ub));const t={a:c,b:h,c:u,normal:new Ab,materialIndex:0};Bw.getNormal(uT,dT,pT,t.normal),p.face=t}return p}MT.prototype.isMesh=!0;class ST extends aT{constructor(t=1,e=1,n=1,i=1,s=1,r=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:s,depthSegments:r};const o=this;i=Math.floor(i),s=Math.floor(s),r=Math.floor(r);const a=[],l=[],c=[],h=[];let u=0,d=0;function p(t,e,n,i,s,r,p,_,m,f,g){const v=r/m,y=p/f,x=r/2,b=p/2,w=_/2,T=m+1,A=f+1;let M=0,E=0;const S=new Ab;for(let r=0;r0?1:-1,c.push(S.x,S.y,S.z),h.push(a/m),h.push(1-r/f),M+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}OT.prototype.isShaderMaterial=!0;class PT extends Ew{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ew,this.projectionMatrix=new ew,this.projectionMatrixInverse=new ew}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}PT.prototype.isCamera=!0;class RT extends PT{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*tb*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*Kx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*tb*Math.atan(Math.tan(.5*Kx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,s,r){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*Kx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,s=-.5*i;const r=this.view;if(null!==this.view&&this.view.enabled){const t=r.fullWidth,o=r.fullHeight;s+=r.offsetX*i/t,e-=r.offsetY*n/o,i*=r.width/t,n*=r.height/o}const o=this.filmOffset;0!==o&&(s+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}RT.prototype.isPerspectiveCamera=!0;const IT=90;class FT extends Ew{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new RT(IT,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Ab(1,0,0)),this.add(i);const s=new RT(IT,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new Ab(-1,0,0)),this.add(s);const r=new RT(IT,1,t,e);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new Ab(0,1,0)),this.add(r);const o=new RT(IT,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new Ab(0,-1,0)),this.add(o);const a=new RT(IT,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new Ab(0,0,1)),this.add(a);const l=new RT(IT,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Ab(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,s,r,o,a,l]=this.children,c=t.xr.enabled,h=t.getRenderTarget();t.xr.enabled=!1;const u=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,s),t.setRenderTarget(n,2),t.render(e,r),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=u,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(h),t.xr.enabled=c}}class DT extends vb{constructor(t,e,n,i,s,r,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:ux,n,i,s,r,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}DT.prototype.isCubeTexture=!0;class BT extends bb{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new DT(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:Tx,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=Rx,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new ST(5,5,5),s=new OT({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:CT(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});s.uniforms.tEquirect.value=e;const r=new MT(i,s),o=e.minFilter;e.minFilter===Mx&&(e.minFilter=Tx);return new FT(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}BT.prototype.isWebGLCubeRenderTarget=!0;const zT=new Ab,kT=new Ab,UT=new db;class GT{constructor(t=new Ab(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=zT.subVectors(n,e).cross(kT.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(zT),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const s=-(t.start.dot(this.normal)+this.constant)/i;return s<0||s>1?null:e.copy(n).multiplyScalar(s).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||UT.getNormalMatrix(t),i=this.coplanarPoint(zT).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}GT.prototype.isPlane=!0;const VT=new qb,HT=new Ab;class jT{constructor(t=new GT,e=new GT,n=new GT,i=new GT,s=new GT,r=new GT){this.planes=[t,e,n,i,s,r]}set(t,e,n,i,s,r){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],s=n[1],r=n[2],o=n[3],a=n[4],l=n[5],c=n[6],h=n[7],u=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,h-a,_-u,v-m).normalize(),e[1].setComponents(o+i,h+a,_+u,v+m).normalize(),e[2].setComponents(o+s,h+l,_+d,v+f).normalize(),e[3].setComponents(o-s,h-l,_-d,v-f).normalize(),e[4].setComponents(o-r,h-c,_-p,v-g).normalize(),e[5].setComponents(o+r,h+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),VT.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(VT)}intersectsSprite(t){return VT.center.set(0,0,0),VT.radius=.7071067811865476,VT.applyMatrix4(t.matrixWorld),this.intersectsSphere(VT)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)0?t.max.x:t.min.x,HT.y=i.normal.y>0?t.max.y:t.min.y,HT.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(HT)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function WT(){let t=null,e=!1,n=null,i=null;function s(e,r){n(e,r),i=t.requestAnimationFrame(s)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(s),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function qT(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,s){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n}\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n}\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\treturn sheenTint * ( D * V );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float vFogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\treturn irradiance;\\\\n}\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\t#endif\\\\n}\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n#ifdef IOR\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n};\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\treturn fab;\\\\n}\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n}\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normal_pars_fragment:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_pars_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",output_fragment:\\\\\\\"#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\t}\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include \\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",depth_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main () {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshnormal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\"},$T={common:{diffuse:{value:new qw(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new db},uv2Transform:{value:new db},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new ub(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new qw(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new qw(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new db}},sprite:{diffuse:{value:new qw(16777215)},opacity:{value:1},center:{value:new ub(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new db}}},JT={basic:{uniforms:NT([$T.common,$T.specularmap,$T.envmap,$T.aomap,$T.lightmap,$T.fog]),vertexShader:YT.meshbasic_vert,fragmentShader:YT.meshbasic_frag},lambert:{uniforms:NT([$T.common,$T.specularmap,$T.envmap,$T.aomap,$T.lightmap,$T.emissivemap,$T.fog,$T.lights,{emissive:{value:new qw(0)}}]),vertexShader:YT.meshlambert_vert,fragmentShader:YT.meshlambert_frag},phong:{uniforms:NT([$T.common,$T.specularmap,$T.envmap,$T.aomap,$T.lightmap,$T.emissivemap,$T.bumpmap,$T.normalmap,$T.displacementmap,$T.fog,$T.lights,{emissive:{value:new qw(0)},specular:{value:new qw(1118481)},shininess:{value:30}}]),vertexShader:YT.meshphong_vert,fragmentShader:YT.meshphong_frag},standard:{uniforms:NT([$T.common,$T.envmap,$T.aomap,$T.lightmap,$T.emissivemap,$T.bumpmap,$T.normalmap,$T.displacementmap,$T.roughnessmap,$T.metalnessmap,$T.fog,$T.lights,{emissive:{value:new qw(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:YT.meshphysical_vert,fragmentShader:YT.meshphysical_frag},toon:{uniforms:NT([$T.common,$T.aomap,$T.lightmap,$T.emissivemap,$T.bumpmap,$T.normalmap,$T.displacementmap,$T.gradientmap,$T.fog,$T.lights,{emissive:{value:new qw(0)}}]),vertexShader:YT.meshtoon_vert,fragmentShader:YT.meshtoon_frag},matcap:{uniforms:NT([$T.common,$T.bumpmap,$T.normalmap,$T.displacementmap,$T.fog,{matcap:{value:null}}]),vertexShader:YT.meshmatcap_vert,fragmentShader:YT.meshmatcap_frag},points:{uniforms:NT([$T.points,$T.fog]),vertexShader:YT.points_vert,fragmentShader:YT.points_frag},dashed:{uniforms:NT([$T.common,$T.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:YT.linedashed_vert,fragmentShader:YT.linedashed_frag},depth:{uniforms:NT([$T.common,$T.displacementmap]),vertexShader:YT.depth_vert,fragmentShader:YT.depth_frag},normal:{uniforms:NT([$T.common,$T.bumpmap,$T.normalmap,$T.displacementmap,{opacity:{value:1}}]),vertexShader:YT.meshnormal_vert,fragmentShader:YT.meshnormal_frag},sprite:{uniforms:NT([$T.sprite,$T.fog]),vertexShader:YT.sprite_vert,fragmentShader:YT.sprite_frag},background:{uniforms:{uvTransform:{value:new db},t2D:{value:null}},vertexShader:YT.background_vert,fragmentShader:YT.background_frag},cube:{uniforms:NT([$T.envmap,{opacity:{value:1}}]),vertexShader:YT.cube_vert,fragmentShader:YT.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:YT.equirect_vert,fragmentShader:YT.equirect_frag},distanceRGBA:{uniforms:NT([$T.common,$T.displacementmap,{referencePosition:{value:new Ab},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:YT.distanceRGBA_vert,fragmentShader:YT.distanceRGBA_frag},shadow:{uniforms:NT([$T.lights,$T.fog,{color:{value:new qw(0)},opacity:{value:1}}]),vertexShader:YT.shadow_vert,fragmentShader:YT.shadow_frag}};function ZT(t,e,n,i,s){const r=new qw(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===mx)?(void 0===a&&(a=new MT(new ST(1,1,1),new OT({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:CT(JT.cube.uniforms),vertexShader:JT.cube.vertexShader,fragmentShader:JT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new MT(new XT(2,2),new OT({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:CT(JT.background.uniforms),vertexShader:JT.background.vertexShader,fragmentShader:JT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function QT(t,e,n,i){const s=t.getParameter(34921),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(34930),u=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(36183):0}}function eA(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new GT,a=new db,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length0){const o=t.getRenderTarget(),a=new BT(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}JT.physical={uniforms:NT([JT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new ub(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new qw(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new ub},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new qw(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new qw(1,1,1)},specularTintMap:{value:null}}]),vertexShader:YT.meshphysical_vert,fragmentShader:YT.meshphysical_frag};class iA extends PT{constructor(t=-1,e=1,n=1,i=-1,s=.1,r=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=s,this.far=r,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,s,r){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let s=n-t,r=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;s+=t*this.view.offsetX,r=s+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(s,r,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}iA.prototype.isOrthographicCamera=!0;class sA extends OT{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}sA.prototype.isRawShaderMaterial=!0;const rA=Math.pow(2,8),oA=[.125,.215,.35,.446,.526,.582],aA=5+oA.length,lA=20,cA={[Hx]:0,[jx]:1,[qx]:2,3004:3,3005:4,3006:5,[Wx]:6},hA=new iA,{_lodPlanes:uA,_sizeLods:dA,_sigmas:pA}=bA(),_A=new qw;let mA=null;const fA=(1+Math.sqrt(5))/2,gA=1/fA,vA=[new Ab(1,1,1),new Ab(-1,1,1),new Ab(1,1,-1),new Ab(-1,1,-1),new Ab(0,fA,gA),new Ab(0,fA,-gA),new Ab(gA,0,fA),new Ab(-gA,0,fA),new Ab(fA,gA,0),new Ab(-fA,gA,0)];class yA{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Ab(0,1,0);return new sA({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:cA[3e3]},outputEncoding:{value:cA[3e3]}},vertexShader:EA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${SA()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(lA),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){mA=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=MA(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=AA(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?rA:0,rA,rA),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===Rx&&e.type===Ex&&e.encoding===jx?t.value=cA[3e3]:t.value=cA[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=MA()):null==this._equirectShader&&(this._equirectShader=AA());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new MT(uA[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),TA(e,0,0,3*rA,2*rA),n.setRenderTarget(e),n.render(s,hA)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;elA&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,hA)}}function xA(t){return void 0!==t&&t.type===Ex&&(t.encoding===Hx||t.encoding===jx||t.encoding===Wx)}function bA(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=oA[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new aT;y.setAttribute(\\\\\\\"position\\\\\\\",new Jw(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new Jw(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new Jw(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function wA(t){const e=new bb(3*rA,3*rA,t);return e.texture.mapping=mx,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function TA(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function AA(){const t=new ub(1,1);return new sA({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:cA[3e3]},outputEncoding:{value:cA[3e3]}},vertexShader:EA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${SA()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function MA(){return new sA({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:cA[3e3]},outputEncoding:{value:cA[3e3]}},vertexShader:EA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${SA()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function EA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function SA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function CA(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===px||r===_x,a=r===ux||r===dx;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?Qw:Zw)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[e.id]=!0,n.memory.geometries++),e},update:function(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const i=t.morphAttributes;for(const t in i){const n=i[t];for(let t=0,i=n.length;te.maxTextureSize&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const d=new Float32Array(h*u*4*i),p=new RA(d,h,u,i);p.format=Rx,p.type=Nx;const _=4*l;for(let e=0;e0)return t;const s=e*n;let r=jA[s];if(void 0===r&&(r=new Float32Array(s),jA[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function JA(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function KM(t){return t.replace(QM,tE)}function tE(t,e){const n=YT[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return KM(n)}const eE=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,nE=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function iE(t){return t.replace(nE,rE).replace(eE,sE)}function sE(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. 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i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter($M).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter($M).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[oE(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define 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y=v+g+a,x=VM(s,35633,v+f+o),b=VM(s,35632,y);if(s.attachShader(m,x),s.attachShader(m,b),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(x).trim(),n=s.getShaderInfoLog(b).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,35714)){i=!1;const e=WM(s,x,\\\\\\\"vertex\\\\\\\"),n=WM(s,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return s.deleteShader(x),s.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new GM(s,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let s=0;s0,C=r.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:r.type,vertexShader:A,fragmentShader:M,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==E?f(E.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===mx||b.mapping===fx),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:1===r.normalMapType,tangentSpaceNormalMap:0===r.normalMapType,clearcoat:C,clearcoatMap:C&&!!r.clearcoatMap,clearcoatRoughnessMap:C&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:S,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:h,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:2===r.side,flipSided:1===r.side,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const 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Ab,halfHeight:new Ab}}return t[e.id]=n,n}}}let mE=0;function fE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function gE(t,e){const n=new _E,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new ub};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new ub,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new Ab);const r=new Ab,o=new ew,a=new ew;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(fE);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=$T.LTC_FLOAT_1,s.rectAreaLTC2=$T.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=$T.LTC_HALF_1,s.rectAreaLTC2=$T.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=mE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new vE(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class xE extends kw{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}xE.prototype.isMeshDepthMaterial=!0;class bE extends kw{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Ab,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}bE.prototype.isMeshDistanceMaterial=!0;function wE(t,e,n){let i=new jT;const s=new ub,r=new ub,o=new xb,a=new xE({depthPacking:3201}),l=new bE,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new OT({uniforms:{shadow_pass:{value:null},resolution:{value:new ub},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include \\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const _=new aT;_.setAttribute(\\\\\\\"position\\\\\\\",new Jw(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new MT(_,d),f=this;function g(n,i){const s=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,d.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,p,m,null)}function v(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===h?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:u[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function y(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&3===this.type){const t={minFilter:Tx,magFilter:Tx,format:Rx};u.map=new bb(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new bb(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:xx,magFilter:xx,format:Rx};u.map=new bb(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),y(n,a,u.camera,c,this.type)}u.isPointLightShadow||3!==this.type||g(u,a),u.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function TE(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new xb;let i=null;const s=new xb(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(t){t?B(2929):z(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?B(2960):z(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,_=null,m=null,f=null,g=null,v=null,y=!1,x=null,b=null,w=null,T=null,A=null;const M=t.getParameter(35661);let E=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),E=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),E=S>=2);let N=null,L={};const O=t.getParameter(3088),P=t.getParameter(2978),R=(new xb).fromArray(O),I=(new xb).fromArray(P);function F(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?cb:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+r+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return t}function g(t){return ab(t.width)&&ab(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==xx&&t.minFilter!==Tx}function y(e,n,s,r,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(s,r,o))}function x(n,i,s,r){if(!1===a)return i;if(null!==n){if(void 0!==t[n])return t[n];console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\"+n+\\\\\\\"'\\\\\\\")}let o=i;return 6403===i&&(5126===s&&(o=33326),5131===s&&(o=33325),5121===s&&(o=33321)),6407===i&&(5126===s&&(o=34837),5131===s&&(o=34843),5121===s&&(o=32849)),6408===i&&(5126===s&&(o=34836),5131===s&&(o=34842),5121===s&&(o=r===jx?35907:32856)),33325!==o&&33326!==o&&34842!==o&&34836!==o||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(t){return t===xx||t===bx||t===wx?9728:9729}function w(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",w),function(e){const n=i.get(e);if(void 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e=0,s=n.length;e0&&s.__version!==t.version){const n=t.image;if(void 0===n)console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");else{if(!1!==n.complete)return void O(s,t,e);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(33984+e),n.bindTexture(3553,s.__webglTexture)}function E(e,s){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,s){if(6!==i.image.length)return;L(e,i),n.activeTexture(33984+s),n.bindTexture(34067,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const o=i&&(i.isCompressedTexture||i.image[0].isCompressedTexture),l=i.image[0]&&i.image[0].isDataTexture,h=[];for(let t=0;t<6;t++)h[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const u=h[0],d=g(u)||a,p=r.convert(i.format),_=r.convert(i.type),m=x(i.internalFormat,p,_,i.encoding);let b;if(N(34067,i,d),o){for(let t=0;t<6;t++){b=h[t].mipmaps;for(let e=0;e1||i.get(r).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(r.anisotropy,s.getMaxAnisotropy())),i.get(r).__currentAnisotropy=r.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",w),e.__webglTexture=t.createTexture(),o.memory.textures++)}function O(e,i,s){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+s),n.bindTexture(o,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=function(t){return!a&&(t.wrapS!==vx||t.wrapT!==vx||t.minFilter!==xx&&t.minFilter!==Tx)}(i)&&!1===g(i.image),c=f(i.image,l,!1,h),u=g(c)||a,d=r.convert(i.format);let p,_=r.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,u);const 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s=i.get(t);t.version>0&&s.__version!==t.version?O(s,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,s.__webglTexture))},this.setTexture3D=function(t,e){const s=i.get(t);t.version>0&&s.__version!==t.version?O(s,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,s.__webglTexture))},this.setTextureCube=E,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),h=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(h.__webglTexture=t.createTexture(),h.__version=l.version,o.memory.textures++);const u=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==Px||l.type!==Nx&&l.type!==Lx||(l.format=Rx,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),u){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(s.drawBuffers){const n=e.texture;for(let e=0,s=n.length;ea+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(s=e.getPose(t.gripSpace,n),null!==s&&(a.matrix.fromArray(s.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),s.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(s.linearVelocity)):a.hasLinearVelocity=!1,s.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(s.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==s),null!==l&&(l.visible=null!==r),this}}class LE extends Zx{constructor(t,e){super();const n=this,i=t.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new RT;w.layers.enable(1),w.viewport=new xb;const T=new RT;T.layers.enable(2),T.viewport=new xb;const A=[w,T],M=new EE;M.layers.enable(1),M.layers.enable(2);let E=null,S=null;function C(t){const e=b.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function N(){b.forEach((function(t,e){t.disconnect(e)})),b.clear(),E=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),h=null,_=null,m=null,f=null,d=null,u=null,c=null,s=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=s.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const s=t.get(i).__maxMipLevel;void 0!==s&&(e.maxMipLevel.value=s)}let s,r;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap?s=n.clearcoatRoughnessMap:n.specularIntensityMap?s=n.specularIntensityMap:n.specularTintMap?s=n.specularTintMap:n.transmissionMap?s=n.transmissionMap:n.thicknessMap&&(s=n.thicknessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function PE(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=_b(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=null,d=null;const p=[],_=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=Hx,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const m=this;let f=!1,g=0,v=0,y=null,x=-1,b=null;const w=new xb,T=new xb;let A=null,M=e.width,E=e.height,S=1,C=null,N=null;const L=new xb(0,0,M,E),O=new xb(0,0,M,E);let P=!1;const R=[],I=new jT;let F=!1,D=!1,B=null;const z=new ew,k=new Ab,U={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function G(){return null===y?S:1}let V,H,j,W,q,X,Y,$,J,Z,Q,K,tt,et,nt,it,st,rt,ot,at,lt,ct,ht,ut=n;function dt(t,n){for(let i=0;i0&&function(t,e,n){if(null===B){const t=!0===o&&!0===H.isWebGL2;B=new(t?wb:bb)(1024,1024,{generateMipmaps:!0,type:null!==ct.convert(Lx)?Lx:Ex,minFilter:Mx,magFilter:xx,wrapS:vx,wrapT:vx})}const i=m.getRenderTarget();m.setRenderTarget(B),m.clear();const s=m.toneMapping;m.toneMapping=0,At(t,e,n),m.toneMapping=s,X.updateMultisampleRenderTarget(B),X.updateRenderTargetMipmap(B),m.setRenderTarget(i)}(s,e,n),i&&j.viewport(w.copy(i)),s.length>0&&At(s,e,n),r.length>0&&At(r,e,n),a.length>0&&At(a,e,n)}function At(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?_[_.length-1]:null,p.pop(),u=p.length>0?p[p.length-1]:null},this.getActiveCubeFace=function(){return g},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTarget=function(t,e=0,n=0){y=t,g=e,v=n,t&&void 0===q.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=P;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&ut.readPixels(e,n,i,s,ct.convert(a),ct.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==y?q.get(y).__webglFramebuffer:null;j.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),s=Math.floor(e.image.width*i),r=Math.floor(e.image.height*i);let o=ct.convert(e.format);H.isWebGL2&&(6407===o&&(o=32849),6408===o&&(o=32856)),X.setTexture2D(e,0),ut.copyTexImage2D(3553,n,o,t.x,t.y,s,r,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ut.pixelStorei(37440,n.flipY),ut.pixelStorei(37441,n.premultiplyAlpha),ut.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ut.texSubImage2D(3553,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?ut.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ut.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ut.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let h;if(i.isDataTexture3D)X.setTexture3D(i,0),h=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports 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srcTexture.\\\\\\\"),ut.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,g.data)):ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g),ut.pixelStorei(3314,u),ut.pixelStorei(32878,d),ut.pixelStorei(3316,p),ut.pixelStorei(3315,_),ut.pixelStorei(32877,f),0===s&&i.generateMipmaps&&ut.generateMipmap(h),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ht.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends PE{}).prototype.isWebGL1Renderer=!0;class RE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new qw(t),this.density=e}clone(){return new RE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}RE.prototype.isFogExp2=!0;class IE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new qw(t),this.near=e,this.far=n}clone(){return new IE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}IE.prototype.isFog=!0;class FE extends Ew{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}FE.prototype.isScene=!0;class DE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Yx,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=ib()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,s=this.stride;it.far||e.push({distance:a,point:GE.clone(),uv:Bw.getUV(GE,XE,YE,$E,JE,ZE,QE,new ub),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function tS(t,e,n,i,s,r){jE.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(WE.x=r*jE.x-s*jE.y,WE.y=s*jE.x+r*jE.y):WE.copy(jE),t.copy(e),t.x+=WE.x,t.y+=WE.y,t.applyMatrix4(qE)}KE.prototype.isSprite=!0;const eS=new Ab,nS=new xb,iS=new xb,sS=new Ab,rS=new ew;class oS extends MT{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ew,this.bindMatrixInverse=new ew}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new xb,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;u.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(u);dt.far||e.push({distance:d,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;na)continue;u.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(u);it.far||e.push({distance:i,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}bS.prototype.isLine=!0;const wS=new Ab,TS=new Ab;class AS extends bS{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function PS(t,e,n,i,s,r,o){const a=CS.distanceSqToPoint(t);if(as.far)return;r.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}OS.prototype.isPoints=!0;(class extends vb{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:Px,this.minFilter=void 0!==r?r:Tx,this.magFilter=void 0!==s?s:Tx,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class RS extends vb{constructor(t,e,n,i,s,r,o,a,l,c,h,u){super(null,r,o,a,l,c,i,s,h,u),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}RS.prototype.isCompressedTexture=!0;(class extends vb{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends vb{constructor(t,e,n,i,s,r,o,a,l,c){if((c=void 0!==c?c:Ix)!==Ix&&c!==Fx)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===Ix&&(n=Sx),void 0===n&&c===Fx&&(n=Ox),super(null,i,s,r,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:xx,this.minFilter=void 0!==a?a:xx,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new Ab,new Ab,new Ab,new Bw;class IS{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),s=0;e.push(0);for(let r=1;r<=t;r++)n=this.getPoint(r/t),s+=n.distanceTo(i),e.push(s),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const s=n.length;let r;r=e||t*n[s-1];let o,a=0,l=s-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-r,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===r)return i/(s-1);const c=n[i];return(i+(r-c)/(n[i+1]-c))/(s-1)}getTangent(t,e){const n=1e-4;let i=t-n,s=t+n;i<0&&(i=0),s>1&&(s=1);const r=this.getPoint(i),o=this.getPoint(s),a=e||(r.isVector2?new ub:new Ab);return a.copy(o).sub(r).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Ab,i=[],s=[],r=[],o=new Ab,a=new ew;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Ab)}s[0]=new Ab,r[0]=new Ab;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),h=Math.abs(i[0].y),u=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),h<=l&&(l=h,n.set(0,1,0)),u<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),s[0].crossVectors(i[0],o),r[0].crossVectors(i[0],s[0]);for(let e=1;e<=t;e++){if(s[e]=s[e-1].clone(),r[e]=r[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(sb(i[e-1].dot(i[e]),-1,1));s[e].applyMatrix4(a.makeRotationAxis(o,t))}r[e].crossVectors(i[e],s[e])}if(!0===e){let e=Math.acos(sb(s[0].dot(s[t]),-1,1));e/=t,i[0].dot(o.crossVectors(s[0],s[t]))>0&&(e=-e);for(let n=1;n<=t;n++)s[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),r[n].crossVectors(i[n],s[n])}return{tangents:i,normals:s,binormals:r}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class FS extends IS{constructor(t=0,e=0,n=1,i=1,s=0,r=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=s,this.aEndAngle=r,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new ub,i=2*Math.PI;let s=this.aEndAngle-this.aStartAngle;const r=Math.abs(s)i;)s-=i;s0?0:(Math.floor(Math.abs(l)/s)+1)*s:0===c&&l===s-1&&(l=s-2,c=1),this.closed||l>0?o=i[(l-1)%s]:(zS.subVectors(i[0],i[1]).add(i[0]),o=zS);const h=i[l%s],u=i[(l+1)%s];if(this.closed||l+2i.length-2?i.length-1:r+1],h=i[r>i.length-3?i.length-1:r+2];return n.set(HS(o,a.x,l.x,c.x,h.x),HS(o,a.y,l.y,c.y,h.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e=n){const t=i[s]-n,r=this.curves[s],o=r.getLength(),a=0===o?0:1-t/o;return r.getPointAt(a,e)}s++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class eC extends tC{constructor(t){super(t),this.uuid=ib(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n80*n){a=c=t[0],l=h=t[1];for(let e=n;ec&&(c=u),d>h&&(h=d);p=Math.max(c-a,h-l),p=0!==p?1/p:0}return rC(r,o,n,a,l,p),o};function iC(t,e,n,i,s){let r,o;if(s===function(t,e,n,i){let s=0;for(let r=e,o=n-i;r0)for(r=e;r=e;r-=i)o=AC(r,t[r],t[r+1],o);return o&&vC(o,o.next)&&(MC(o),o=o.next),o}function sC(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!vC(i,i.next)&&0!==gC(i.prev,i,i.next))i=i.next;else{if(MC(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function rC(t,e,n,i,s,r,o){if(!t)return;!o&&r&&function(t,e,n,i){let 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gC(t.prev,t,t.next)<0?gC(t,e,t.next)>=0&&gC(t,t.prev,e)>=0:gC(t,e,t.prev)<0||gC(t,t.next,e)<0}function TC(t,e){const n=new EC(t.i,t.x,t.y),i=new EC(e.i,e.x,e.y),s=t.next,r=e.prev;return t.next=e,e.prev=t,n.next=s,s.prev=n,i.next=n,n.prev=i,r.next=i,i.prev=r,i}function AC(t,e,n,i){const s=new EC(t,e,n);return i?(s.next=i.next,s.prev=i,i.next.prev=s,i.next=s):(s.prev=s,s.next=s),s}function MC(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function EC(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class SC{static area(t){const e=t.length;let n=0;for(let i=e-1,s=0;s2&&t[e-1].equals(t[0])&&t.pop()}function NC(t,e){for(let n=0;nNumber.EPSILON){const u=Math.sqrt(h),d=Math.sqrt(l*l+c*c),p=e.x-a/u,_=e.y+o/u,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,s=_+a*m-t.y;const f=i*i+s*s;if(f<=2)return new ub(i,s);r=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new ub(i/r,s/r)}const O=[];for(let t=0,e=E.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+d;for(let t=0,e=E.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*p;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}FC.prototype.isMeshPhysicalMaterial=!0;class DC extends kw{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new qw(16777215),this.specular=new qw(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new qw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new ub(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}DC.prototype.isMeshPhongMaterial=!0;class BC extends kw{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new qw(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new qw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new ub(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}BC.prototype.isMeshToonMaterial=!0;class zC extends kw{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new ub(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}zC.prototype.isMeshNormalMaterial=!0;class kC extends kw{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new qw(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new qw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}kC.prototype.isMeshLambertMaterial=!0;class UC extends kw{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new qw(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new ub(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}UC.prototype.isMeshMatcapMaterial=!0;class GC extends mS{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}GC.prototype.isLineDashedMaterial=!0;const VC={arraySlice:function(t,e,n){return VC.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,s=new t.constructor(i);for(let r=0,o=0;o!==i;++r){const i=n[r]*e;for(let n=0;n!==e;++n)s[o++]=t[i+n]}return s},flattenJSON:function(t,e,n,i){let s=1,r=t[0];for(;void 0!==r&&void 0===r[i];)r=t[s++];if(void 0===r)return;let o=r[i];if(void 0!==o)if(Array.isArray(o))do{o=r[i],void 0!==o&&(e.push(r.time),n.push.apply(n,o)),r=t[s++]}while(void 0!==r);else if(void 0!==o.toArray)do{o=r[i],void 0!==o&&(e.push(r.time),o.toArray(n,n.length)),r=t[s++]}while(void 0!==r);else do{o=r[i],void 0!==o&&(e.push(r.time),n.push(o)),r=t[s++]}while(void 0!==r)},subclip:function(t,e,n,i,s=30){const r=t.clone();r.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;nr.tracks[t].times[0]&&(a=r.tracks[t].times[0]);for(let t=0;t=i.times[u]){const t=u*l+a,e=t+l-a;d=VC.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(r),d=VC.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===s){(new Tb).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;te;)--r;if(++r,0!==s||r!==i){s>=r&&(r=Math.max(r,1),s=r-1);const t=this.getValueSize();this.times=VC.arraySlice(n,s,r),this.values=VC.arraySlice(this.values,s*t,r*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,s=n.length;0===s&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let r=null;for(let e=0;e!==s;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==r&&r>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,r),t=!1;break}r=i}if(void 0!==i&&VC.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=VC.arraySlice(this.times),e=VC.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===zx,s=t.length-1;let r=1;for(let o=1;o0){t[r]=t[s];for(let t=s*n,i=r*n,o=0;o!==n;++o)e[i+o]=e[t+o];++r}return r!==t.length?(this.times=VC.arraySlice(t,0,r),this.values=VC.arraySlice(e,0,r*n)):(this.times=t,this.values=e),this}clone(){const t=VC.arraySlice(this.times,0),e=VC.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}XC.prototype.TimeBufferType=Float32Array,XC.prototype.ValueBufferType=Float32Array,XC.prototype.DefaultInterpolation=Bx;class YC extends XC{}YC.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",YC.prototype.ValueBufferType=Array,YC.prototype.DefaultInterpolation=Dx,YC.prototype.InterpolantFactoryMethodLinear=void 0,YC.prototype.InterpolantFactoryMethodSmooth=void 0;class $C extends XC{}$C.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class JC extends XC{}JC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class ZC extends HC{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)Tb.slerpFlat(s,0,r,l-o,r,l,a);return s}}class QC extends XC{InterpolantFactoryMethodLinear(t){return new ZC(this.times,this.values,this.getValueSize(),t)}}QC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",QC.prototype.DefaultInterpolation=Bx,QC.prototype.InterpolantFactoryMethodSmooth=void 0;class KC extends XC{}KC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",KC.prototype.ValueBufferType=Array,KC.prototype.DefaultInterpolation=Dx,KC.prototype.InterpolantFactoryMethodLinear=void 0,KC.prototype.InterpolantFactoryMethodSmooth=void 0;class tN extends XC{}tN.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class eN{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=ib(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,s=n.length;t!==s;++t)e.push(nN(n[t]).scale(i));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(XC.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const s=e.length,r=[];for(let t=0;t1){const t=r[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const r=[];for(const t in i)r.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return r}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,s){if(0!==n.length){const r=[],o=[];VC.flattenJSON(n,r,o,i),0!==r.length&&s.push(new t(e,r,o))}},i=[],s=t.name||\\\\\\\"default\\\\\\\",r=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Tb.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;Tb.multiplyQuaternionsFlat(t,r,t,e,t,n),Tb.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}const zN=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",kN=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),UN=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",GN=\\\\\\\"[^\\\\\\\"+zN.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",VN=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",UN),HN=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",GN),jN=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",UN),WN=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",UN),qN=new RegExp(\\\\\\\"^\\\\\\\"+VN+HN+jN+WN+\\\\\\\"$\\\\\\\"),XN=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class YN{constructor(t,e,n){this.path=e,this.parsedPath=n||YN.parseTrackName(e),this.node=YN.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new YN.Composite(t,e,n):new YN(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(kN,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=qN.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==XN.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case Vx:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=2202===n;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(2200===n){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Ux,i.endingEnd=Ux):(i.endingStart=t?this.zeroSlopeAtStart?Ux:kx:Gx,i.endingEnd=e?this.zeroSlopeAtEnd?Ux:kx:Gx)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}(class extends Zx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new BN(YN.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;tthis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return ZN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}QN.prototype.isBox2=!0;const KN=new Ab,tL=new Ab;class eL{constructor(t=new Ab,e=new Ab){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){KN.subVectors(t,this.start),tL.subVectors(this.end,this.start);const n=tL.dot(tL);let i=tL.dot(KN)/n;return e&&(i=sb(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends Ew{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const nL=new Ab,iL=new ew,sL=new ew;function rL(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n8&&t<16&&e[n].split(t+1),0!==i&&this.subTrees.push(e[n])}return this}build(){return this.calcBox(),this.split(0),this}getRayTriangles(t,e){for(let n=0;n0)for(let t=0;t0&&i>0||n<-t.radius&&i<-t.radius)return!1;const s=Math.abs(n/(Math.abs(n)+Math.abs(i))),r=uL.copy(t.start).lerp(t.end,s);if(e.containsPoint(r))return{normal:pL.normal.clone(),point:r.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=_L.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e0)for(let t=0;t0)for(let t=0;ta&&(i=o.clone().add(t.origin),s=a,n=e[r])}}return s<1e100&&{distance:s,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new yL(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}setJumpParams(t){}setGravity(t){}setPlayerMass(t){}}const bL={rotateSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI}},wL={type:\\\\\\\"change\\\\\\\"},TL=new p.a,AL=new oy;class ML extends xL{constructor(t,e,n){super(),this._camera=t,this.domElement=e,this.player=n,this.translationData={direction:new p.a},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this),onJump:this._onJump.bind(this),onRunToggle:this._onRunToggle.bind(this)},this._startCameraRotation=new Zv.a,this._rotationSpeed=bL.rotateSpeed,this._rotationRange={min:bL.rotationRange.min,max:bL.rotationRange.max},this._azimuthalAngle=0,this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this._runDomElement=this._createRunDomElement(),this._jumpDomElement=this._createJumpDomElement(),this._addElements(),this._addEvents()}dispose(){this._removeEvents(),this._removeElements(),this.updateElements()}_createTranslateDomElement(){const t=this.domElement.getBoundingClientRect(),e=Math.min(t.width,t.height),n=Math.round(.4*e),i=Math.round(.1*e),s=document.createElement(\\\\\\\"div\\\\\\\");return s.id=\\\\\\\"MobileJoystickControls-translate\\\\\\\",s.style.width=`${n}px`,s.style.height=s.style.width,s.style.border=\\\\\\\"1px solid black\\\\\\\",s.style.borderRadius=`${n}px`,s.style.position=\\\\\\\"absolute\\\\\\\",s.style.bottom=`${i}px`,s.style.left=`${i}px`,s}_jumpDomElementSize(){const t=this.domElement.getBoundingClientRect(),e=Math.min(t.width,t.height);return{size:Math.round(.2*e),margin:Math.round(.05*e)}}_createJumpDomElement(){const{size:t,margin:e}=this._jumpDomElementSize(),n=document.createElement(\\\\\\\"div\\\\\\\");n.id=\\\\\\\"MobileJoystickControls-jump\\\\\\\",n.style.width=`${t}px`;const i=Math.floor(t);return n.style.height=`${i}px`,n.style.border=\\\\\\\"1px solid black\\\\\\\",n.style.position=\\\\\\\"absolute\\\\\\\",n.style.bottom=`${2*e+parseInt(this._runDomElement.style.height)}px`,n.style.right=`${e}px`,n.style.borderRadius=`${i}px`,n}_createRunDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.2*n),s=Math.round(.05*n);return t.id=\\\\\\\"MobileJoystickControls-run\\\\\\\",t.style.width=`${i}px`,t.style.height=`${Math.floor(i)}px`,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${s}px`,t.style.right=`${s}px`,t.style.translate=\\\\\\\"-50%\\\\\\\",t}_addElements(){var t,e,n;null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement),null===(e=this.domElement.parentElement)||void 0===e||e.append(this._jumpDomElement),null===(n=this.domElement.parentElement)||void 0===n||n.append(this._runDomElement)}_removeElements(){var t;const e=[this._translateDomElement,this._jumpDomElement,this._runDomElement];for(let n of e)null===(t=n.parentElement)||void 0===t||t.removeChild(n)}updateElements(){this.player&&(this._jumpDomElement.style.display=this.player.jumpAllowed?\\\\\\\"block\\\\\\\":\\\\\\\"none\\\\\\\",this._runDomElement.style.display=this.player.runAllowed?\\\\\\\"block\\\\\\\":\\\\\\\"none\\\\\\\")}_addEvents(){cx.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),this._jumpDomElement.addEventListener(\\\\\\\"pointerdown\\\\\\\",this._boundMethods.onJump),this._runDomElement.addEventListener(\\\\\\\"pointerdown\\\\\\\",this._boundMethods.onRunToggle)}_removeEvents(){cx.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),this._jumpDomElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",this._boundMethods.onJump),this._runDomElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",this._boundMethods.onRunToggle)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=or.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this._computeAzimuthalAngle(),this.dispatchEvent(wL)}_computeAzimuthalAngle(){this._camera.updateMatrixWorld(),TL.set(0,0,1),this._camera.localToWorld(TL),TL.sub(this._camera.position),AL.setFromVector3(TL),this._azimuthalAngle=AL.theta}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);if(!this._getTouch(t,this._translateDomElement))return;this._translateDomElementRect=this._translateDomElement.getBoundingClientRect();const e=this._translateDomElementRect.left+.5*this._translateDomElementRect.width,n=this._translateDomElementRect.top+.5*this._translateDomElementRect.height;this._translationStartPosition.set(e,n)}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationDelta.x/this._translateDomElementRect.width*.5,this.translationData.direction.z=-this._translationDelta.y/this._translateDomElementRect.height*.5,this._updatePlayerTranslate(),this.dispatchEvent(wL))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.z=0,this._updatePlayerTranslate()}_updatePlayerTranslate(){if(!this.player)return;const t=this.translationData.direction;this.player.setForward(!1),this.player.setBackward(!1),this.player.setLeft(!1),this.player.setRight(!1);const e=Math.abs(t.x),n=Math.abs(t.z),i=n-e;function s(e){t.z>0&&e.setForward(!0),t.z<0&&e.setBackward(!0)}function r(e){t.x>0&&e.setRight(!0),t.x<0&&e.setLeft(!0)}i>0?(s(this.player),i<.5*n&&r(this.player)):(r(this.player),i<.5*e&&s(this.player))}_onJump(){var t;null===(t=this.player)||void 0===t||t.jump()}_onRunToggle(){if(!this.player)return;const t=this.player.running();this.player.setRun(!t);const e=this.player.running()?3:1;this._runDomElement.style.border=`${e}px solid black`}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}_getTouch(t,e){for(let n=0;n{OL.PARAM_CALLBACK_updatePlayerParams(t)}}}const LL=new class extends ua{constructor(){super(...arguments),this.main=ha.FOLDER(),this.colliderObject=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},dependentOnFoundNode:!1,callback:t=>{OL.PARAM_CALLBACK_updateCollider(t)}}),this.capsuleRadius=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...NL()}),this.capsuleHeight=ha.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...NL()}),this.physics=ha.FOLDER(),this.physicsSteps=ha.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...NL()}),this.gravity=ha.VECTOR3([0,-30,0],{...NL()}),this.translateSpeed=ha.FLOAT(1),this.rotateSpeed=ha.FLOAT(bL.rotateSpeed),this.jumpAllowed=ha.BOOLEAN(!0,{...NL()}),this.jumpStrength=ha.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...NL()}),this.runAllowed=ha.BOOLEAN(!0,{...NL()}),this.runSpeedMult=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...NL()}),this.updateCollider=ha.BUTTON(null,{callback:t=>{OL.PARAM_CALLBACK_updateCollider(t)}}),this.init=ha.FOLDER(),this.startPosition=ha.VECTOR3([0,2,0],{...NL()}),this.startRotation=ha.VECTOR3([0,0,0],{...NL()}),this.reset=ha.BUTTON(null,{callback:t=>{OL.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=ha.FLOAT(\\\\\\\"-$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=ha.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]})}};class OL extends Jv{constructor(){super(...arguments),this.paramsConfig=LL,this._controls_by_element_id=new Map}static type(){return _s.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}collisionController(){return this._collisionController=this._collisionController||new $y(this)}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(OL.INPUT_UPDATE_COLLIDER,Ko.BASE,this._updateCollider.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(EL,Ko.BASE),new ta(SL,Ko.BASE),new ta(CL,Ko.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new ML(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}player(){return this._player}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}),this._controls_by_element_id.forEach((t=>t.updateElements())))}async _createPlayer(t){const e=t,n=await this.collisionController().getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new jy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _updateCollider(){await this.collisionController().updateCollider()}_bind_listeners_to_controls_instance(t){t.addEventListener(EL,(()=>{this.dispatchEventToOutput(EL,{})})),t.addEventListener(SL,(()=>{this.dispatchEventToOutput(SL,{})})),t.addEventListener(CL,(()=>{this.dispatchEventToOutput(CL,{})}))}updateRequired(){return!0}setupControls(t){t.setRotationSpeed(this.pv.rotateSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle}),t.updateElements()}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var PL;OL.INPUT_UPDATE_COLLIDER=\\\\\\\"updateCollider\\\\\\\",function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(PL||(PL={}));const RL=[PL.ALL_TOGETHER,PL.BATCH];const IL=new class extends ua{constructor(){super(...arguments),this.mask=ha.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{FL.PARAM_CALLBACK_updateResolvedNodes(t)}}),this.force=ha.BOOLEAN(0),this.cookMode=ha.INTEGER(RL.indexOf(PL.ALL_TOGETHER),{menu:{entries:RL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=ha.INTEGER(1,{visibleIf:{cookMode:RL.indexOf(PL.BATCH)},separatorAfter:!0}),this.registerOnlyFirstCooks=ha.BOOLEAN(!0),this.updateResolve=ha.BUTTON(null,{callback:(t,e)=>{FL.PARAM_CALLBACK_updateResolve(t)}}),this.printResolve=ha.BUTTON(null,{callback:(t,e)=>{FL.PARAM_CALLBACK_printResolve(t)}})}};class FL extends Va{constructor(){super(...arguments),this.paramsConfig=IL,this._resolvedNodes=[],this._dispatchedFirstNodeCooked=!1,this._dispatchedAllNodesCooked=!1,this._cookStateByNodeId=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(FL.INPUT_TRIGGER,Ko.BASE,this.processEventTrigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(FL.OUTPUT_FIRST_NODE,Ko.BASE),new ta(FL.OUTPUT_EACH_NODE,Ko.BASE),new ta(FL.OUTPUT_ALL_NODES,Ko.BASE)])}trigger(){this.processEventTrigger({})}cook(){this._updateResolvedNodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}resolvedNodes(){return this._resolvedNodes}processEventTrigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._updateResolvedNodes();const t=RL[this.pv.cookMode];switch(t){case PL.ALL_TOGETHER:return this._cookNodesAllTogether();case PL.BATCH:return this._cookNodesInBatch()}os.unreachable(t)}_cookNodesAllTogether(){this._cookNodes(this._resolvedNodes)}async _cookNodesInBatch(){const t=this.pv.batchSize,e=Math.ceil(this._resolvedNodes.length/t);for(let n=0;n{this._onNodeCookComplete(t)})),this._cookStateByNodeId.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatchedFirstNodeCooked=!1,this._cookStateByNodeId.clear();for(let t of this._resolvedNodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolvedNodes=[]}_allNodesHaveCooked(){for(let t of this._resolvedNodes){if(!this._cookStateByNodeId.get(t.graphNodeId()))return!1}return!0}_onNodeCookComplete(t){const e={value:{node:t}};this._dispatchedFirstNodeCooked||(this._dispatchedFirstNodeCooked=!0,this.dispatchEventToOutput(FL.OUTPUT_FIRST_NODE,e));this._cookStateByNodeId.get(t.graphNodeId())&&this.pv.registerOnlyFirstCooks||this.dispatchEventToOutput(FL.OUTPUT_EACH_NODE,e),this._cookStateByNodeId.set(t.graphNodeId(),!0),this._dispatchedAllNodesCooked||this._allNodesHaveCooked()&&(this._dispatchedAllNodesCooked=!0,this.dispatchEventToOutput(FL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_updateResolve(t){t._allNodesHaveCooked()}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedNodes)}}var DL,BL;FL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",FL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",FL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",FL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(DL||(DL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(BL||(BL={}));const zL=new class extends ua{};class kL extends Va{constructor(){super(...arguments),this.paramsConfig=zL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(DL.TRIGGER,Ko.BASE,this.processEventTrigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(BL.OUT,Ko.BASE)])}processEvent(t){}processEventTrigger(t){this.dispatchEventToOutput(BL.OUT,t)}}const UL=\\\\\\\"init\\\\\\\",GL=\\\\\\\"dispose\\\\\\\",VL=\\\\\\\"reset\\\\\\\";function HL(){return{callback:t=>{YL.PARAM_CALLBACK_updatePlayerParams(t)}}}const jL=new p.a,WL=new p.a,qL=new oy;const XL=new class extends ua{constructor(){super(...arguments),this.main=ha.FOLDER(),this.playerObject=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.colliderObject=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},dependentOnFoundNode:!1,callback:t=>{YL.PARAM_CALLBACK_updateCollider(t)}}),this.camera=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{types:[is.PERSPECTIVE,is.ORTHOGRAPHIC],context:ts.OBJ}}),this.initPlayer=ha.BUTTON(null,{callback:t=>{YL.PARAM_CALLBACK_initPlayer(t)}}),this.capsuleRadius=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...HL()}),this.capsuleHeight=ha.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...HL()}),this.physics=ha.FOLDER(),this.physicsSteps=ha.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...HL()}),this.gravity=ha.VECTOR3([0,-30,0],{...HL()}),this.speed=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...HL()}),this.jumpAllowed=ha.BOOLEAN(!0,{...HL()}),this.jumpStrength=ha.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...HL()}),this.runAllowed=ha.BOOLEAN(!0,{...HL()}),this.runSpeedMult=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...HL()}),this.updateCollider=ha.BUTTON(null,{callback:t=>{YL.PARAM_CALLBACK_updateCollider(t)}}),this.mesh=ha.FOLDER(),this.useMesh=ha.BOOLEAN(!0,{callback:t=>{YL.PARAM_CALLBACK_updatePlayerMesh(t)}}),this.material=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},callback:t=>{YL.PARAM_CALLBACK_updatePlayerMaterial(t)}}),this.init=ha.FOLDER(),this.startPosition=ha.VECTOR3([0,5,0],{...HL()}),this.reset=ha.BUTTON(null,{callback:t=>{YL.PARAM_CALLBACK_resetPlayer(t)}})}};class YL extends Va{constructor(){super(...arguments),this.paramsConfig=XL}static type(){return _s.PLAYER}collisionController(){return this._collisionController=this._collisionController||new $y(this)}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(UL,Ko.BASE,this._initPlayer.bind(this)),new ta(GL,Ko.BASE,this._disposePlayer.bind(this)),new ta(VL,Ko.BASE,this._resetPlayer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(UL,Ko.BASE),new ta(GL,Ko.BASE),new ta(VL,Ko.BASE)])}async _initPlayer(){if(this._player=this._player||await this._createPlayer(),!this._player)return void this.states.error.set(\\\\\\\"could not create player\\\\\\\");this._updatePlayerMesh(),this._updatePlayerMaterial(),this._updatePlayerParams(),this._corePlayerKeyEvents=new qy(this._player),this._corePlayerKeyEvents.addEvents(),this._player.reset();const t=this._player;this.scene().registerOnBeforeTick(this._callbackName(),(e=>{t.setAzimuthalAngle(this._getAzimuthalAngle()),t.update(e)})),this.dispatchEventToOutput(UL,{})}player(){return this._player}_callbackName(){return`event/PlayerControls-${this.graphNodeId()}`}_disposePlayer(){var t;this._player&&(null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),this.scene().unRegisterOnBeforeTick(this._callbackName())),this.dispatchEventToOutput(GL,{})}_resetPlayer(){this._player&&this._player.reset(),this.dispatchEventToOutput(VL,{})}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.speed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}_updatePlayerMesh(){this._player&&this._player.setUsePlayerMesh(this.pv.useMesh)}async _updatePlayerMaterial(){if(!this._player)return;const t=this.pv.material.nodeWithContext(ts.MAT);if(!t)return void this.states.error.set(\\\\\\\"material node not found\\\\\\\");const e=(await t.compute()).material();this._player.setMaterial(e)}async _createPlayer(){const t=this.pv.playerObject.nodeWithContext(ts.OBJ);if(!t)return void this.states.error.set(\\\\\\\"player node not found\\\\\\\");const e=this.pv.camera.nodeWithContext(ts.OBJ);if(!e)return void this.states.error.set(\\\\\\\"invalid camera node\\\\\\\");this._cameraObject=e.object;const n=t.object,i=await this.collisionController().getCollider();if(!i)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new jy({object:n,collider:i,meshName:this.path()})}async _updateCollider(){await this.collisionController().updateCollider()}_getAzimuthalAngle(){if(!this._cameraObject||!this._player)return 0;const t=this._cameraObject.position,e=this._player.object.position;return jL.copy(t),WL.copy(e),jL.sub(WL),qL.setFromVector3(jL),qL.theta}static PARAM_CALLBACK_initPlayer(t){t._initPlayer()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_updatePlayerMaterial(t){t._updatePlayerMaterial()}static PARAM_CALLBACK_updatePlayerMesh(t){t._updatePlayerMesh()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var $L,JL=n(39),ZL=n(36);class QL{constructor(t,e,n=0,i=1/0){this.ray=new JL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new ZL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return tO(t,this,n,e),n.sort(KL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,s=t.length;i{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=iO[this._node.pv.intersectWith];switch(e){case nO.GEOMETRY:return this._intersect_with_geometry(t);case nO.PLANE:return this._intersect_with_plane(t)}os.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=ks[this._node.pv.geoAttributeType],n=rO.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case zs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case zs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}os.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Os(t.object.constructor)){case Ns.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Ns.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const s=i.getAttribute(e);if(s){switch(n){case zs.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(s,t.face.a),this._uvB.fromBufferAttribute(s,t.face.b),this._uvC.fromBufferAttribute(s,t.face.c),t.uv=tr.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case zs.STRING:{const t=new fr(i).points()[0];return t?t.stringAttribValue(e):void 0}}os.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case zs.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case zs.STRING:{const n=new fr(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}os.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(li.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Cr.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Cr.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(ts.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=mO[this._node.pv.targetType];switch(t){case _O.NODE:return this._update_target_from_node();case _O.SCENE_GRAPH:return this._update_target_from_scene_graph()}os.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(ts.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}rO._vA=new p.a,rO._vB=new p.a,rO._vC=new p.a,rO._uvA=new d.a,rO._uvB=new d.a,rO._uvC=new d.a,rO._hitUV=new d.a;class oO{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,s=i.renderController;if(s){if(this._render_target=this._render_target||new Q(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,r=s.resolved_scene||i.scene().threejsScene(),o=s.renderer(n);this._modify_scene_and_renderer(r,o),o.setRenderTarget(this._render_target),o.clear(),o.render(r,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(r,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==ts.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const aO=1e3/60;var lO;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(lO||(lO={}));const cO=[lO.CPU,lO.GPU];function hO(t={}){return t.mode=cO.indexOf(lO.CPU),{visibleIf:t}}function uO(t={}){return t.mode=cO.indexOf(lO.CPU),t.intersectWith=iO.indexOf(nO.GEOMETRY),{visibleIf:t}}function dO(t={}){return t.mode=cO.indexOf(lO.CPU),t.intersectWith=iO.indexOf(nO.PLANE),{visibleIf:t}}function pO(t={}){return t.mode=cO.indexOf(lO.GPU),{visibleIf:t}}var _O;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(_O||(_O={}));const mO=[_O.SCENE_GRAPH,_O.NODE];const fO=new class extends ua{constructor(){super(...arguments),this.mode=ha.INTEGER(cO.indexOf(lO.CPU),{menu:{entries:cO.map(((t,e)=>({name:t,value:e})))}}),this.mouse=ha.VECTOR2([0,0],{cook:!1}),this.overrideCamera=ha.BOOLEAN(0),this.overrideRay=ha.BOOLEAN(0,{visibleIf:{mode:cO.indexOf(lO.CPU),overrideCamera:1}}),this.camera=ha.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=ha.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=ha.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=ha.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{oO.PARAM_CALLBACK_update_material(t)},...pO()}),this.pixelValue=ha.VECTOR4([0,0,0,0],{cook:!1,...pO()}),this.hitThreshold=ha.FLOAT(.5,{cook:!1,...pO()}),this.intersectWith=ha.INTEGER(iO.indexOf(nO.GEOMETRY),{menu:{entries:iO.map(((t,e)=>({name:t,value:e})))},...hO()}),this.pointsThreshold=ha.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...hO()}),this.planeDirection=ha.VECTOR3([0,1,0],{...dO()}),this.planeOffset=ha.FLOAT(0,{...dO()}),this.targetType=ha.INTEGER(0,{menu:{entries:mO.map(((t,e)=>({name:t,value:e})))},...uO()}),this.targetNode=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{rO.PARAM_CALLBACK_update_target(t)},...uO({targetType:mO.indexOf(_O.NODE)})}),this.objectMask=ha.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{rO.PARAM_CALLBACK_update_target(t)},...uO({targetType:mO.indexOf(_O.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=ha.BUTTON(null,{callback:(t,e)=>{rO.PARAM_CALLBACK_print_resolve(t)},...uO({targetType:mO.indexOf(_O.SCENE_GRAPH)})}),this.traverseChildren=ha.BOOLEAN(!0,{callback:(t,e)=>{rO.PARAM_CALLBACK_update_target(t)},...uO(),separatorAfter:!0}),this.tpositionTarget=ha.BOOLEAN(0,{cook:!1,...hO()}),this.position=ha.VECTOR3([0,0,0],{cook:!1,...hO({tpositionTarget:0})}),this.positionTarget=ha.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...hO({tpositionTarget:1}),paramSelection:Cr.VECTOR3,computeOnDirty:!0}),this.tvelocity=ha.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=ha.BOOLEAN(0,{cook:!1,...hO({tvelocity:1})}),this.velocity=ha.VECTOR3([0,0,0],{cook:!1,...hO({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=ha.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...hO({tvelocity:1,tvelocityTarget:1}),paramSelection:Cr.VECTOR3,computeOnDirty:!0}),this.geoAttribute=ha.BOOLEAN(0,uO()),this.geoAttributeName=ha.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...uO({geoAttribute:1})}),this.geoAttributeType=ha.INTEGER(ks.indexOf(zs.NUMERIC),{menu:{entries:Us},...uO({geoAttribute:1})}),this.geoAttributeValue1=ha.FLOAT(0,{cook:!1,...uO({geoAttribute:1,geoAttributeType:ks.indexOf(zs.NUMERIC)})}),this.geoAttributeValues=ha.STRING(\\\\\\\"\\\\\\\",{...uO({geoAttribute:1,geoAttributeType:ks.indexOf(zs.STRING)})})}};class gO extends Va{constructor(){super(...arguments),this.paramsConfig=fO,this.cpuController=new rO(this),this.gpuController=new oO(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(gO.INPUT_TRIGGER,Ko.BASE,this._process_trigger_event_throttled.bind(this)),new ta(gO.INPUT_MOUSE,Ko.MOUSE,this._process_mouse_event.bind(this)),new ta(gO.INPUT_UPDATE_OBJECTS,Ko.BASE,this._process_trigger_update_objects.bind(this)),new ta(gO.INPUT_TRIGGER_VEL_RESET,Ko.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new ta(gO.OUTPUT_HIT,Ko.BASE),new ta(gO.OUTPUT_MISS,Ko.BASE)])}trigger_hit(t){this.dispatchEventToOutput(gO.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(gO.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==cO.indexOf(lO.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=li.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),aO-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==cO.indexOf(lO.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==cO.indexOf(lO.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==cO.indexOf(lO.CPU)&&this.cpuController.velocity_controller.reset()}}var vO;gO.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",gO.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",gO.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",gO.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",gO.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",gO.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(vO||(vO={}));const yO=[vO.SET,vO.TOGGLE];const xO=new class extends ua{constructor(){super(...arguments),this.mask=ha.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=ha.BOOLEAN(0),this.displayMode=ha.INTEGER(yO.indexOf(vO.SET),{visibleIf:{tdisplay:1},menu:{entries:yO.map(((t,e)=>({name:t,value:e})))}}),this.display=ha.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:yO.indexOf(vO.SET)},separatorAfter:!0}),this.tbypass=ha.BOOLEAN(0),this.bypassMode=ha.INTEGER(yO.indexOf(vO.SET),{visibleIf:{tbypass:1},menu:{entries:yO.map(((t,e)=>({name:t,value:e})))}}),this.bypass=ha.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:yO.indexOf(vO.SET)}}),this.execute=ha.BUTTON(null,{callback:t=>{bO.PARAM_CALLBACK_execute(t)}})}};class bO extends Va{constructor(){super(...arguments),this.paramsConfig=xO}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(\\\\\\\"trigger\\\\\\\",Ko.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=yO[this.pv.displayMode];switch(i){case vO.SET:return void n.set(this.pv.display);case vO.TOGGLE:return void n.set(!n.active())}os.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=yO[this.pv.bypassMode];switch(i){case vO.SET:return void n.set(this.pv.bypass);case vO.TOGGLE:return void n.set(!n.active())}os.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var wO;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(wO||(wO={}));const TO=[wO.BOOLEAN,wO.BUTTON,wO.NUMBER,wO.VECTOR2,wO.VECTOR3,wO.VECTOR4,wO.STRING],AO=TO.indexOf(wO.BOOLEAN),MO=TO.indexOf(wO.NUMBER),EO=TO.indexOf(wO.VECTOR2),SO=TO.indexOf(wO.VECTOR3),CO=TO.indexOf(wO.VECTOR4),NO=TO.indexOf(wO.STRING),LO=\\\\\\\"output\\\\\\\";const OO=new class extends ua{constructor(){super(...arguments),this.param=ha.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=ha.INTEGER(MO,{menu:{entries:TO.map(((t,e)=>({name:t,value:e})))}}),this.toggle=ha.BOOLEAN(0,{visibleIf:{type:AO}}),this.boolean=ha.BOOLEAN(0,{visibleIf:{type:AO,toggle:0}}),this.number=ha.FLOAT(0,{visibleIf:{type:MO}}),this.vector2=ha.VECTOR2([0,0],{visibleIf:{type:EO}}),this.vector3=ha.VECTOR3([0,0,0],{visibleIf:{type:SO}}),this.vector4=ha.VECTOR4([0,0,0,0],{visibleIf:{type:CO}}),this.increment=ha.BOOLEAN(0,{visibleIf:[{type:MO},{type:EO},{type:SO},{type:CO}]}),this.string=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:NO}}),this.execute=ha.BUTTON(null,{callback:t=>{PO.PARAM_CALLBACK_execute(t)}})}};class PO extends Va{constructor(){super(...arguments),this.paramsConfig=OO,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new ta(\\\\\\\"trigger\\\\\\\",Ko.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new ta(LO,Ko.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(LO,t)}async _new_param_value(t){const e=TO[this.pv.type];switch(e){case wO.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case wO.BUTTON:return t.options.executeCallback();case wO.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Cr.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case wO.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Cr.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case wO.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Cr.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case wO.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Cr.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case wO.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}os.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const RO=new class extends ua{constructor(){super(...arguments),this.outputsCount=ha.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class IO extends Va{constructor(){super(...arguments),this.paramsConfig=RO}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[Ko.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill(Ko.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n{this._timer_active&&(this.pv.count<=0||this._current_count{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class UO extends aa{static context(){return ts.EVENT}cook(){this.cookController.endCook()}}class GO extends UO{}class VO extends GO{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class HO extends GO{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class jO extends GO{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class WO extends GO{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class qO extends UO{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class XO extends GO{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const YO=\\\\\\\"int\\\\\\\";const $O=new class extends ua{constructor(){super(...arguments),this.float=ha.FLOAT(0)}};class JO extends gf{constructor(){super(...arguments),this.paramsConfig=$O}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(YO,Uo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(YO)} = int(${mf.float(e)})`;t.addBodyLines(this,[n])}}const ZO=\\\\\\\"float\\\\\\\";const QO=new class extends ua{constructor(){super(...arguments),this.int=ha.INTEGER(0)}};class KO extends gf{constructor(){super(...arguments),this.paramsConfig=QO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(ZO,Uo.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(ZO)} = float(${mf.integer(e)})`;t.addBodyLines(this,[n])}}const tP=\\\\\\\"bool\\\\\\\";const eP=new class extends ua{constructor(){super(...arguments),this.int=ha.INTEGER(0)}};class nP extends gf{constructor(){super(...arguments),this.paramsConfig=eP}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(tP,Uo.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(tP)} = bool(${mf.integer(e)})`;t.addBodyLines(this,[n])}}const iP=new class extends ua{constructor(){super(...arguments),this.bool=ha.BOOLEAN(0)}};class sP extends gf{constructor(){super(...arguments),this.paramsConfig=iP}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(YO,Uo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(YO)} = int(${mf.bool(e)})`;t.addBodyLines(this,[n])}}const rP=new class extends ua{constructor(){super(...arguments),this.x=ha.FLOAT(0),this.y=ha.FLOAT(0)}};class oP extends gf{constructor(){super(...arguments),this.paramsConfig=rP}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(oP.OUTPUT_NAME,Uo.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(oP.OUTPUT_NAME)} = ${mf.float2(e,n)}`;t.addBodyLines(this,[i])}}oP.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const aP=new class extends ua{constructor(){super(...arguments),this.x=ha.FLOAT(0),this.y=ha.FLOAT(0),this.z=ha.FLOAT(0)}};class lP extends gf{constructor(){super(...arguments),this.paramsConfig=aP}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(lP.OUTPUT_NAME,Uo.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=`vec3 ${this.glVarName(lP.OUTPUT_NAME)} = ${mf.float3(e,n,i)}`;t.addBodyLines(this,[s])}}lP.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const cP=new class extends ua{constructor(){super(...arguments),this.x=ha.FLOAT(0),this.y=ha.FLOAT(0),this.z=ha.FLOAT(0),this.w=ha.FLOAT(0)}};class hP extends gf{constructor(){super(...arguments),this.paramsConfig=cP}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(hP.OUTPUT_NAME,Uo.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=this.variableForInputParam(this.p.w),r=`vec4 ${this.glVarName(hP.OUTPUT_NAME)} = ${mf.float4(e,n,i,s)}`;t.addBodyLines(this,[r])}}hP.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const uP=new class extends ua{};class dP extends gf{constructor(){super(...arguments),this.paramsConfig=uP}}function pP(t,e){const n=e.components,i=e.param_type;return class extends dP{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new qo(t,Uo.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const _P=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],mP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],fP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class gP extends(pP(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Cr.VECTOR2})){}class vP extends(pP(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Cr.VECTOR3})){}class yP extends(pP(\\\\\\\"vec4ToFloat\\\\\\\",{components:fP,param_type:Cr.VECTOR4})){}class xP extends dP{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(xP.OUTPUT_NAME_VEC3,Uo.VEC3),new qo(xP.OUTPUT_NAME_W,Uo.FLOAT)])}createParams(){this.addParam(Cr.VECTOR4,xP.INPUT_NAME_VEC4,fP.map((t=>0)))}setLines(t){const e=[],n=xP.INPUT_NAME_VEC4,i=xP.OUTPUT_NAME_VEC3,s=xP.OUTPUT_NAME_W,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${r}.xyz`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.w`)}t.addBodyLines(this,e)}}xP.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",xP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",xP.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class bP extends dP{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(bP.OUTPUT_NAME_VEC2,Uo.VEC2),new qo(bP.OUTPUT_NAME_Z,Uo.FLOAT)])}createParams(){this.addParam(Cr.VECTOR3,bP.INPUT_NAME_VEC3,mP.map((t=>0)))}setLines(t){const e=[],n=bP.INPUT_NAME_VEC3,i=bP.OUTPUT_NAME_VEC2,s=bP.OUTPUT_NAME_Z,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${r}.xy`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.z`)}t.addBodyLines(this,e)}}bP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",bP.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",bP.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class wP extends dP{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(wP.OUTPUT_NAME_VEC3,Uo.VEC3)])}createParams(){this.addParam(Cr.VECTOR2,wP.INPUT_NAME_VEC2,_P.map((t=>0))),this.addParam(Cr.FLOAT,wP.INPUT_NAME_Z,0)}setLines(t){const e=[],n=wP.INPUT_NAME_VEC2,i=wP.INPUT_NAME_Z,s=wP.OUTPUT_NAME_VEC3,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec3 ${a} = vec3(${r}.xy, ${o})`),t.addBodyLines(this,e)}}wP.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",wP.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",wP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class TP extends dP{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(TP.OUTPUT_NAME_VEC4,Uo.VEC4)])}createParams(){this.addParam(Cr.VECTOR3,TP.INPUT_NAME_VEC3,mP.map((t=>0))),this.addParam(Cr.FLOAT,TP.INPUT_NAME_W,0)}setLines(t){const e=[],n=TP.INPUT_NAME_VEC3,i=TP.INPUT_NAME_W,s=TP.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),t.addBodyLines(this,e)}}TP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",TP.INPUT_NAME_W=\\\\\\\"w\\\\\\\",TP.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const AP=new class extends ua{};class MP extends gf{constructor(){super(...arguments),this.paramsConfig=AP}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return mf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class EP extends MP{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Uo.FLOAT]}}class SP extends MP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t]}}class CP extends MP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t,t]}}class NP extends MP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t,t,t]}}class LP extends MP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t,t,t,t]}}function OP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||\\\\\\\"in\\\\\\\";return class extends EP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return s}_gl_output_name(t){return i}gl_method_name(){return n}}}class PP extends(OP(\\\\\\\"abs\\\\\\\")){}class RP extends(OP(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class IP extends(OP(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class FP extends(OP(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class DP extends(OP(\\\\\\\"ceil\\\\\\\")){}class BP extends(OP(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class zP extends(OP(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class kP extends(OP(\\\\\\\"exp\\\\\\\")){}class UP extends(OP(\\\\\\\"exp2\\\\\\\")){}class GP extends(OP(\\\\\\\"floor\\\\\\\")){}class VP extends(OP(\\\\\\\"fract\\\\\\\")){}class HP extends(OP(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class jP extends(OP(\\\\\\\"log\\\\\\\")){}class WP extends(OP(\\\\\\\"log2\\\\\\\")){}class qP extends(OP(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class XP extends(OP(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class YP extends(OP(\\\\\\\"sign\\\\\\\")){}class $P extends(OP(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class JP extends(OP(\\\\\\\"sqrt\\\\\\\")){}class ZP extends(OP(\\\\\\\"tan\\\\\\\")){}function QP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],r=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends SP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Cf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():r}const e=t||r||Uo.FLOAT;return[e,e]}}}class KP extends(QP(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Uo.VEC3,allowed_in_types:[Uo.VEC2,Uo.VEC3,Uo.VEC4],out_type:Uo.FLOAT})){}class tR extends(QP(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Uo.VEC3,allowed_in_types:[Uo.VEC2,Uo.VEC3,Uo.VEC4],out_type:Uo.FLOAT})){}class eR extends(QP(\\\\\\\"max\\\\\\\")){}class nR extends(QP(\\\\\\\"min\\\\\\\")){}class iR extends(QP(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Uo.FLOAT;return[t,t]}}class sR extends(QP(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class rR extends(QP(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Uo.VEC3})){}class oR extends(QP(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function aR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=e.default||{},o=e.out_type||Uo.FLOAT,a=e.functions||[];return class extends CP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return r[t]}gl_function_definitions(){return a.map((t=>new Cf(this,t)))}}}class lR extends(aR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class cR extends(aR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class hR extends(aR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function uR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.operation,r=e.allowed_in_types;return class extends SP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return mf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return s}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&r&&!r.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Uo.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,s=[];for(let t=0;t{const n=t.name();return mf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function gR(t,e){return class extends fR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class vR extends(gR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class yR extends(gR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var xR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(xR||(xR={}));var bR,wR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(bR||(bR={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(wR||(wR={}));const TR=[bR.POSITION,bR.VELOCITY,bR.MASS,bR.FORCE],AR=[wR.POSITION,wR.VELOCITY],MR={[bR.POSITION]:[0,0,0],[bR.VELOCITY]:[0,0,0],[bR.MASS]:1,[bR.FORCE]:[0,-9.8,0]};const ER=new class extends ua{};class SR extends gf{constructor(){super(...arguments),this.paramsConfig=ER}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(wR.POSITION,Uo.VEC3),new qo(wR.VELOCITY,Uo.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.VEC3;return[t,t,Uo.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return TR[t]}_gl_output_name(t){return AR[t]}paramDefaultValue(t){return MR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Nf(this,Uo.FLOAT,xR.DELTA_TIME),i=new Cf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const s=mf.any(this.variableForInput(bR.POSITION)),r=mf.any(this.variableForInput(bR.VELOCITY)),o=mf.float(this.variableForInput(bR.MASS)),a=mf.any(this.variableForInput(bR.FORCE)),l=this.glVarName(wR.POSITION),c=this.glVarName(wR.VELOCITY),h=`${e} ${c} = compute_velocity_from_acceleration(${[r,a,o,xR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,u=`${e} ${l} = compute_position_from_velocity(${[s,c,xR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[h,u])}}var CR,NR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n// let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n// let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n// out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n// out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n// out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n// out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n// return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s, oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s, oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s, 0.0, oc * axis.z * axis.x - axis.y * s, oc * axis.y * axis.z + axis.x * s, oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(CR||(CR={}));const LR=[CR.DIR,CR.UP],OR={[CR.DIR]:[0,0,1],[CR.UP]:[0,1,0]};class PR extends SP{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>LR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Uo.VEC3,Uo.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Uo.VEC4]))}paramDefaultValue(t){return OR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Cf(this,NR)]}}var RR;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(RR||(RR={}));const IR=[RR.LINEAR,RR.GAMMA,RR.SRGB,RR.RGBE,RR.RGBM,RR.RGBD,RR.LogLuv];const FR=new class extends ua{constructor(){super(...arguments),this.color=ha.VECTOR4([1,1,1,1]),this.from=ha.INTEGER(IR.indexOf(RR.LINEAR),{menu:{entries:IR.map(((t,e)=>({name:t,value:e})))}}),this.to=ha.INTEGER(IR.indexOf(RR.GAMMA),{menu:{entries:IR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=ha.FLOAT(2.2)}};class DR extends gf{constructor(){super(...arguments),this.paramsConfig=FR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new qo(DR.OUTPUT_NAME,Uo.VEC4)])}setLines(t){const e=IR[this.pv.from],n=IR[this.pv.to],i=this.glVarName(DR.OUTPUT_NAME),s=mf.any(this.variableForInput(DR.INPUT_NAME)),r=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(s),e==RR.GAMMA||n==RR.GAMMA){const t=mf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}r.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);t.addBodyLines(this,r)}}var BR,zR;DR.INPUT_NAME=\\\\\\\"color\\\\\\\",DR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",DR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(BR||(BR={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(zR||(zR={}));const kR=[BR.EQUAL,BR.LESS_THAN,BR.GREATER_THAN,BR.LESS_THAN_OR_EQUAL,BR.GREATER_THAN_OR_EQUAL,BR.NOT_EQUAL],UR=[zR.EQUAL,zR.LESS_THAN,zR.GREATER_THAN,zR.LESS_THAN_OR_EQUAL,zR.GREATER_THAN_OR_EQUAL,zR.NOT_EQUAL],GR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const VR=new class extends ua{constructor(){super(...arguments),this.test=ha.INTEGER(0,{menu:{entries:kR.map(((t,e)=>({name:`${UR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class HR extends gf{constructor(){super(...arguments),this.paramsConfig=VR}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Uo.BOOL]))}set_test_name(t){this.p.test.set(kR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=UR[this.pv.test],s=mf.any(this.variableForInput(this._gl_input_name(0))),r=mf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Wo[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=ha.BOOLEAN(0,WR(Uo.BOOL)),this.int=ha.INTEGER(0,WR(Uo.INT)),this.float=ha.FLOAT(0,WR(Uo.FLOAT)),this.vec2=ha.VECTOR2([0,0],WR(Uo.VEC2)),this.vec3=ha.VECTOR3([0,0,0],WR(Uo.VEC3)),this.vec4=ha.VECTOR4([0,0,0,0],WR(Uo.VEC4))}};class XR extends gf{constructor(){super(...arguments),this.paramsConfig=qR,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>XR.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=mf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=mf.integer(e.value));const s=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[s])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=Go[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Uo.BOOL,this.p.bool],[Uo.INT,this.p.int],[Uo.FLOAT,this.p.float],[Uo.VEC2,this.p.vec2],[Uo.VEC3,this.p.vec3],[Uo.VEC4,this.p.vec4]]);const t=Go[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(XR.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(Go.indexOf(t))}}XR.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const YR=\\\\\\\"cross\\\\\\\";const $R=new class extends ua{constructor(){super(...arguments),this.x=ha.VECTOR3([0,0,1]),this.y=ha.VECTOR3([0,1,0])}};class JR extends gf{constructor(){super(...arguments),this.paramsConfig=$R}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(YR,Uo.VEC3)])}setLines(t){const e=mf.float(this.variableForInputParam(this.p.x)),n=mf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(YR)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class ZR extends(aR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var QR=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const KR=new class extends ua{constructor(){super(...arguments),this.position=ha.VECTOR2([0,0]),this.center=ha.VECTOR2([0,0]),this.radius=ha.FLOAT(1),this.feather=ha.FLOAT(.1)}};class tI extends gf{constructor(){super(...arguments),this.paramsConfig=KR}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"float\\\\\\\",Uo.FLOAT)])}setLines(t){const e=mf.vector2(this.variableForInputParam(this.p.position)),n=mf.vector2(this.variableForInputParam(this.p.center)),i=mf.float(this.variableForInputParam(this.p.radius)),s=mf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Cf(this,QR)])}}var eI=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n const float a = 4.0 / 11.0;\\\\n const float b = 8.0 / 11.0;\\\\n const float c = 9.0 / 10.0;\\\\n\\\\n const float ca = 4356.0 / 361.0;\\\\n const float cb = 35442.0 / 1805.0;\\\\n const float cc = 16061.0 / 1805.0;\\\\n\\\\n float t2 = t * t;\\\\n\\\\n return t < a\\\\n ? 7.5625 * t2\\\\n : t < b\\\\n ? 9.075 * t2 - 9.9 * t + 3.4\\\\n : t < c\\\\n ? ca * t2 - cb * t + cc\\\\n : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const nI=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],iI={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n return t == 0.0 || t == 1.0\\\\n ? t\\\\n : t < 0.5\\\\n ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n float f = t < 0.5\\\\n ? 2.0 * t\\\\n : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n return t < 0.5\\\\n ? 0.5 * g\\\\n : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n float f = 1.0 - t;\\\\n return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n return t < 0.5\\\\n ? +8.0 * pow(t, 4.0)\\\\n : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n return t < 0.5\\\\n ? 4.0 * t * t * t\\\\n : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":eI,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n float p = 2.0 * t * t;\\\\n return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n return t < 0.5\\\\n ? +16.0 * pow(t, 5.0)\\\\n : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n float f = t - 1.0;\\\\n return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},sI={\\\\\\\"bounce-in\\\\\\\":[eI],\\\\\\\"bounce-in-out\\\\\\\":[eI]},rI={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},oI=nI.indexOf(\\\\\\\"sine-in-out\\\\\\\");const aI=new class extends ua{constructor(){super(...arguments),this.type=ha.INTEGER(oI,{menu:{entries:nI.map(((t,e)=>({name:t,value:e})))}}),this.input=ha.FLOAT(0)}};class lI extends gf{constructor(){super(...arguments),this.paramsConfig=aI}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"out\\\\\\\",Uo.FLOAT)])}setLines(t){const e=nI[this.pv.type],n=rI[e];let i=[new Cf(this,iI[e])];const s=(sI[e]||[]).map((t=>new Cf(this,t)));s&&(i=s.concat(i));const r=mf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var cI=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const hI={srcMin:0,srcMax:1,destMin:0,destMax:1};class uI extends LP{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return hI[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Cf(this,cI)]}}const dI={srcMin:0,srcMax:1};class pI extends CP{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return dI[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Cf(this,cI)]}}const _I={destMin:0,destMax:1};class mI extends CP{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return _I[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Cf(this,cI)]}}const fI={center:.5,variance:.5};class gI extends CP{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return fI[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Cf(this,cI)]}}const vI=\\\\\\\"color\\\\\\\";const yI=new class extends ua{constructor(){super(...arguments),this.mvPosition=ha.VECTOR4([0,0,0,0]),this.baseColor=ha.COLOR([0,0,0]),this.fogColor=ha.COLOR([1,1,1]),this.near=ha.FLOAT(0),this.far=ha.FLOAT(0)}};class xI extends gf{constructor(){super(...arguments),this.paramsConfig=yI}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(vI,Uo.VEC3)])}setLines(t){if(t.current_shader_name==Mf.FRAGMENT){const e=this.glVarName(this.name()),n=new Lf(this,Uo.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],Mf.VERTEX),t.addBodyLines(this,[i],Mf.VERTEX);const s=new Cf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),r=mf.vector4(this.variableForInputParam(this.p.mvPosition)),o=mf.vector3(this.variableForInputParam(this.p.baseColor)),a=mf.vector3(this.variableForInputParam(this.p.fogColor)),l=mf.vector3(this.variableForInputParam(this.p.near)),c=mf.vector3(this.variableForInputParam(this.p.far)),h=`vec3 ${this.glVarName(vI)} = compute_fog(${[r,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,s]),t.addBodyLines(this,[h])}}}const bI=new class extends ua{};class wI extends gf{constructor(){super(...arguments),this.paramsConfig=bI}static type(){return ns.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),s=mf.any(this.variableForInput(i.name())),r=`\\\\t${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class TI extends gf{constructor(){super(...arguments),this._children_controller_context=ts.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(wI.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Uo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const e=t.dest_connection_point(),i=mf.any(this.variableForInput(e.name())),s=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,l=mf.float(r),c=mf.float(o),h=mf.float(a),u=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${u} = ${l}; ${u} < ${c}; ${u}+= ${h}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(EI.START_INDEX)} = ${u}`;if(n.push(p),s)for(let t of s)if(t&&t.input_index>=0){const i=t.dest_connection_point(),s=this.glVarName(i.name()),r=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(e,n)}setLines(t){}}const LI=new class extends ua{};class OI extends gf{constructor(){super(...arguments),this.paramsConfig=LI}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const PI=new class extends ua{constructor(){super(...arguments),this.hsluv=ha.VECTOR3([1,1,1])}};class RI extends gf{constructor(){super(...arguments),this.paramsConfig=PI}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"rgb\\\\\\\",Uo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Cf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214 ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786 );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4 xyzToRgb(vec4 c) {return vec4( xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToXyz(vec4 c) {return vec4( rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 xyzToLuv(vec4 c) {return vec4( xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToXyz(vec4 c) {return vec4( luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToLch(vec4 c) {return vec4( luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToLuv(vec4 c) {return vec4( lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToRgb(vec4 c) {return vec4( lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToLch(vec4 c) {return vec4( rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToRgb(vec4 c) {return vec4( luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3 xyzToRgb(float x, float y, float z) {return xyzToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToXyz(float x, float y, float z) {return rgbToXyz( vec3(x,y,z) );}\\\\nvec3 xyzToLuv(float x, float y, float z) {return xyzToLuv( vec3(x,y,z) );}\\\\nvec3 luvToXyz(float x, float y, float z) {return luvToXyz( vec3(x,y,z) );}\\\\nvec3 luvToLch(float x, float y, float z) {return luvToLch( vec3(x,y,z) );}\\\\nvec3 lchToLuv(float x, float y, float z) {return lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3 lchToRgb(float x, float y, float z) {return lchToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToLch(float x, float y, float z) {return rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3 luvToRgb(float x, float y, float z) {return luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4 xyzToRgb(float x, float y, float z, float a) {return xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToXyz(float x, float y, float z, float a) {return rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4 xyzToLuv(float x, float y, float z, float a) {return xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4 luvToXyz(float x, float y, float z, float a) {return luvToXyz( vec4(x,y,z,a) );}\\\\nvec4 luvToLch(float x, float y, float z, float a) {return luvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToLuv(float x, float y, float z, float a) {return lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4 lchToRgb(float x, float y, float z, float a) {return lchToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToLch(float x, float y, float z, float a) {return rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4 luvToRgb(float x, float y, float z, float a) {return luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=mf.vector3(this.variableForInputParam(this.p.hsluv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const II=new class extends ua{constructor(){super(...arguments),this.hsv=ha.VECTOR3([1,1,1])}};class FI extends gf{constructor(){super(...arguments),this.paramsConfig=II}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"rgb\\\\\\\",Uo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Cf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=mf.vector3(this.variableForInputParam(this.p.hsv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const DI=\\\\\\\"condition\\\\\\\";const BI=new class extends ua{};class zI extends MI{constructor(){super(...arguments),this.paramsConfig=BI}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Uo.BOOL],e=Uo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=1;s[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.FLOAT;return[t,t,Uo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const eF=\\\\\\\"mvMult\\\\\\\";const nF=new class extends ua{constructor(){super(...arguments),this.vector=ha.VECTOR3([0,0,0])}};class iF extends gf{constructor(){super(...arguments),this.paramsConfig=nF}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(eF,Uo.VEC4)])}setLines(t){if(t.current_shader_name==Mf.VERTEX){const e=mf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(eF)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],Mf.VERTEX)}}}const sF={mult:1};var rF;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(rF||(rF={}));class oF extends NP{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[rF.VALUE,rF.PRE_ADD,rF.MULT,rF.POST_ADD][t]}paramDefaultValue(t){return sF[t]}setLines(t){const e=mf.any(this.variableForInput(rF.VALUE)),n=mf.any(this.variableForInput(rF.PRE_ADD)),i=mf.any(this.variableForInput(rF.MULT)),s=mf.any(this.variableForInput(rF.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${s}`;t.addBodyLines(this,[a])}}class aF extends EP{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=mf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var lF;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(lF||(lF={}));const cF=[lF.CLASSIC_PERLIN_2D,lF.CLASSIC_PERLIN_3D,lF.CLASSIC_PERLIN_4D,lF.NOISE_2D,lF.NOISE_3D,lF.NOISE_4D],hF={[lF.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n',[lF.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n vec3 Pi0 = floor(P); // Integer part for indexing\\\\n vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n',[lF.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n vec4 Pi0 = floor(P); // Integer part for indexing\\\\n vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n',[lF.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n {\\\\n const vec4 C = vec4(0.211324865405187, // (3.0-sqrt(3.0))/6.0\\\\n 0.366025403784439, // 0.5*(sqrt(3.0)-1.0)\\\\n -0.577350269189626, // -1.0 + 2.0 * C.x\\\\n 0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n vec2 i = floor(v + dot(v, C.yy) );\\\\n vec2 x0 = v - i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n vec2 i1;\\\\n //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n //i1.y = 1.0 - i1.x;\\\\n i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n vec4 x12 = x0.xyxy + C.xxzz;\\\\n x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n i = mod289(i); // Avoid truncation effects in permutation\\\\n vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n m = m*m ;\\\\n m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n vec3 h = abs(x) - 0.5;\\\\n vec3 ox = floor(x + 0.5);\\\\n vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n vec3 g;\\\\n g.x = a0.x * x0.x + h.x * x0.y;\\\\n g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[lF.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n { \\\\n const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n vec3 i = floor(v + dot(v, C.yyy) );\\\\n vec3 x0 = v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n vec3 g = step(x0.yzx, x0.xyz);\\\\n vec3 l = 1.0 - g;\\\\n vec3 i1 = min( g.xyz, l.zxy );\\\\n vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n // x1 = x0 - i1 + 1.0 * C.xxx;\\\\n // x2 = x0 - i2 + 2.0 * C.xxx;\\\\n // x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n vec3 x1 = x0 - i1 + C.xxx;\\\\n vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n vec3 x3 = x0 - D.yyy; // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n vec4 p = permute( permute( permute( \\\\n i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n float n_ = 0.142857142857; // 1.0/7.0\\\\n vec3 ns = n_ * D.wyz - D.xzx;\\\\n\\\\n vec4 j = p - 49.0 * floor(p * ns.z * ns.z); // mod(p,7*7)\\\\n\\\\n vec4 x_ = floor(j * ns.z);\\\\n vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\\\\n\\\\n vec4 x = x_ *ns.x + ns.yyyy;\\\\n vec4 y = y_ *ns.x + ns.yyyy;\\\\n vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n vec4 b0 = vec4( x.xy, y.xy );\\\\n vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n vec3 p0 = vec3(a0.xy,h.x);\\\\n vec3 p1 = vec3(a0.zw,h.y);\\\\n vec3 p2 = vec3(a1.xy,h.z);\\\\n vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n m = m * m;\\\\n return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n dot(p2,x2), dot(p3,x3) ) );\\\\n }\\\\n\\\\\\\",[lF.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n {\\\\n const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n vec4 p,s;\\\\n\\\\n p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n s = vec4(lessThan(p, vec4(0.0)));\\\\n p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n return p;\\\\n }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n {\\\\n const vec4 C = vec4( 0.138196601125011, // (5 - sqrt(5))/20 G4\\\\n 0.276393202250021, // 2 * G4\\\\n 0.414589803375032, // 3 * G4\\\\n -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n vec4 i = floor(v + dot(v, vec4(F4)) );\\\\n vec4 x0 = v - i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n vec4 i0;\\\\n vec3 isX = step( x0.yzw, x0.xxx );\\\\n vec3 isYZ = step( x0.zww, x0.yyz );\\\\n// i0.x = dot( isX, vec3( 1.0 ) );\\\\n i0.x = isX.x + isX.y + isX.z;\\\\n i0.yzw = 1.0 - isX;\\\\n// i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n i0.y += isYZ.x + isYZ.y;\\\\n i0.zw += 1.0 - isYZ.xy;\\\\n i0.z += isYZ.z;\\\\n i0.w += 1.0 - isYZ.z;\\\\n\\\\n // i0 now contains the unique values 0,1,2,3 in each channel\\\\n vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n // x1 = x0 - i1 + 1.0 * C.xxxx\\\\n // x2 = x0 - i2 + 2.0 * C.xxxx\\\\n // x3 = x0 - i3 + 3.0 * C.xxxx\\\\n // x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n vec4 x1 = x0 - i1 + C.xxxx;\\\\n vec4 x2 = x0 - i2 + C.yyyy;\\\\n vec4 x3 = x0 - i3 + C.zzzz;\\\\n vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n vec4 j1 = permute( permute( permute( permute (\\\\n i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n vec4 p0 = grad4(j0, ip);\\\\n vec4 p1 = grad4(j1.x, ip);\\\\n vec4 p2 = grad4(j1.y, ip);\\\\n vec4 p3 = grad4(j1.z, ip);\\\\n vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4) ), 0.0);\\\\n m0 = m0 * m0;\\\\n m1 = m1 * m1;\\\\n return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n }\\\\n\\\\\\\"},uF={[lF.CLASSIC_PERLIN_2D]:Uo.VEC2,[lF.CLASSIC_PERLIN_3D]:Uo.VEC3,[lF.CLASSIC_PERLIN_4D]:Uo.VEC4,[lF.NOISE_2D]:Uo.VEC2,[lF.NOISE_3D]:Uo.VEC3,[lF.NOISE_4D]:Uo.VEC4},dF={[lF.CLASSIC_PERLIN_2D]:Uo.FLOAT,[lF.CLASSIC_PERLIN_3D]:Uo.FLOAT,[lF.CLASSIC_PERLIN_4D]:Uo.FLOAT,[lF.NOISE_2D]:Uo.FLOAT,[lF.NOISE_3D]:Uo.FLOAT,[lF.NOISE_4D]:Uo.FLOAT},pF={[lF.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[lF.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[lF.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[lF.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[lF.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[lF.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var _F;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(_F||(_F={}));const mF=[_F.NoChange,_F.Float,_F.Vec2,_F.Vec3,_F.Vec4],fF={[_F.NoChange]:\\\\\\\"Same as noise\\\\\\\",[_F.Float]:\\\\\\\"Float\\\\\\\",[_F.Vec2]:\\\\\\\"Vec2\\\\\\\",[_F.Vec3]:\\\\\\\"Vec3\\\\\\\",[_F.Vec4]:\\\\\\\"Vec4\\\\\\\"},gF={[_F.NoChange]:Uo.FLOAT,[_F.Float]:Uo.FLOAT,[_F.Vec2]:Uo.VEC2,[_F.Vec3]:Uo.VEC3,[_F.Vec4]:Uo.VEC4},vF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],yF=\\\\\\\"noise\\\\\\\",xF=cF.indexOf(lF.NOISE_3D),bF=_F.NoChange,wF={amp:1,freq:1};var TF;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(TF||(TF={}));const AF=new class extends ua{constructor(){super(...arguments),this.type=ha.INTEGER(xF,{menu:{entries:cF.map(((t,e)=>({name:`${t} (output: ${dF[t]})`,value:e})))}}),this.outputType=ha.INTEGER(bF,{menu:{entries:mF.map((t=>{const e=mF[t];return{name:fF[e],value:e}}))}}),this.octaves=ha.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=ha.FLOAT(.5,{range:[0,1]}),this.freqIncrease=ha.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class MF extends gf{constructor(){super(...arguments),this.paramsConfig=AF}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new qo(yF,Uo.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>yF))}_gl_input_name(t){return[TF.AMP,TF.POSITION,TF.FREQ,TF.OFFSET][t]}paramDefaultValue(t){return wF[t]}_expected_input_types(){const t=cF[this.pv.type],e=this._expected_output_types()[0],n=uF[t];return[e,n,n,n]}_expected_output_types(){const t=cF[this.pv.type],e=mF[this.pv.outputType];return e==_F.NoChange?[uF[t]]:[gF[e]]}setLines(t){const e=[],n=[],i=cF[this.pv.type],s=hF[i],r=dF[i];e.push(new Cf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Cf(this,s)),e.push(new Cf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==r){const t=this.single_noise_line();n.push(t)}else{const t=Wo[o],e=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;rmf.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const r=`vec${t} ${s} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(r)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=cF[this.pv.type];return`fbm_${pF[t]}_${this.name()}`}fbm_function(){const t=cF[this.pv.type],e=pF[t],n=uF[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${mf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${mf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${mf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),s=mf.any(this.variableForInput(TF.AMP)),r=mf.any(this.variableForInput(TF.POSITION)),o=mf.any(this.variableForInput(TF.FREQ));let a=mf.any(this.variableForInput(TF.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(yF),h=`${s}*${i}(${l})`;if(e)return`float ${c}${t} = (${h}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${h}`}}class EF extends EP{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=mf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const SF=new class extends ua{};class CF extends gf{constructor(){super(...arguments),this.paramsConfig=SF}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class NF{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const LF=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(Go.indexOf(Uo.FLOAT),{menu:{entries:Go.map(((t,e)=>({name:t,value:e})))}}),this.asColor=ha.BOOLEAN(0,{visibleIf:{type:Go.indexOf(Uo.VEC3)}})}};class OF extends gf{constructor(){super(...arguments),this.paramsConfig=LF,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[Go[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=Go[this.pv.type],i=this.uniform_name();e.push(new Nf(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=Go[this.pv.type],e=jo[t];let n=Vo[t];if(this._param_configs_controller=this._param_configs_controller||new NF,this._param_configs_controller.reset(),n==Cr.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new tg(Cr.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new tg(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=Go.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class PF extends MP{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Uo.VEC3;return[t,t,Uo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const RF=\\\\\\\"SSSModel\\\\\\\";const IF=new class extends ua{constructor(){super(...arguments),this.color=ha.COLOR([1,1,1]),this.thickness=ha.FLOAT(.1),this.power=ha.FLOAT(2),this.scale=ha.FLOAT(16),this.distortion=ha.FLOAT(.1),this.ambient=ha.FLOAT(.4),this.attenuation=ha.FLOAT(.8)}};class FF extends gf{constructor(){super(...arguments),this.paramsConfig=IF}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(RF,Uo.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(RF);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${mf.vector3(this.variableForInputParam(e))};`}}class DF extends EP{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Uo.VEC4,Uo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Uo.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Cf(this,NR)]}}var BF;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}(BF||(BF={}));const zF=[BF.AXIS,BF.ANGLE],kF={[BF.AXIS]:[0,0,1],[BF.ANGLE]:0};class UF extends SP{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>zF[t])),this.io.connection_points.set_expected_input_types_function((()=>[Uo.VEC3,Uo.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Uo.VEC4]))}paramDefaultValue(t){return kF[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Cf(this,NR)]}}class GF extends EP{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Uo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Uo.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Cf(this,NR)]}}class VF extends EP{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Uo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Uo.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Cf(this,NR)]}}const HF=\\\\\\\"val\\\\\\\";const jF=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"ramp\\\\\\\"),this.input=ha.FLOAT(0)}};class WF extends gf{constructor(){super(...arguments),this.paramsConfig=jF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new qo(HF,Uo.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Uo.VEC3,n=this.glVarName(\\\\\\\"tmpTexureVarName\\\\\\\"),i=Uo.FLOAT,s=this._uniform_name(),r=this.glVarName(HF),o=new Nf(this,Uo.SAMPLER_2D,s);t.addDefinitions(this,[o]);const a=this.variableForInputParam(this.p.input),l=[`${e} ${n} = texture2D(${this._uniform_name()}, vec2(${a}, 0.0)).xyz`,`${i} ${r} = -1.0 + ${n}.x + ${n}.y + ${n}.z`];t.addBodyLines(this,l)}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new NF,this._param_configs_controller.reset();const t=new tg(Cr.RAMP,this.pv.name,Ao.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(HF)}}const qF=\\\\\\\"rand\\\\\\\";const XF=new class extends ua{constructor(){super(...arguments),this.seed=ha.VECTOR2([1,1])}};class YF extends gf{constructor(){super(...arguments),this.paramsConfig=XF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(qF,Uo.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=mf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(qF)} = rand(${n})`;t.addBodyLines(this,[i])}}const $F=new class extends ua{constructor(){super(...arguments),this.rgb=ha.VECTOR3([1,1,1])}};class JF extends gf{constructor(){super(...arguments),this.paramsConfig=$F}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"hsv\\\\\\\",Uo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Cf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=mf.vector3(this.variableForInputParam(this.p.rgb)),s=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${s} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var ZF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(ZF||(ZF={}));const QF=[ZF.AXIS,ZF.QUAT],KF={[ZF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[ZF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},tD={[ZF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[ZF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},eD={[ZF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[ZF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},nD={[ZF.AXIS]:[Uo.VEC3,Uo.VEC3,Uo.FLOAT],[ZF.QUAT]:[Uo.VEC3,Uo.VEC4]},iD={vector:[0,0,1],axis:[0,1,0]};const sD=new class extends ua{constructor(){super(...arguments),this.signature=ha.INTEGER(ZF.AXIS,{menu:{entries:QF.map(((t,e)=>({name:KF[t],value:e})))}})}};class rD extends gf{constructor(){super(...arguments),this.paramsConfig=sD}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=QF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=QF[this.pv.signature];return tD[e][t]}paramDefaultValue(t){return iD[t]}gl_method_name(){const t=QF[this.pv.signature];return eD[t]}_expected_input_types(){const t=QF[this.pv.signature];return nD[t]}_expected_output_types(){return[Uo.VEC3]}gl_function_definitions(){return[new Cf(this,NR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return mf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const oD=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class aD extends EP{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=mf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==Wo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const t=oD.map((t=>this._simple_line(`${n}.${t}`)));r.push(`${i.type()} ${s} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,r)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const lD=new class extends ua{constructor(){super(...arguments),this.position=ha.VECTOR3([0,0,0]),this.center=ha.VECTOR3([0,0,0]),this.radius=ha.FLOAT(1),this.feather=ha.FLOAT(.1)}};class cD extends gf{constructor(){super(...arguments),this.paramsConfig=lD}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"float\\\\\\\",Uo.FLOAT)])}setLines(t){const e=mf.vector2(this.variableForInputParam(this.p.position)),n=mf.vector2(this.variableForInputParam(this.p.center)),i=mf.float(this.variableForInputParam(this.p.radius)),s=mf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Cf(this,QR)])}}const hD=new class extends ua{};class uD extends gf{constructor(){super(...arguments),this.paramsConfig=hD}static type(){return ns.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const dD=new class extends ua{};class pD extends gf{constructor(){super(...arguments),this.paramsConfig=dD}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?pD.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Uo.FLOAT,e=this.io.connections.inputConnections(),n=e?or.clamp(e.length,2,16):2,i=[Uo.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=mf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(mD.OUTPUT_NAME),i=this._uniform_name(),s=new Nf(this,Uo.SAMPLER_2D,i),r=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[s]),t.addBodyLines(this,[r])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new NF,this._param_configs_controller.reset();const t=new tg(Cr.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var fD;mD.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(fD||(fD={}));const gD=[fD.POSITION,fD.DIR_VEC];const vD=new class extends ua{constructor(){super(...arguments),this.vec=ha.VECTOR3([0,0,0]),this.interpretation=ha.INTEGER(0,{menu:{entries:gD.map(((t,e)=>({name:t,value:e})))}})}};class yD extends gf{constructor(){super(...arguments),this.paramsConfig=vD}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"out\\\\\\\",Uo.VEC3)])}setLines(t){const e=[],n=mf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(gD[this.pv.interpretation]){case fD.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case fD.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var xD;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(xD||(xD={}));const bD=[xD.CONDITION,xD.IF_TRUE,xD.IF_FALSE];class wD extends yf{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return bD[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Uo.FLOAT;return[Uo.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=mf.bool(this.variableForInput(xD.CONDITION)),s=mf.any(this.variableForInput(xD.IF_TRUE)),r=mf.any(this.variableForInput(xD.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${s}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${r}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const TD=[Uo.FLOAT,Uo.VEC2,Uo.VEC3,Uo.VEC4];const AD=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(0,{menu:{entries:TD.map(((t,e)=>({name:t,value:e})))}})}};class MD extends gf{constructor(){super(...arguments),this.paramsConfig=AD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[TD[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return MD.OUTPUT_NAME}setLines(t){if(t.current_shader_name==Mf.FRAGMENT){const e=this.pv.name,n=new Lf(this,this.gl_type(),e),i=this.glVarName(MD.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(TD.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}MD.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const ED={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class SD extends(aR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[NR]})){_expected_input_types(){const t=Uo.VEC3;return[t,t,t]}_expected_output_types(){return[Uo.VEC4]}paramDefaultValue(t){return ED[t]}}const CD={start:[0,0,1],end:[1,0,0]};class ND extends(QP(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[NR]})){_expected_input_types(){const t=Uo.VEC3;return[t,t]}_expected_output_types(){return[Uo.FLOAT]}paramDefaultValue(t){return CD[t]}}const LD={only:[`${zI.context()}/${zI.type()}`,`${MI.context()}/${MI.type()}`,`${NI.context()}/${NI.type()}`]};class OD extends aa{static context(){return ts.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends ua{};const PD=[Xo.FLOAT,Xo.VEC2,Xo.VEC3,Xo.VEC4];const RD=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(0,{menu:{entries:PD.map(((t,e)=>({name:t,value:e})))}})}};class ID extends OD{constructor(){super(...arguments),this.paramsConfig=RD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[PD[this.pv.type]]))}get input_name(){return ID.INPUT_NAME}get output_name(){return ID.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(PD.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(ID.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(ID.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}ID.INPUT_NAME=\\\\\\\"export\\\\\\\",ID.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const FD=new class extends ua{};class DD extends OD{constructor(){super(...arguments),this.paramsConfig=FD}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const BD=new class extends ua{};class zD extends OD{constructor(){super(...arguments),this.paramsConfig=BD}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class kD{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var UD;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(UD||(UD={}));class GD{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new kD}}class VD extends GD{constructor(t,e,n){super(UD.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class HD extends Kf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Cr.BOOLEAN:case Cr.BUTTON:return{value:0};case Cr.COLOR:return{value:new D.a(0,0,0)};case Cr.FLOAT:case Cr.FOLDER:case Cr.INTEGER:case Cr.OPERATOR_PATH:case Cr.NODE_PATH:case Cr.PARAM_PATH:return{value:0};case Cr.RAMP:case Cr.STRING:return{value:null};case Cr.VECTOR2:return{value:new d.a(0,0)};case Cr.VECTOR3:return{value:new p.a(0,0,0)};case Cr.VECTOR4:return{value:new _.a(0,0,0,0)}}os.unreachable(t)}}const jD=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(\\\\\\\"\\\\\\\"),this.type=ha.INTEGER(Yo.indexOf(Xo.FLOAT),{menu:{entries:Yo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=ha.BOOLEAN(0,{visibleIf:{type:Yo.indexOf(Xo.VEC3)}})}};class WD extends OD{constructor(){super(...arguments),this.paramsConfig=jD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[Yo[this.pv.type]]))}setLines(t){const e=[],n=Yo[this.pv.type],i=this.uniform_name();e.push(new VD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=Yo[this.pv.type],e=Zo[t];let n=$o[t];if(this._param_configs_controller=this._param_configs_controller||new NF,this._param_configs_controller.reset(),n==Cr.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new HD(Cr.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new HD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=Yo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class qD extends aa{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return ts.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class XD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const YD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},$D=Object.keys(YD);function JD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=ha.BOOLEAN(0),this.front=ha.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=ha.BOOLEAN(0),this.shadowDoubleSided=ha.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=ha.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=ha.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{ZD.update(t)}}),this.depthWrite=ha.BOOLEAN(1,{cook:!1,callback:(t,e)=>{ZD.update(t)}}),this.depthTest=ha.BOOLEAN(1,{cook:!1,callback:(t,e)=>{ZD.update(t)}}),this.premultipliedAlpha=ha.BOOLEAN(!1,{separatorAfter:!0}),this.blending=ha.INTEGER(w.xb,{menu:{entries:$D.map((t=>({name:t,value:YD[t]})))}}),this.dithering=ha.BOOLEAN(0),this.polygonOffset=ha.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=ha.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=ha.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}JD(ua);class ZD extends XD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let n of t){const t=s[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class QD extends(JD(ua)){constructor(){super(...arguments),this.color=ha.COLOR([1,1,1]),this.lineWidth=ha.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const KD=new QD;class tB extends qD{constructor(){super(...arguments),this.paramsConfig=KD,this.controllers={advancedCommon:new ZD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new As.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function eB(t){return class extends t{constructor(){super(...arguments),this.transparent=ha.BOOLEAN(0),this.opacity=ha.FLOAT(1),this.alphaTest=ha.FLOAT(0)}}}eB(ua);class nB extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class iB extends Qf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]);const r=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});r||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:r||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),s=this._material.uniforms[n],r=Object.keys(this._material.customMaterials);let o;for(let t of r){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(s||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(s&&tg.callback(i,s),o)for(let t of o)tg.callback(i,t)}))}}}material(){if(li.playerMode())return this._material}}function sB(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=ha.BOOLEAN(0,{callback:t=>{rB.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{rB.PARAM_CALLBACK_setCompileRequired(t)}})}}}sB(ua);class rB extends qD{constructor(){super(...arguments),this._children_controller_context=ts.GL,this.persisted_config=new iB(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(ts.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function oB(t){return class extends t{constructor(){super(...arguments),this.useFog=ha.BOOLEAN(0)}}}oB(ua);class aB extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function lB(t){return class extends t{constructor(){super(...arguments),this.default=ha.FOLDER(null)}}}function cB(t){return class extends t{constructor(){super(...arguments),this.advanced=ha.FOLDER(null)}}}class hB extends(oB(JD(sB(cB(eB(lB(ua))))))){constructor(){super(...arguments),this.linewidth=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const uB=new hB;class dB extends rB{constructor(){super(...arguments),this.paramsConfig=uB,this.controllers={advancedCommon:new ZD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_LINE}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function pB(t){return class extends t{constructor(){super(...arguments),this.color=ha.COLOR([1,1,1],{conversion:lo.SRGB_TO_LINEAR}),this.useVertexColors=ha.BOOLEAN(0,{separatorAfter:!0}),this.transparent=ha.BOOLEAN(0),this.opacity=ha.FLOAT(1),this.alphaTest=ha.FLOAT(0)}}}O.a;pB(ua);class _B extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function mB(t){return class extends t{constructor(){super(...arguments),this.useFog=ha.BOOLEAN(0)}}}mB(ua);class fB extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function gB(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function vB(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:ts.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class yB extends XD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const s=t,r=e;await this._update_texture_on_uniforms(s,r,n,i)}if(this._update_options.directParams){const s=t,r=e;await this._update_texture_on_material(s,r,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const s=null!=e[n]&&null!=e[n].value;let r=!1;if(s){e[n].value.uuid!=i.uuid&&(r=!0)}if(!s||r){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let e of t){const t=s[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else li.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const s=null!=e[n];let r=!1;if(s){e[n].uuid!=i.uuid&&(r=!0)}s&&!r||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,s,r,o){i.isDirty()&&await i.compute();if(i.value){s.isDirty()&&await s.compute();const i=s.value.nodeWithContext(ts.COP);if(i){const s=(await i.compute()).texture();if(s)return void r(t,e,n,s)}}o(t,e,n)}}function xB(t){return class extends t{constructor(){super(...arguments),this.useMap=ha.BOOLEAN(0,gB(bB)),this.map=ha.NODE_PATH(vi.EMPTY,vB(bB,\\\\\\\"useMap\\\\\\\"))}}}O.a;xB(ua);class bB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function wB(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(TB)}),this.alphaMap=ha.NODE_PATH(vi.EMPTY,vB(TB,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;wB(ua);class TB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function AB(t){return class extends t{constructor(){super(...arguments),this.useAOMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(MB)}),this.aoMap=ha.NODE_PATH(vi.EMPTY,vB(MB,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;AB(ua);class MB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var EB;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(EB||(EB={}));const SB=[EB.MULT,EB.ADD,EB.MIX],CB={[EB.MULT]:w.nb,[EB.ADD]:w.c,[EB.MIX]:w.lb};function NB(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=ha.BOOLEAN(0,gB(LB)),this.envMap=ha.NODE_PATH(vi.EMPTY,vB(LB,\\\\\\\"useEnvMap\\\\\\\",{types:[Fg.CUBE_CAMERA]})),this.combine=ha.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:SB.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=ha.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=ha.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}NB(ua);class LB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=CB[SB[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function OB(t){return class extends t{constructor(){super(...arguments),this.useLightMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(PB)}),this.lightMap=ha.NODE_PATH(vi.EMPTY,vB(PB,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=ha.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;OB(ua);class PB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var RB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(RB||(RB={}));const IB=[RB.ROUND,RB.BUTT,RB.SQUARE];var FB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(FB||(FB={}));const DB=[FB.ROUND,FB.BEVEL,FB.MITER];function BB(t){return class extends t{constructor(){super(...arguments),this.wireframe=ha.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=ha.INTEGER(0,{menu:{entries:IB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=ha.INTEGER(0,{menu:{entries:DB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;BB(ua);class zB extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=IB[n.wireframeLinecap],e.wireframeLinejoin=DB[n.wireframeLinejoin],e.needsUpdate=!0}}function kB(t){return class extends t{constructor(){super(...arguments),this.textures=ha.FOLDER(null)}}}const UB={directParams:!0};class GB extends(mB(BB(JD(cB(OB(NB(AB(wB(xB(kB(pB(lB(ua))))))))))))){}const VB=new GB;class HB extends qD{constructor(){super(...arguments),this.paramsConfig=VB,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,UB),aoMap:new MB(this,UB),envMap:new LB(this,UB),lightMap:new PB(this,UB),map:new bB(this,UB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new lt.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.setMaterial(this.material)}}function jB(t){return class extends t{constructor(){super(...arguments),this.wireframe=ha.BOOLEAN(0)}}}jB(ua);class WB extends XD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const qB={uniforms:!0};class XB extends(oB(jB(JD(sB(cB(NB(AB(wB(xB(kB(eB(lB(ua))))))))))))){}const YB=new XB;class $B extends rB{constructor(){super(...arguments),this.paramsConfig=YB,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,qB),aoMap:new MB(this,qB),envMap:new LB(this,qB),map:new bB(this,qB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_BASIC}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),WB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function JB(t){return class extends t{constructor(){super(...arguments),this.emissive=ha.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=ha.BOOLEAN(0,gB(ZB)),this.emissiveMap=ha.NODE_PATH(vi.EMPTY,vB(ZB,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=ha.FLOAT(1)}}}O.a;JB(ua);class ZB extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const QB={directParams:!0};class KB extends(mB(BB(JD(cB(OB(NB(JB(AB(wB(xB(kB(pB(lB(ua)))))))))))))){}const tz=new KB;class ez extends qD{constructor(){super(...arguments),this.paramsConfig=tz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,QB),aoMap:new MB(this,QB),emissiveMap:new ZB(this,QB),envMap:new LB(this,QB),lightMap:new PB(this,QB),map:new bB(this,QB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new Ts.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.setMaterial(this.material)}}function nz(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=ha.BOOLEAN(0,{callback:t=>{iz.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=ha.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=ha.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}nz(ua);class iz extends XD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${mf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${mf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=z;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include \\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>iz.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const sz={uniforms:!0};class rz extends(nz(oB(jB(JD(sB(cB(OB(NB(JB(AB(wB(xB(kB(eB(lB(ua)))))))))))))))){}const oz=new rz;class az extends rB{constructor(){super(...arguments),this.paramsConfig=oz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,sz),aoMap:new MB(this,sz),emissiveMap:new ZB(this,sz),envMap:new LB(this,sz),lightMap:new PB(this,sz),map:new bB(this,sz),PCSS:new iz(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_LAMBERT}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),WB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function lz(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(cz)}),this.bumpMap=ha.NODE_PATH(\\\\\\\"\\\\\\\",vB(cz,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...vB(cz,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=ha.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...vB(cz,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;lz(ua);class cz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var hz;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(hz||(hz={}));const uz=[hz.TANGENT,hz.OBJECT],dz={[hz.TANGENT]:w.Uc,[hz.OBJECT]:w.zb};function pz(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(_z)}),this.normalMap=ha.NODE_PATH(vi.EMPTY,vB(_z,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=ha.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:uz.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=ha.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;pz(ua);class _z extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=dz[uz[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function mz(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(fz)}),this.displacementMap=ha.NODE_PATH(\\\\\\\"\\\\\\\",vB(fz,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...vB(fz,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=ha.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...vB(fz,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;mz(ua);class fz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function gz(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=ha.BOOLEAN(0,gB(vz)),this.matcapMap=ha.NODE_PATH(vi.EMPTY,vB(vz,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;gz(ua);class vz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const yz={directParams:!0};class xz extends(mB(JD(cB(pz(mz(lz(wB(xB(gz(kB(pB(lB(ua))))))))))))){}const bz=new xz;class wz extends qD{constructor(){super(...arguments),this.paramsConfig=bz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,yz),bumpMap:new cz(this,yz),displacementMap:new fz(this,yz),map:new bB(this,yz),matcap:new vz(this,yz),normalMap:new _z(this,yz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new $f({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),this.setMaterial(this.material)}}const Tz={directParams:!0};class Az extends(mB(JD(pz(mz(lz(kB(lB(ua)))))))){}const Mz=new Az;class Ez extends qD{constructor(){super(...arguments),this.paramsConfig=Mz,this.controllers={advancedCommon:new ZD(this),bumpMap:new cz(this,Tz),displacementMap:new fz(this,Tz),normalMap:new _z(this,Tz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshNormal\\\\\\\"}createMaterial(){return new Yf({vertexColors:!1,side:w.H,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();fB.update(this),this.setMaterial(this.material)}}function Sz(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=ha.BOOLEAN(0,gB(Cz)),this.specularMap=ha.NODE_PATH(vi.EMPTY,vB(Cz,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;Sz(ua);class Cz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const Nz={directParams:!0};class Lz extends(mB(BB(JD(cB(Sz(pz(OB(NB(JB(mz(lz(AB(wB(xB(kB(pB(lB(ua)))))))))))))))))){constructor(){super(...arguments),this.flatShading=ha.BOOLEAN(0)}}const Oz=new Lz;class Pz extends qD{constructor(){super(...arguments),this.paramsConfig=Oz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,Nz),aoMap:new MB(this,Nz),bumpMap:new cz(this,Nz),displacementMap:new fz(this,Nz),emissiveMap:new ZB(this,Nz),envMap:new LB(this,Nz),lightMap:new PB(this,Nz),map:new bB(this,Nz),normalMap:new _z(this,Nz),specularMap:new Cz(this,Nz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new qf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Rz={uniforms:!0};class Iz extends(nz(oB(jB(JD(sB(cB(Sz(pz(OB(NB(JB(mz(lz(AB(wB(xB(kB(eB(lB(ua)))))))))))))))))))){}const Fz=new Iz;class Dz extends rB{constructor(){super(...arguments),this.paramsConfig=Fz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,Rz),aoMap:new MB(this,Rz),bumpMap:new cz(this,Rz),displacementMap:new fz(this,Rz),emissiveMap:new ZB(this,Rz),envMap:new LB(this,Rz),lightMap:new PB(this,Rz),map:new bB(this,Rz),normalMap:new _z(this,Rz),specularMap:new Cz(this,Rz),PCSS:new iz(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHONG}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),WB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Bz(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(zz)}),this.envMap=ha.NODE_PATH(vi.EMPTY,vB(zz,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=ha.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=ha.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Bz(ua);class zz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function kz(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(Uz)}),this.metalnessMap=ha.NODE_PATH(vi.EMPTY,vB(Uz,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=ha.FLOAT(1),this.useRoughnessMap=ha.BOOLEAN(0,{separatorBefore:!0,...gB(Uz)}),this.roughnessMap=ha.NODE_PATH(vi.EMPTY,vB(Uz,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=ha.FLOAT(.5)}}}O.a;kz(ua);class Uz extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function Gz(t){return class extends t{constructor(){super(...arguments),this.clearcoat=ha.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=ha.BOOLEAN(0,gB(Hz)),this.clearcoatMap=ha.NODE_PATH(vi.EMPTY,vB(Hz,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=ha.BOOLEAN(0,gB(Hz)),this.clearcoatNormalMap=ha.NODE_PATH(vi.EMPTY,vB(Hz,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=ha.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=ha.FLOAT(0),this.useClearCoatRoughnessMap=ha.BOOLEAN(0,gB(Hz)),this.clearcoatRoughnessMap=ha.NODE_PATH(vi.EMPTY,vB(Hz,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=ha.BOOLEAN(0),this.sheen=ha.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=ha.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=ha.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=ha.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=ha.BOOLEAN(0),this.transmissionMap=ha.NODE_PATH(vi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.ior=ha.FLOAT(1.5,{range:[1,2.3333],rangeLocked:[!0,!0]}),this.thickness=ha.FLOAT(.01,{range:[0,10],rangeLocked:[!0,!1]}),this.useThicknessMap=ha.BOOLEAN(0),this.thicknessMap=ha.NODE_PATH(vi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=ha.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1]}),this.attenuationColor=ha.COLOR([1,1,1])}}}Gz(ua);const Vz=new Wf.a;class Hz extends yB{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;Vz.ior=t.ior;const e=Vz.reflectivity;if(this._update_options.uniforms){const n=this.node.material;n.uniforms.clearcoat.value=t.clearcoat,n.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),n.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,n.uniforms.reflectivity.value=e,n.uniforms.transmission.value=t.transmission,n.uniforms.thickness.value=t.thickness,n.uniforms.attenuationDistance.value=t.attenuationDistance,n.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.uniforms.sheen.value=t.sheen,n.uniforms.sheenRoughness.value=t.sheenRoughness,n.uniforms.sheenTint.value=this._sheenColorClone):n.uniforms.sheen.value=0,n.uniforms.ior.value=t.ior,n.specularTint=n.uniforms.specularTint.value,n.ior=n.uniforms.ior.value}if(this._update_options.directParams){const n=this.node.material;n.clearcoat=t.clearcoat,null!=n.clearcoatNormalScale&&n.clearcoatNormalScale.copy(t.clearcoatNormalScale),n.clearcoatRoughness=t.clearcoatRoughness,n.reflectivity=e,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.sheen=t.sheen,n.sheenRoughness=t.sheenRoughness,n.sheenTint=this._sheenColorClone):n.sheen=0,n.transmission=t.transmission,n.thickness=t.thickness,n.attenuationDistance=t.attenuationDistance,n.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const jz={directParams:!0};class Wz extends(mB(BB(JD(cB(Gz(kz(pz(OB(Bz(JB(mz(lz(AB(wB(xB(kB(pB(lB(ua))))))))))))))))))){}const qz=new Wz;class Xz extends qD{constructor(){super(...arguments),this.paramsConfig=qz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,jz),aoMap:new MB(this,jz),bumpMap:new cz(this,jz),displacementMap:new fz(this,jz),emissiveMap:new ZB(this,jz),envMap:new zz(this,jz),lightMap:new PB(this,jz),map:new bB(this,jz),metalnessRoughnessMap:new Uz(this,jz),normalMap:new _z(this,jz),physical:new Hz(this,jz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Wf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.setMaterial(this.material)}}const Yz={uniforms:!0};class $z extends(function(t){return class extends(nz(oB(jB(JD(sB(t)))))){}}(cB(Gz(kz(pz(OB(Bz(JB(mz(lz(AB(wB(xB(kB(eB(lB(ua))))))))))))))))){}const Jz=new $z;class Zz extends rB{constructor(){super(...arguments),this.paramsConfig=Jz,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,Yz),aoMap:new MB(this,Yz),bumpMap:new cz(this,Yz),displacementMap:new fz(this,Yz),emissiveMap:new ZB(this,Yz),envMap:new zz(this,{uniforms:!0,directParams:!0}),lightMap:new PB(this,Yz),map:new bB(this,Yz),metalnessRoughnessMap:new Uz(this,{uniforms:!0,directParams:!0}),normalMap:new _z(this,Yz),physical:new Hz(this,{uniforms:!0,directParams:!0}),PCSS:new iz(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHYSICAL}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}createMaterial(){const t=super.createMaterial();return t.isMeshStandardMaterial=!0,t.isMeshPhysicalMaterial=!0,t}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),WB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Qz={directParams:!0};class Kz extends(mB(BB(JD(cB(kz(pz(OB(Bz(JB(mz(lz(AB(wB(xB(kB(pB(lB(ua)))))))))))))))))){}const tk=new Kz;class ek extends qD{constructor(){super(...arguments),this.paramsConfig=tk,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,Qz),aoMap:new MB(this,Qz),bumpMap:new cz(this,Qz),displacementMap:new fz(this,Qz),emissiveMap:new ZB(this,Qz),envMap:new zz(this,Qz),lightMap:new PB(this,Qz),map:new bB(this,Qz),metalnessRoughnessMap:new Uz(this,Qz),normalMap:new _z(this,Qz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new ws.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.setMaterial(this.material)}}const nk={uniforms:!0};class ik extends(nz(oB(jB(JD(sB(cB(kz(pz(OB(Bz(JB(mz(lz(AB(wB(xB(kB(eB(lB(ua)))))))))))))))))))){}const sk=new ik;class rk extends rB{constructor(){super(...arguments),this.paramsConfig=sk,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,nk),aoMap:new MB(this,nk),bumpMap:new cz(this,nk),displacementMap:new fz(this,nk),emissiveMap:new ZB(this,nk),envMap:new zz(this,nk),lightMap:new PB(this,nk),map:new bB(this,nk),metalnessRoughnessMap:new Uz(this,nk),normalMap:new _z(this,nk),PCSS:new iz(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_STANDARD}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),WB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const ok=U.meshphong_frag.slice(0,U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),ak=U.meshphong_frag.slice(U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),lk={uniforms:I.merge([H.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",U.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",ok,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",ak.replace(\\\\\\\"#include \\\\\\\",(ck=U.lights_fragment_begin,hk=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",uk=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),ck.split(hk).join(uk)))].join(\\\\\\\"\\\\n\\\\\\\")};var ck,hk,uk;function dk(t){return{cook:!1,callback:(e,n)=>{gk.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function pk(t){return{cook:!1,callback:(e,n)=>{gk.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const _k={uniforms:!0};class mk extends(mB(jB(JD(cB(wB(xB(kB(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=ha.COLOR([1,1,1],{...dk(\\\\\\\"diffuse\\\\\\\")}),this.shininess=ha.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=ha.NODE_PATH(vi.EMPTY,{nodeSelection:{context:ts.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{gk.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=ha.COLOR([.5,.3,0],{...dk(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=ha.FLOAT(.1,{...pk(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=ha.FLOAT(.4,{...pk(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=ha.FLOAT(.8,{...pk(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=ha.FLOAT(2,{range:[0,10],...pk(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=ha.FLOAT(16,{range:[0,100],...pk(\\\\\\\"thicknessScale\\\\\\\")})}}}(lB(ua)))))))))){}const fk=new mk;class gk extends qD{constructor(){super(...arguments),this.paramsConfig=fk,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,_k),map:new bB(this,_k)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(lk.uniforms),e=new F({uniforms:t,vertexShader:lk.vertexShader,fragmentShader:lk.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();fB.update(this),WB.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(ts.COP);n||(this.material.uniforms[e].value=null);const i=n,s=await i.compute();this.material.uniforms[e].value=s.texture()}}function vk(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=ha.BOOLEAN(0,gB(yk)),this.gradientMap=ha.NODE_PATH(vi.EMPTY,vB(yk,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;vk(ua);class yk extends yB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const xk={directParams:!0};class bk extends(mB(BB(JD(cB(pz(OB(vk(JB(mz(lz(AB(wB(xB(kB(pB(lB(ua))))))))))))))))){}const wk=new bk;class Tk extends qD{constructor(){super(...arguments),this.paramsConfig=wk,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,xk),aoMap:new MB(this,xk),bumpMap:new cz(this,xk),displacementMap:new fz(this,xk),emissiveMap:new ZB(this,xk),gradientMap:new yk(this,xk),lightMap:new PB(this,xk),map:new bB(this,xk),normalMap:new _z(this,xk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Xf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),fB.update(this),zB.update(this),this.setMaterial(this.material)}}const Ak={directParams:!0};class Mk extends(oB(JD(cB(wB(xB(kB(pB(function(t){return class extends t{constructor(){super(...arguments),this.size=ha.FLOAT(1),this.sizeAttenuation=ha.BOOLEAN(1)}}}(lB(ua)))))))))){}const Ek=new Mk;class Sk extends qD{constructor(){super(...arguments),this.paramsConfig=Ek,this.controllers={advancedCommon:new ZD(this),alphaMap:new TB(this,Ak),map:new bB(this,Ak)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new bs.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),aB.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class Ck extends(oB(JD(sB(cB(eB(lB(ua))))))){}const Nk=new Ck;class Lk extends rB{constructor(){super(...arguments),this.paramsConfig=Nk,this.controllers={advancedCommon:new ZD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return jn.GL_POINTS}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();nB.update(this),aB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class Ok extends(JD(pB(ua))){}const Pk=new Ok;class Rk extends qD{constructor(){super(...arguments),this.paramsConfig=Pk,this.controllers={advancedCommon:new ZD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new Hf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_B.update(this),this.setMaterial(this.material)}}class Ik extends B.a{constructor(){const t=Ik.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}Ik.prototype.isSky=!0,Ik.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Fk=new class extends ua{constructor(){super(...arguments),this.turbidity=ha.FLOAT(2,{range:[0,20]}),this.rayleigh=ha.FLOAT(1,{range:[0,4]}),this.mieCoefficient=ha.FLOAT(.005),this.mieDirectional=ha.FLOAT(.8),this.inclination=ha.FLOAT(.5),this.azimuth=ha.FLOAT(.25),this.up=ha.VECTOR3([0,1,0])}};class Dk extends qD{constructor(){super(...arguments),this.paramsConfig=Fk}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new Ik).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var Bk=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include \\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",zk=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const kk={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};function Uk(t){return class extends t{constructor(){super(...arguments),this.color=ha.COLOR([1,1,1]),this.stepSize=ha.FLOAT(.01),this.density=ha.FLOAT(1),this.shadowDensity=ha.FLOAT(1),this.lightDir=ha.VECTOR3([-1,-1,-1])}}}Uk(ua);class Gk{constructor(t){this.node=t}static render_hook(t,e,n,i,s,r,o){if(o){this._object_bbox.setFromObject(o);const t=s;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}Gk._object_bbox=new Cy.a;class Vk extends(Uk(ua)){}const Hk=new Vk;class jk extends qD{constructor(){super(...arguments),this.paramsConfig=Hk,this._volume_controller=new Gk(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:Bk,fragmentShader:zk,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(kk)});return yr.add_user_data_render_hook(t,Gk.render_hook.bind(Gk)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class Wk extends(sB(Uk(ua))){}const qk=new Wk;class Xk extends rB{constructor(){super(...arguments),this.paramsConfig=qk,this._volume_controller=new 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Float32Array(t),i=new Float32Array(e);V.LTC_FLOAT_1=new vo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),V.LTC_FLOAT_2=new vo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const s=new Uint16Array(t.length);t.forEach((function(t,e){s[e]=dU.a.toHalfFloat(t)}));const r=new Uint16Array(e.length);e.forEach((function(t,e){r[e]=dU.a.toHalfFloat(t)})),V.LTC_HALF_1=new vo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),V.LTC_HALF_2=new vo.a(r,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1)}}!function(t){t.OBJECTS=\\\\\\\"objects\\\\\\\",t.GEOMETRIES=\\\\\\\"geometries\\\\\\\"}(uU||(uU={}));const _U=[uU.GEOMETRIES,uU.OBJECTS];var mU;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(mU||(mU={}));const fU=[mU.XYZ,mU.XZY,mU.YXZ,mU.YZX,mU.ZXY,mU.ZYX],gU=mU.XYZ;class vU{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new hh.a,this._translation_matrix_s=new 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set_params_from_object(t,e){t.position.toArray(this.set_params_from_object_position_array),t.rotation.toArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.multiplyScalar(180/Math.PI),this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_object_position_array),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_object_rotation_array)}))}translation_matrix(t){return this._translation_matrix.compose(t,this._translation_matrix_q,this._translation_matrix_s),this._translation_matrix}matrix(t,e,n,i,s){return this._matrix_euler.set(Object(On.e)(e.x),Object(On.e)(e.y),Object(On.e)(e.z),s),this._matrix_q.setFromEuler(this._matrix_euler),this._matrix_s.copy(n).multiplyScalar(i),this._matrix.compose(t,this._matrix_q,this._matrix_s),this._matrix}rotate_geometry(t,e,n){this._rotate_geometry_vec_dest.copy(n),this._rotate_geometry_vec_dest.normalize(),this._rotate_geometry_q.setFromUnitVectors(e,this._rotate_geometry_vec_dest),this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q),t.applyMatrix4(this._rotate_geometry_m)}static decompose_matrix(t){t.matrix.decompose(t.position,t.quaternion,t.scale)}}function yU(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=ha.FOLDER(),this.keepPosWhenParenting=ha.BOOLEAN(0),this.rotationOrder=ha.INTEGER(fU.indexOf(mU.XYZ),{menu:{entries:fU.map(((t,e)=>({name:t,value:e})))}}),this.t=ha.VECTOR3([0,0,0]),this.r=ha.VECTOR3([0,0,0]),this.s=ha.VECTOR3([1,1,1]),this.scale=ha.FLOAT(1),this.matrixAutoUpdate=ha.BOOLEAN(n?1:0),this.updateTransformFromObject=ha.BUTTON(null,{callback:t=>{bU.PARAM_CALLBACK_update_transform_from_object(t)}})}}}vU.set_params_from_matrix_position=new p.a,vU.set_params_from_matrix_quaternion=new hh.a,vU.set_params_from_matrix_scale=new p.a,vU.set_params_from_matrix_euler=new Zv.a,vU.set_params_from_matrix_rotation=new p.a,vU.set_params_from_matrix_t=[0,0,0],vU.set_params_from_matrix_r=[0,0,0],vU.set_params_from_matrix_s=[0,0,0],vU.set_params_from_object_position_array=[0,0,0],vU.set_params_from_object_rotation_deg=new p.a,vU.set_params_from_object_rotation_array=[0,0,0];yU(ua);const xU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class bU{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new vU,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(xU)||this.node.dirtyController.addPostDirtyHook(xU,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,fU[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){vU.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),vU.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),vU.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class wU{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&bU.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){wU.on_input_updated(this.node)}}yU(ua);class TU extends aU{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new wU(this),this.transformController=new bU(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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t.matrixAutoUpdate=!1,pU.initialized||(pU.init(),pU.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var PU=n(72);const RU=new p.a,IU=new of.a;class FU extends Ms.a{constructor(t){const e=new S.a,n=new As.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],s=[],r={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),h=new D.a(3355443);function u(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),s.push(e.r,e.g,e.b),void 0===r[t]&&(r[t]=[]),r[t].push(i.length/3-1)}u(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),u(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),u(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),u(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),u(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),u(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),u(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",h),u(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",h),u(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",h),u(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",h),u(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",h),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(s,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=r,this.update()}update(){const t=this.geometry,e=this.pointMap;IU.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),DU(\\\\\\\"c\\\\\\\",e,t,IU,0,0,-1),DU(\\\\\\\"t\\\\\\\",e,t,IU,0,0,1),DU(\\\\\\\"n1\\\\\\\",e,t,IU,-1,-1,-1),DU(\\\\\\\"n2\\\\\\\",e,t,IU,1,-1,-1),DU(\\\\\\\"n3\\\\\\\",e,t,IU,-1,1,-1),DU(\\\\\\\"n4\\\\\\\",e,t,IU,1,1,-1),DU(\\\\\\\"f1\\\\\\\",e,t,IU,-1,-1,1),DU(\\\\\\\"f2\\\\\\\",e,t,IU,1,-1,1),DU(\\\\\\\"f3\\\\\\\",e,t,IU,-1,1,1),DU(\\\\\\\"f4\\\\\\\",e,t,IU,1,1,1),DU(\\\\\\\"u1\\\\\\\",e,t,IU,.7,1.1,-1),DU(\\\\\\\"u2\\\\\\\",e,t,IU,-.7,1.1,-1),DU(\\\\\\\"u3\\\\\\\",e,t,IU,0,2,-1),DU(\\\\\\\"cf1\\\\\\\",e,t,IU,-1,0,1),DU(\\\\\\\"cf2\\\\\\\",e,t,IU,1,0,1),DU(\\\\\\\"cf3\\\\\\\",e,t,IU,0,-1,1),DU(\\\\\\\"cf4\\\\\\\",e,t,IU,0,1,1),DU(\\\\\\\"cn1\\\\\\\",e,t,IU,-1,0,-1),DU(\\\\\\\"cn2\\\\\\\",e,t,IU,1,0,-1),DU(\\\\\\\"cn3\\\\\\\",e,t,IU,0,-1,-1),DU(\\\\\\\"cn4\\\\\\\",e,t,IU,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function DU(t,e,n,i,s,r,o){RU.set(s,r,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e.9&&o<.1&&(e<.2&&(r[t+0]+=1),n<.2&&(r[t+2]+=1),i<.2&&(r[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(r,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new jU(t.vertices,t.indices,t.radius,t.details)}}class WU extends jU{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new WU(t.radius,t.detail)}}class qU extends SU{constructor(){super(...arguments),this._geometry=new WU(1),this._quat=new hh.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new B.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e0)&&d.push(e,s,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new sG(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class rG extends sG{constructor(t=1,e=1,n=8,i=1,s=!1,r=0,o=2*Math.PI){super(0,t,e,n,i,s,r,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:s,thetaStart:r,thetaLength:o}}static fromJSON(t){return new rG(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class oG{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;iG.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new rG(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new B.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class aG extends(yU(ua)){constructor(){super(...arguments),this.light=ha.FOLDER(),this.color=ha.COLOR([1,1,1],{conversion:lo.SRGB_TO_LINEAR}),this.intensity=ha.FLOAT(1),this.angle=ha.FLOAT(45,{range:[0,180]}),this.penumbra=ha.FLOAT(.1),this.decay=ha.FLOAT(.1,{range:[0,1]}),this.distance=ha.FLOAT(100,{range:[0,100]}),this.showHelper=ha.BOOLEAN(0),this.helperSize=ha.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=ha.FOLDER(),this.castShadow=ha.BOOLEAN(1),this.shadowAutoUpdate=ha.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=ha.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=ha.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=ha.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=ha.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=ha.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=ha.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=ha.FOLDER(),this.tvolumetric=ha.BOOLEAN(0),this.volAttenuation=ha.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=ha.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const lG=new aG;class cG extends TU{constructor(){super(...arguments),this.paramsConfig=lG,this._helperController=new AU(this,iG,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new oG(this)}static type(){return zU.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new nG.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let hG;const uG=function(){return void 0===hG&&(hG=new(window.AudioContext||window.webkitAudioContext)),hG},dG=new p.a,pG=new hh.a,_G=new p.a,mG=new p.a;class fG extends K.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=uG(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Dm}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(dG,pG,_G),mG.set(0,0,-1).applyQuaternion(pG),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(dG.x,t),e.positionY.linearRampToValueAtTime(dG.y,t),e.positionZ.linearRampToValueAtTime(dG.z,t),e.forwardX.linearRampToValueAtTime(mG.x,t),e.forwardY.linearRampToValueAtTime(mG.y,t),e.forwardZ.linearRampToValueAtTime(mG.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(dG.x,dG.y,dG.z),e.setOrientation(mG.x,mG.y,mG.z,n.x,n.y,n.z)}}class gG extends(yU(ua)){constructor(){super(...arguments),this.audio=ha.FOLDER(),this.soundOn=ha.BOOLEAN(1),this.masterVolume=ha.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]})}}const vG=new gG;class yG extends oU{constructor(){super(...arguments),this.paramsConfig=vG,this.hierarchyController=new wU(this),this.transformController=new bU(this),this.flags=new Di(this)}static type(){return Ig.AUDIO_LISTENER}createObject(){const t=new fG;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.soundOn],(()=>this.pv.soundOn?\\\\\\\"on\\\\\\\":\\\\\\\"off\\\\\\\"))}))}))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}_updatePositionalAudio(){const t=this.pv.soundOn?this.pv.masterVolume:0;this.object.setMasterVolume(t)}}class xG extends Ms.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new As.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,s=this.geometry.attributes.color.array;return i.set(t),i.toArray(s,0),i.toArray(s,3),i.set(e),i.toArray(s,6),i.toArray(s,9),i.set(n),i.toArray(s,12),i.toArray(s,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var bG;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(bG||(bG={}));const wG=[bG.TOGETHER,bG.SEPARATELY];const TG=new class extends ua{constructor(){super(...arguments),this.object0=ha.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.object1=ha.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.mode=ha.INTEGER(wG.indexOf(bG.TOGETHER),{menu:{entries:wG.map(((t,e)=>({name:t,value:e})))}}),this.blend=ha.FLOAT(0,{visibleIf:{mode:wG.indexOf(bG.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=ha.FLOAT(0,{visibleIf:{mode:wG.indexOf(bG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=ha.FLOAT(0,{visibleIf:{mode:wG.indexOf(bG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class AG extends oU{constructor(){super(...arguments),this.paramsConfig=TG,this.hierarchyController=new wU(this),this.flags=new Di(this),this._helper=new xG(1),this._t0=new p.a,this._q0=new hh.a,this._s0=new p.a,this._t1=new p.a,this._q1=new hh.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(ts.OBJ),e=this.p.object1.found_node_with_context(ts.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=wG[this.pv.mode];switch(n){case bG.TOGETHER:return this._blend_together(t,e);case bG.SEPARATELY:return this._blend_separately(t,e)}os.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var MG={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const EG={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},SG=1/256e3;class CG{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(MG),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(EG),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new ot.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new B.a(t)}applyBlur(t,e,n,i){const s=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*s*SG,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*s*SG,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var NG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(NG||(NG={}));const LG=[NG.ON_RENDER,NG.MANUAL];class OG extends(yU(ua)){constructor(){super(...arguments),this.shadow=ha.FOLDER(),this.dist=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=ha.VECTOR2([1,1]),this.shadowRes=ha.VECTOR2([256,256]),this.blur=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=ha.BOOLEAN(1),this.blur2=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=ha.FLOAT(1),this.opacity=ha.FLOAT(1),this.showHelper=ha.BOOLEAN(0),this.updateMode=ha.INTEGER(LG.indexOf(NG.ON_RENDER),{callback:t=>{IG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:LG.map(((t,e)=>({name:t,value:e})))}}),this.update=ha.BUTTON(null,{callback:t=>{IG.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:LG.indexOf(NG.MANUAL)}}),this.scene=ha.FOLDER(),this.include=ha.STRING(\\\\\\\"\\\\\\\"),this.exclude=ha.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=ha.BUTTON(null,{callback:t=>{IG.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=ha.BUTTON(null,{callback:t=>{IG.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const PG=new OG,RG=new d.a(256,256);class IG extends oU{constructor(){super(...arguments),this.paramsConfig=PG,this.hierarchyController=new wU(this),this.flags=new Di(this),this._renderTarget=this._createRenderTarget(RG),this._coreRenderBlur=this._createCoreRenderBlur(RG),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new bU(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new CG(t)}createObject(){const t=new Fn.a;this._shadowGroup=new Fn.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new lt.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new B.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new ot.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new FU(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Sn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,s=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=s,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=LG[this.pv.updateMode];switch(t){case NG.ON_RENDER:return this._addRenderHook();case NG.MANUAL:return this._removeRenderHook()}os.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,s,r){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=li.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const FG=\\\\\\\"display\\\\\\\";class DG{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(FG),i=this.node.params.boolean(FG),s=this.node.usedInScene(),r=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return s&&r&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class BG extends(yU(ua)){constructor(){super(...arguments),this.display=ha.BOOLEAN(1),this.renderOrder=ha.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const zG=new BG;class kG extends oU{constructor(){super(...arguments),this.paramsConfig=zG,this.hierarchyController=new wU(this),this.transformController=new bU(this),this.flags=new Di(this),this.childrenDisplayController=new DG(this),this.displayNodeController=new Fm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ig.GEO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class UG extends(yU(ua)){}const GG=new UG;class VG extends oU{constructor(){super(...arguments),this.paramsConfig=GG,this.hierarchyController=new wU(this),this.transformController=new bU(this),this.flags=new Di(this),this._helper=new xG(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const HG=new class extends ua{constructor(){super(...arguments),this.center=ha.VECTOR3([0,0,0]),this.longitude=ha.FLOAT(0,{range:[0,360]}),this.latitude=ha.FLOAT(0,{range:[-180,180]}),this.depth=ha.FLOAT(1,{range:[0,10]})}},jG=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",WG=new p.a(0,1,0),qG=new p.a(-1,0,0);class XG extends oU{constructor(){super(...arguments),this.paramsConfig=HG,this.hierarchyController=new wU(this),this.flags=new Di(this),this._helper=new xG(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new hh.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(jG)||this.dirtyController.addPostDirtyHook(jG,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(WG,Object(On.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(qG,Object(On.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class YG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=nV.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||Nl.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=ss.escapeLineBreaks(t)}}class $G{constructor(t){this._node=t}data(t={}){var e,n,i,s,r,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(s=this._node.flags.display)||void 0===s?void 0:s.active())),this._node.flags.hasOptimize()&&(null===(r=this._node.flags.optimize)||void 0===r?void 0:r.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=nV.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=YG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const s=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const r=s.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[r])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=nV.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=nV.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class JG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class ZG extends JG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class QG extends JG{add_main(){let t=this._param.rawInput();if(t=YG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class KG extends JG{add_main(){let t=this._param.rawInput();if(t=YG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class tV extends JG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class eV extends $G{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class nV{static dispatch_node(t){return t.polyNodeController?new eV(t):new $G(t)}static dispatch_param(t){return t instanceof ro?new ZG(t):t instanceof fo?new QG(t):t instanceof Mo?new KG(t):t instanceof Ao?new tV(t):new JG(t)}}class iV{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=li.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new iV;if(this._objects){const e=[];for(let t of this._objects)e.push(br.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new br(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=fr.pointsCount(t.geometry)),{type:Os(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new fr(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new fr(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=fr.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=ss.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new iV;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>ss.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(t=!1){return this._bounding_box=t?this._computeBoundingBox():this._bounding_box||this._computeBoundingBox()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_computeBoundingBox(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new Cy.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case js.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=iV.geometryFromObject(n);if(i){new fr(i).renameAttrib(t,e)}}));break;case js.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new br(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=ss.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(ss.matchMask(t,i))n.push(t);else{t==Xs.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=Xs.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new Fn.a;return t.children.forEach((t=>{const n=br.clone(t);e.add(n)})),e}}class sV extends zl{static context(){return ts.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new iV;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Ns.MESH){const n=sV.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return sV.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Ls[e])(t,n=n||js.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){sV.createIndexIfNone(t)}static createIndexIfNone(t){pr.createIndexIfNone(t)}}var rV;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(rV||(rV={}));const oV=\\\\\\\"input geometry\\\\\\\",aV=[oV,oV,oV,oV];class lV extends aa{constructor(){super(...arguments),this.flags=new Ui(this)}static context(){return ts.SOP}static displayedInputNames(){return aV}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,rV.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],rV.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,rV.FROM_SET_OBJECTS)}setGeometry(t,e=Ns.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],rV.FROM_SET_GEOMETRY)}setGeometries(t,e=Ns.MESH){const n=[];let i;for(let s of t)i=this.createObject(s,e),n.push(i);this._setContainerObjects(n,rV.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new iV;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return sV.createObject(t,e,n)}createObject(t,e,n){return lV.createObject(t,e,n)}static createIndexIfNone(t){sV.createIndexIfNone(t)}_createIndexIfNone(t){lV.createIndexIfNone(t)}}const cV=new class extends ua{};class hV extends lV{constructor(){super(...arguments),this.paramsConfig=cV}static type(){return ns.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){this.setCoreGroup(t[0])}}class uV extends lV{constructor(){super(...arguments),this.childrenDisplayController=new pV(this),this.displayNodeController=new Fm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const dV={dependsOnDisplayNode:!0};class pV{constructor(t,e=dV){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(hV.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Mi(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function _V(t,e){const n=new class extends ua{constructor(){super(...arguments),this.template=ha.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=ha.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends uV{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new gV(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const mV=_V(\\\\\\\"poly\\\\\\\",{nodeContext:ts.SOP,inputs:[0,4]});class fV extends mV{}class gV{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Jl({}),i=new Vl(this.node);i.create_nodes(n,t);const s=this._definition.ui;s&&i.process_nodes_ui_data(n,s),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=nV.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),s={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(s))}}static createNodeClass(t,e,n){switch(e){case ts.SOP:return _V(t,n);case ts.OBJ:return vV(t,n)}}}function vV(t,e){const n=new class extends ua{constructor(){super(...arguments),this.display=ha.BOOLEAN(1),this.template=ha.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=ha.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends oU{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new wU(this),this.flags=new Di(this),this.childrenDisplayController=new DG(this),this.displayNodeController=new Fm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this.polyNodeController=new gV(this,e)}static type(){return t}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const yV=vV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.OBJ});class xV extends yV{}class bV extends K.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t{t.load(e,(function(t){n(t)}))}))}}var OV;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(OV||(OV={}));const PV=[OV.LINEAR,OV.INVERSE,OV.EXPONENTIAL];class RV extends(yU(ua)){constructor(){super(...arguments),this.audio=ha.FOLDER(),this.listener=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:[Ig.AUDIO_LISTENER]}}),this.url=ha.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Rr.AUDIO]}}),this.volume=ha.FLOAT(1),this.loop=ha.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=ha.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=ha.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=ha.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=ha.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=ha.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=ha.INTEGER(PV.indexOf(OV.LINEAR),{menu:{entries:PV.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=ha.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=ha.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=ha.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=ha.BOOLEAN(1),this.showHelper=ha.BOOLEAN(0),this.helperSize=ha.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{showHelper:!0}}),this.play=ha.BUTTON(null,{callback:t=>{FV.PARAM_CALLBACK_play(t)}}),this.pause=ha.BUTTON(null,{callback:t=>{FV.PARAM_CALLBACK_pause(t)}})}}const IV=new RV;class FV extends oU{constructor(){super(...arguments),this.paramsConfig=IV,this.hierarchyController=new wU(this),this.transformController=new bU(this),this.flags=new Di(this)}static type(){return Ig.POSITIONAL_AUDIO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(PV[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.range=this.pv.helperSize,this._helper.update()))}_createHelper(t){const e=new SV(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(ts.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new EV(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new LV(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var DV;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(DV||(DV={}));const BV=[DV.ON_RENDER,DV.MANUAL];const zV=new class extends ua{constructor(){super(...arguments),this.object=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{kV.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=ha.INTEGER(0,{range:[0,100]}),this.updateMode=ha.INTEGER(BV.indexOf(DV.ON_RENDER),{callback:t=>{kV.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:BV.map(((t,e)=>({name:t,value:e})))}}),this.update=ha.BUTTON(null,{callback:t=>{kV.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:BV.indexOf(DV.MANUAL)}})}};class kV extends oU{constructor(){super(...arguments),this.paramsConfig=zV,this.hierarchyController=new wU(this),this.flags=new Di(this),this._helper=new xG(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new B.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=BV[this.pv.updateMode];switch(t){case DV.ON_RENDER:return this._add_render_hook();case DV.MANUAL:return this._remove_render_hook()}os.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,s,r){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==ts.OBJ&&t.type()==kG.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class UV extends(Pa(Na(wa(ya(_a(ua)))))){}const GV=new UV;class VV extends oU{constructor(){super(...arguments),this.paramsConfig=GV,this.hierarchyController=new wU(this),this.SceneAutoUpdateController=new ma(this),this.sceneBackgroundController=new xa(this),this.SceneEnvController=new Ta(this),this.sceneFogController=new La(this),this.sceneMaterialOverrideController=new Ra(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new vs;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class HV{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._updateRequired=this._controls_node.updateRequired()}updateRequired(){return this._updateRequired}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const jV=\\\\\\\"controls\\\\\\\";class WV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(jV)?this.node.params.get(jV):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(ms.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let s=!1,r=this._applied_controls_by_element_id.get(e.id);if(r&&r.get(i)&&(s=!0),!s){r=new Map,this._applied_controls_by_element_id.set(e.id,r),r.set(i,n);const s=await n.apply_controls(this.node.object,t);if(!s)return;const o=new HV(this.node.graphNodeId(),n,s);return this.set_controls_events(s),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.disposeControlsForHtmlElementId(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=BH[this.node.pv.updateFromControlsMode];switch(e){case DH.ON_END:return this._set_controls_events_to_update_on_end(t);case DH.ALWAYS:return this._set_controls_events_to_update_always(t);case DH.NEVER:return this._reset(t)}os.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function qV(t){return class extends t{constructor(){super(...arguments),this.layer=ha.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class XV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const YV={callback:t=>{qH.PARAM_CALLBACK_reset_effects_composer(t)}};function $V(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=ha.BOOLEAN(0),this.postProcessNode=ha.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[es.POST]},...YV})}}}class JV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,s=this.node.p.postProcessNode.value.node();if(s){if(s.type()==es.POST){const r=s,o=this.node.renderController.canvas_resolution(t);return r.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class ZV extends aa{constructor(){super(...arguments),this.flags=new Pi(this)}static context(){return ts.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var QV,KV,tH,eH;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(QV||(QV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(KV||(KV={})),(eH=tH||(tH={}))[eH.Linear=w.U]=\\\\\\\"Linear\\\\\\\",eH[eH.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",eH[eH.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",eH[eH.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",eH[eH.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",eH[eH.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",eH[eH.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",eH[eH.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const nH=[KV.Linear,KV.sRGB,KV.Gamma,KV.RGBE,KV.LogLuv,KV.RGBM7,KV.RGBM16,KV.RGBD],iH=[tH.Linear,tH.sRGB,tH.Gamma,tH.RGBE,tH.LogLuv,tH.RGBM7,tH.RGBM16,tH.RGBD],sH=tH.sRGB;var rH,oH,aH;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(rH||(rH={})),(aH=oH||(oH={}))[aH.No=w.vb]=\\\\\\\"No\\\\\\\",aH[aH.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",aH[aH.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",aH[aH.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",aH[aH.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const lH=[rH.No,rH.Linear,rH.Reinhard,rH.Cineon,rH.ACESFilmic],cH=[oH.No,oH.Linear,oH.Reinhard,oH.Cineon,oH.ACESFilmic],hH=oH.ACESFilmic,uH=lH.map(((t,e)=>({name:t,value:cH[e]})));var dH;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(dH||(dH={}));const pH=[dH.HIGH,dH.MEDIUM,dH.LOW];var _H;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(_H||(_H={}));const mH=[_H.HIGH,_H.LOW,_H.DEFAULT];var fH,gH,vH;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(fH||(fH={})),(vH=gH||(gH={}))[vH.Basic=w.k]=\\\\\\\"Basic\\\\\\\",vH[vH.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",vH[vH.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",vH[vH.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const yH=[fH.Basic,fH.PCF,fH.PCFSoft,fH.VSM],xH=[gH.Basic,gH.PCF,gH.PCFSoft,gH.VSM],bH=(w.k,w.Fb,w.Gb,w.gd,gH.PCFSoft),wH={alpha:!1,precision:dH.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:_H.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const TH=new class extends ua{constructor(){super(...arguments),this.tprecision=ha.BOOLEAN(0),this.precision=ha.INTEGER(pH.indexOf(dH.HIGH),{visibleIf:{tprecision:1},menu:{entries:pH.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=ha.BOOLEAN(0),this.powerPreference=ha.INTEGER(mH.indexOf(_H.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:mH.map(((t,e)=>({value:e,name:t})))}}),this.alpha=ha.BOOLEAN(1),this.premultipliedAlpha=ha.BOOLEAN(1),this.antialias=ha.BOOLEAN(1),this.stencil=ha.BOOLEAN(1),this.depth=ha.BOOLEAN(1),this.logarithmicDepthBuffer=ha.BOOLEAN(0),this.toneMapping=ha.INTEGER(hH,{menu:{entries:uH}}),this.toneMappingExposure=ha.FLOAT(1,{range:[0,2]}),this.outputEncoding=ha.INTEGER(sH,{menu:{entries:nH.map(((t,e)=>({name:t,value:iH[e]})))}}),this.physicallyCorrectLights=ha.BOOLEAN(1),this.sortObjects=ha.BOOLEAN(1),this.tpixelRatio=ha.BOOLEAN(0),this.pixelRatio=ha.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=ha.BOOLEAN(1),this.shadowMapAutoUpdate=ha.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=ha.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=ha.INTEGER(bH,{visibleIf:{tshadowMap:1},menu:{entries:yH.map(((t,e)=>({name:t,value:xH[e]})))}})}};class AH extends ZV{constructor(){super(...arguments),this.paramsConfig=TH,this._renderers_by_canvas_id={}}static type(){return QV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(wH);let s;for(s of i)n[s]=wH[s];if(this.pv.tprecision){const t=pH[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=mH[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const r=li.renderersController.createWebGLRenderer(n);return li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),li.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[t.id]=r,r}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:EH.defaultPixelRatio();li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),li.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function MH(t){return class extends t{constructor(){super(...arguments),this.render=ha.FOLDER(),this.setScene=ha.BOOLEAN(0),this.scene=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:ts.OBJ,types:[VV.type()]}}),this.setRenderer=ha.BOOLEAN(0),this.renderer=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:ts.ROP,types:[AH.type()]}}),this.setCSSRenderer=ha.BOOLEAN(0),this.CSSRenderer=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:ts.ROP,types:[QV.CSS2D,QV.CSS3D]}})}}}class EH{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n,i){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.renderWithRenderer(t,i),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}renderWithRenderer(t,e){const n=this.renderer(t);if(n){const t=this._resolved_scene;t&&n.render(t,this.node.object)}}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(ts.OBJ,VV.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(ts.ROP,QV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(ts.ROP,[QV.CSS2D,QV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=li.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=EH._createDefaultRenderer(t,n)),li.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return ng.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=li.renderersController.createWebGLRenderer(n),s=this.defaultPixelRatio();return i.setPixelRatio(s),i.shadowMap.enabled=!0,i.shadowMap.type=bH,i.physicallyCorrectLights=!0,i.toneMapping=hH,i.toneMappingExposure=1,i.outputEncoding=sH,li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),li.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}deleteRenderer(t){const e=this.renderer(t);e&&li.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class SH{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(t){this._config&&this._controls&&this._config.updateRequired()&&this._controls.update(t)}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Mi(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.create_controls()})),t}}class CH{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class NH{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const LH=\\\\\\\"hovered\\\\\\\";class OH{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=OH._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}setRenderObjectOverride(t){console.log(\\\\\\\"setRenderObjectOverride\\\\\\\",t),this._renderObjectOverride=t||void 0}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new CH(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Wa(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new NH(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(LH)}setContainerClassHovered(){this.domElement().classList.add(LH)}registerOnBeforeTick(t,e){this._registerCallback(t,e,this.registeredBeforeTickCallbacks())}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbacks)}registeredBeforeTickCallbacks(){return this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||new Map}registerOnAfterTick(t,e){this._registerCallback(t,e,this.registeredAfterTickCallbacks())}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbacks)}registeredAfterTickCallbacks(){return this._onAfterTickCallbacks=this._onAfterTickCallbacks||new Map}registerOnBeforeRender(t,e){this._registerCallback(t,e,this.registeredBeforeRenderCallbacks())}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbacks(){return this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||new Map}registerOnAfterRender(t,e){this._registerCallback(t,e,this.registeredAfterRenderCallbacks())}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbacks)}registeredAfterRenderCallbacks(){return this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||new Map}_registerCallback(t,e,n){n.has(t)?console.warn(`callback ${t} already registered`):n.set(t,e)}_unregisterCallback(t,e){e&&e.delete(t)}}OH._next_viewer_id=0;class PH extends OH{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._clock=new Dm,this._delta=0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new SH(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t,e,n;this._do_render&&(this._delta=this._clock.getDelta(),this._request_animation_frame_id=requestAnimationFrame(this._animate_method),null===(t=this._onBeforeTickCallbacks)||void 0===t||t.forEach((t=>t(this._delta))),this._scene.timeController.incrementTimeIfPlaying(this._delta),null===(e=this._onAfterTickCallbacks)||void 0===e||e.forEach((t=>t(this._delta))),this.render(this._delta),null===(n=this._controls_controller)||void 0===n||n.update(this._delta))}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.deleteRenderer(this._canvas)}render(t){var e,n;if(this.camerasController.cameraNode()&&this._canvas){null===(e=this._onBeforeRenderCallbacks)||void 0===e||e.forEach((e=>e(t)));const i=this.camerasController.size,s=this.camerasController.aspect;this._camera_node.renderController.render(this._canvas,i,s,this._renderObjectOverride),null===(n=this._onAfterRenderCallbacks)||void 0===n||n.forEach((e=>e(t)))}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const RH={type:\\\\\\\"change\\\\\\\"},IH=1,FH=100;var DH;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(DH||(DH={}));const BH=[DH.ON_END,DH.ALWAYS,DH.NEVER];function zH(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=ha.BUTTON(null,{callback:(t,e)=>{WH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function kH(t){return class extends t{constructor(){super(...arguments),this.camera=ha.FOLDER(),this.controls=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.EVENT}}),this.updateFromControlsMode=ha.INTEGER(BH.indexOf(DH.ON_END),{menu:{entries:BH.map(((t,e)=>({name:t,value:e})))}}),this.near=ha.FLOAT(IH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{qH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=ha.FLOAT(FH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{qH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=ha.BOOLEAN(1),this.showHelper=ha.BOOLEAN(0)}}}var UH;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}(UH||(UH={}));const GH=[UH.DEFAULT,UH.COVER,UH.CONTAIN];function VH(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=ha.INTEGER(GH.indexOf(UH.DEFAULT),{menu:{entries:GH.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=ha.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:GH.indexOf(UH.COVER)},{fovAdjustMode:GH.indexOf(UH.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}zH(ua);$V(MH(yU(qV(kH(zH(ua))))));class HH extends oU{constructor(){super(...arguments),this.renderOrder=rU.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(RH),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){vU.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class jH extends HH{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new wU(this),this.transformController=new bU(this),this.childrenDisplayController=new DG(this),this.displayNodeController=new Fm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new WV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new XV(this)}get renderController(){return this._render_controller=this._render_controller||new EH(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new JV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(RH),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new PH(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new FU(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class WH extends HH{}class qH extends jH{PARAM_CALLBACK_update_effects_composer(t){}}const XH=-.5,YH=.5,$H=.5,JH=-.5;class ZH extends($V(MH(qV(zH(VH(function(t){return class extends t{constructor(){super(...arguments),this.size=ha.FLOAT(1)}}}(kH(yU(ua,{matrixAutoUpdate:!0}))))))))){}const QH=new ZH;class KH extends jH{constructor(){super(...arguments),this.paramsConfig=QH}static type(){return is.ORTHOGRAPHIC}createObject(){return new ot.a(2*XH,2*YH,2*$H,2*JH,IH,FH)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=GH[this.pv.fovAdjustMode];switch(t){case UH.DEFAULT:return this._adjustFOVFromModeDefault();case UH.COVER:return this._adjustFOVFromModeCover();case UH.CONTAIN:return this._adjustFOVFromModeContain()}os.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=XH*e*1,this._object.right=YH*e*1,this._object.top=$H*t*1,this._object.bottom=JH*t*1}}const tj=50;class ej extends($V(MH(qV(zH(VH(function(t){return class extends t{constructor(){super(...arguments),this.fov=ha.FLOAT(tj,{range:[0,100]})}}}(kH(yU(ua,{matrixAutoUpdate:!0}))))))))){}const nj=new ej;class ij extends jH{constructor(){super(...arguments),this.paramsConfig=nj}static type(){return is.PERSPECTIVE}createObject(){return new tt.a(tj,1,IH,FH)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=GH[this.pv.fovAdjustMode];switch(t){case UH.DEFAULT:return this._adjustFOVFromModeDefault();case UH.COVER:return this._adjustFOVFromModeCover();case UH.CONTAIN:return this._adjustFOVFromModeContain()}os.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}}}class sj extends(function(t){return class extends t{constructor(){super(...arguments),this.main=ha.FOLDER(),this.resolution=ha.INTEGER(256),this.excludedObjects=ha.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=ha.BUTTON(null,{callback:t=>{oj.PARAM_CALLBACK_printResolve(t)}}),this.near=ha.FLOAT(1),this.far=ha.FLOAT(100),this.render=ha.BUTTON(null,{callback:t=>{oj.PARAM_CALLBACK_render(t)}}),this.renderTarget=ha.FOLDER(),this.tencoding=ha.BOOLEAN(0),this.encoding=ha.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:og.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=ha.BOOLEAN(0),this.minFilter=ha.INTEGER(Qm,{visibleIf:{tminFilter:1},menu:{entries:tf}}),this.tmagFilter=ha.BOOLEAN(0),this.magFilter=ha.INTEGER(Zm,{visibleIf:{tmagFilter:1},menu:{entries:Km}})}}}(yU(ua))){}const rj=new sj;class oj extends oU{constructor(){super(...arguments),this.paramsConfig=rj,this.hierarchyController=new wU(this),this.transformController=new bU(this),this.flags=new Di(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new xG(1)}static type(){return Ig.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,s=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&s==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new st(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new nt(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=li.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class aj extends oU{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class lj extends aj{}class cj extends lj{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hj extends cj{constructor(){super(...arguments),this.renderOrder=rU.MANAGER}}class uj extends lj{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dj extends lj{constructor(){super(...arguments),this.renderOrder=rU.MANAGER,this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class pj extends lj{constructor(){super(...arguments),this.renderOrder=rU.MANAGER,this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _j extends aj{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mj extends lj{constructor(){super(...arguments),this.renderOrder=rU.MANAGER,this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const fj=[\\\\\\\"input pass\\\\\\\"];const gj={cook:!1,callback:function(t,e){vj.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class vj extends aa{constructor(){super(...arguments),this.flags=new zi(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return ts.POST}static displayedInputNames(){return fj}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const yj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var xj={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class bj extends km{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===zm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=zm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===yj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(yj.uniforms),vertexShader:yj.vertexShader,fragmentShader:yj.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===xj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(xj.uniforms),vertexShader:xj.vertexShader,fragmentShader:xj.fragmentShader,blending:w.ub}),this.fsQuad=new Vm(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Q(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Q(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Q(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new lt.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const wj=new class extends ua{constructor(){super(...arguments),this.adaptive=ha.BOOLEAN(1,{...gj}),this.averageLuminance=ha.FLOAT(.7,{...gj}),this.midGrey=ha.FLOAT(.04,{...gj}),this.maxLuminance=ha.FLOAT(16,{range:[0,20],...gj}),this.adaptiveRange=ha.FLOAT(2,{range:[0,10],...gj})}};class Tj extends vj{constructor(){super(...arguments),this.paramsConfig=wj}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new bj(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const Aj={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Mj extends km{constructor(t=.96){super(),void 0===Aj&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=Aj,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new Vm(this.shaderMaterial);const e=new lt.a;this.copyFsQuad=new Vm(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const Ej=new class extends ua{constructor(){super(...arguments),this.damp=ha.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...gj})}};class Sj extends vj{constructor(){super(...arguments),this.paramsConfig=Ej}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new Mj;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const Cj={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Nj=new class extends ua{constructor(){super(...arguments),this.opacity=ha.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...gj})}};class Lj extends vj{constructor(){super(...arguments),this.paramsConfig=Nj}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new Hm(Cj);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const Oj={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Pj=new class extends ua{constructor(){super(...arguments),this.brightness=ha.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...gj}),this.contrast=ha.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...gj}),this.transparent=ha.BOOLEAN(1,gj)}};class Rj extends vj{constructor(){super(...arguments),this.paramsConfig=Pj}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new Hm(Oj);return e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class Ij extends km{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const Fj=new class extends ua{};class Dj extends vj{constructor(){super(...arguments),this.paramsConfig=Fj}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new Ij;return this.updatePass(e),e}updatePass(t){}}const Bj=new class extends ua{};class zj extends vj{constructor(){super(...arguments),this.paramsConfig=Bj}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Wm;return this.updatePass(e),e}updatePass(t){}}const kj={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Uj=new class extends ua{constructor(){super(...arguments),this.pow=ha.VECTOR3([2,2,2],{...gj}),this.mult=ha.COLOR([1,1,1],{...gj}),this.add=ha.COLOR([0,0,0],{...gj})}};class Gj extends vj{constructor(){super(...arguments),this.paramsConfig=Uj}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new Hm(kj);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const Vj=new class extends ua{constructor(){super(...arguments),this.opacity=ha.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...gj}),this.transparent=ha.BOOLEAN(1,gj)}};class Hj extends vj{constructor(){super(...arguments),this.paramsConfig=Vj}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new Hm(zm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const jj={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth; //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4 HS0 = vec4( 1.0, 0.0, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS1 = vec4( 0.309016994, 0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS2 = vec4(-0.809016994, 0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS3 = vec4(-0.809016994,-0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS4 = vec4( 0.309016994,-0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS5 = vec4( 0.0 ,0.0 , 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x, 0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0, 0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x, 0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0, wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0; kernel[1] = 2.0/16.0; kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0; kernel[4] = 4.0/16.0; kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0; kernel[7] = 2.0/16.0; kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},Wj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class qj{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const s={};let r;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(r=a,r.uniforms)for(let t of Object.keys(n))r.uniforms[t].value=n[t].value}else r=fr.markedAsInstance(i)?e:t;r&&(s[o.uuid]=o.material,o.material=r)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=s[e.uuid])}}));for(let t of Object.keys(s))delete s[t]}}class Xj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new vs,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new qj(this._scene);const s=3,r=4;this._processing_camera=new ot.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Q(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Q(this._resolution.x,this._resolution.y,o);var a=jj;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:s,SAMPLES:r}}),this._quad=new B.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=Wj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=$j.smoothstep(e.near,e.far,t.pv.focalDepth),i=$j.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const Yj=new class extends ua{constructor(){super(...arguments),this.focalDepth=ha.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...gj}),this.fStep=ha.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...gj}),this.maxBlur=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...gj}),this.vignetting=ha.BOOLEAN(0,{...gj}),this.depthBlur=ha.BOOLEAN(0,{...gj}),this.threshold=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...gj}),this.gain=ha.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...gj}),this.bias=ha.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...gj}),this.fringe=ha.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...gj}),this.noise=ha.BOOLEAN(0,{...gj}),this.dithering=ha.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...gj}),this.pentagon=ha.BOOLEAN(0,{...gj}),this.rings=ha.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...gj}),this.samples=ha.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...gj}),this.clearColor=ha.COLOR([1,1,1],{...gj})}};class $j extends vj{constructor(){super(...arguments),this.paramsConfig=Yj}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new Xj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Mi(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const Jj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Zj=new class extends ua{constructor(){super(...arguments),this.center=ha.VECTOR2([.5,.5],{...gj}),this.angle=ha.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...gj}),this.scale=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...gj})}};class Qj extends vj{constructor(){super(...arguments),this.paramsConfig=Zj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new Hm(Jj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const Kj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class tW extends km{constructor(t,e,n,i){super(),void 0===Kj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const s=Kj;this.uniforms=I.clone(s.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:s.vertexShader,fragmentShader:s.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new Vm(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const eW=new class extends ua{constructor(){super(...arguments),this.noiseIntensity=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...gj}),this.scanlinesIntensity=ha.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...gj}),this.scanlinesCount=ha.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...gj}),this.grayscale=ha.BOOLEAN(1,{...gj})}};class nW extends vj{constructor(){super(...arguments),this.paramsConfig=eW}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new tW(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const iW={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const sW=new class extends ua{constructor(){super(...arguments),this.transparent=ha.BOOLEAN(1,gj)}};class rW extends vj{constructor(){super(...arguments),this.paramsConfig=sW}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new Hm(iW);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const oW={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const aW=new class extends ua{};class lW extends vj{constructor(){super(...arguments),this.paramsConfig=aW}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new Hm(oW);return this.updatePass(e),e}updatePass(t){}}const cW=new class extends ua{constructor(){super(...arguments),this.amount=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...gj}),this.transparent=ha.BOOLEAN(1,gj)}};class hW extends vj{constructor(){super(...arguments),this.paramsConfig=cW}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new Hm(MG);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const uW=new class extends ua{constructor(){super(...arguments),this.map=ha.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP},...gj}),this.darkness=ha.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...gj}),this.offset=ha.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...gj})}};class dW extends vj{constructor(){super(...arguments),this.paramsConfig=uW}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new Hm(dW._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==ts.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const pW={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},_W=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",mW=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class fW extends km{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(pW),this.material=new F({uniforms:this.uniforms,vertexShader:_W,fragmentShader:mW,transparent:!0}),this.fsQuad=new Vm(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const gW=new class extends ua{};class vW extends vj{constructor(){super(...arguments),this.paramsConfig=gW}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=new qm(e,i),o=new qm(e,s);r.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=r,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new fW(r,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const yW=new class extends ua{constructor(){super(...arguments),this.overrideScene=ha.BOOLEAN(0,gj),this.scene=ha.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[VV.type()]},...gj}),this.overrideCamera=ha.BOOLEAN(0,gj),this.camera=ha.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...gj}),this.inverse=ha.BOOLEAN(0,gj)}};class xW extends vj{constructor(){super(...arguments),this.paramsConfig=yW}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new jm(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const bW=new class extends ua{};class wW extends vj{constructor(){super(...arguments),this.paramsConfig=bW}static type(){return\\\\\\\"null\\\\\\\"}}class TW extends km{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const s={minFilter:w.V,magFilter:w.V,format:w.Ib},r=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new lt.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Sn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Q(r,o,s),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Q(r,o,s),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Q(r,o,s),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(r,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(r/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===zm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=zm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new Vm(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,s&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=TW.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=TW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=TW.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=TW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,s&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tuniform mat4 textureMatrix;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvPosition = mvPosition;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D depthTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 cameraNearFar;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getEdgeDetectionMaterial(){return new F({uniforms:{maskTexture:{value:null},texSize:{value:new d.a(.5,.5)},visibleEdgeColor:{value:new p.a(1,1,1)},hiddenEdgeColor:{value:new p.a(1,1,1)}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getSeperableBlurMaterial(t){return new F({defines:{MAX_RADIUS:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)},kernelRadius:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}TW.BlurDirectionX=new d.a(1,0),TW.BlurDirectionY=new d.a(0,1);const AW=new class extends ua{constructor(){super(...arguments),this.objectsMask=ha.STRING(\\\\\\\"*outlined*\\\\\\\",{...gj}),this.refreshObjects=ha.BUTTON(null,{...gj}),this.printObjects=ha.BUTTON(null,{cook:!1,callback:t=>{MW.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=ha.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...gj}),this.edgeThickness=ha.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...gj}),this.edgeGlow=ha.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...gj}),this.pulsePeriod=ha.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...gj}),this.visibleEdgeColor=ha.COLOR([1,1,1],{...gj}),this.hiddenEdgeColor=ha.COLOR([.2,.1,.4],{...gj})}};class MW extends vj{constructor(){super(...arguments),this.paramsConfig=AW,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new TW(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const EW={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const SW=new class extends ua{constructor(){super(...arguments),this.pixelSize=ha.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...gj})}};class CW extends vj{constructor(){super(...arguments),this.paramsConfig=SW}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new Hm(EW);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const NW=new class extends ua{constructor(){super(...arguments),this.overrideScene=ha.BOOLEAN(0,gj),this.scene=ha.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[VV.type()]},...gj}),this.overrideCamera=ha.BOOLEAN(0,gj),this.camera=ha.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...gj})}};class LW extends vj{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Ym(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(ts.OBJ);if(e&&(e.type()==is.PERSPECTIVE||e.type()==is.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(ts.OBJ);if(e&&e.type()==VV.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const OW={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const PW=new class extends ua{constructor(){super(...arguments),this.amount=ha.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...gj}),this.angle=ha.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...gj})}};class RW extends vj{constructor(){super(...arguments),this.paramsConfig=PW}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new Hm(OW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const IW={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const FW=new class extends ua{constructor(){super(...arguments),this.amount=ha.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...gj})}};class DW extends vj{constructor(){super(...arguments),this.paramsConfig=FW}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new Hm(IW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const BW=new class extends ua{};class zW extends vj{constructor(){super(...arguments),this.paramsConfig=BW}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const kW=I.clone(MG.uniforms);kW.delta={value:new d.a};const UW={uniforms:kW,vertexShader:MG.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const GW=new class extends ua{constructor(){super(...arguments),this.delta=ha.VECTOR2([2,2],{...gj})}};class VW extends vj{constructor(){super(...arguments),this.paramsConfig=GW}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new Hm(UW);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const HW={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class jW extends km{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const s={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Q(r,o,s),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}jW.BlurDirectionX=new d.a(1,0),jW.BlurDirectionY=new d.a(0,1);const WW=new class extends ua{constructor(){super(...arguments),this.strength=ha.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...gj}),this.radius=ha.FLOAT(1,{...gj}),this.threshold=ha.FLOAT(0,{...gj})}};class qW extends vj{constructor(){super(...arguments),this.paramsConfig=WW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new jW(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const XW=new class extends ua{constructor(){super(...arguments),this.amount=ha.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...gj}),this.transparent=ha.BOOLEAN(1,gj)}};class YW extends vj{constructor(){super(...arguments),this.paramsConfig=XW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new Hm(EG);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const $W={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const JW=new class extends ua{constructor(){super(...arguments),this.offset=ha.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...gj}),this.darkness=ha.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...gj})}};class ZW extends vj{constructor(){super(...arguments),this.paramsConfig=JW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new Hm($W);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class QW extends aa{static context(){return ts.POST}cook(){this.cookController.endCook()}}class KW extends QW{}class tq extends KW{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class eq extends KW{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class nq extends KW{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iq extends KW{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sq extends QW{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rq extends KW{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oq extends aa{static context(){return ts.ROP}cook(){this.cookController.endCook()}}class aq extends oq{}class lq extends aq{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cq extends K.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof cq&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}clone(t){const e=this._element.cloneNode(!0);return new cq(e).copy(this,t)}copy(t,e){return K.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class hq{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof cq){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,s=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=s,i.style.transform=s,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=or.fit(t,this._fog_near,this._fog_far,0,1),s=or.clamp(1-n,0,1);i.style.opacity=`${s}`,0==s&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var r=0,o=t.children.length;r{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class pq extends aq{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _q extends aq{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mq extends aq{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class fq extends oq{constructor(){super(...arguments),this.paramsConfig=new ef,this.effectsComposerController=new nf(this),this.displayNodeController=new Fm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class gq extends aq{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class vq extends sV{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let s=[];const r=[];let o;for(let t=0;t0&&(r.push(t-1),r.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(s,s.length);const t=r[r.length-1];r.push(t),r.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r);const l=this.createObject(a,Ns.LINE_SEGMENTS);e.push(l)}}vq.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const yq=vq.DEFAULT_PARAMS;const xq=new class extends ua{constructor(){super(...arguments),this.createPoint=ha.BOOLEAN(yq.createPoint),this.pointsCount=ha.INTEGER(yq.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=ha.VECTOR3(yq.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=ha.BOOLEAN(yq.connectInputPoints),this.connectToLastPoint=ha.BOOLEAN(yq.connectToLastPoint)}};class bq extends lV{constructor(){super(...arguments),this.paramsConfig=xq}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new vq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const wq=new class extends ua{};class Tq extends lV{constructor(){super(...arguments),this.paramsConfig=wq}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],s=n.animations;s?(i.animations=s.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class Aq{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const s=e.tracks,r=s.length,o=new Array(r),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==r;++t){const e=s[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(r),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,s=i/n,r=n/i;t.warp(1,s,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,s=i.time,r=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=s,a[1]=s+n,l[0]=t/r,l[1]=e/r,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const s=this._startTime;if(null!==s){const i=(t-s)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const r=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=n===w.db;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(n===w.cb){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}var Mq=n(71),Eq=n(66);class Sq{constructor(t,e,n){let i,s,r;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,s=this._slerpAdditive,r=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,s=this._select,r=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,s=this._lerpAdditive,r=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=s,this._setIdentity=r,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,s=t*i+i;let r=this.cumulativeWeight;if(0===r){for(let t=0;t!==i;++t)n[s+t]=n[t];r=e}else{r+=e;const t=e/r;this._mixBufferRegion(n,s,0,t,i)}this.cumulativeWeight=r}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,s=this.cumulativeWeight,r=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,s<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-s,e)}r>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){hh.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;hh.a.multiplyQuaternionsFlat(t,r,t,e,t,n),hh.a.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}var Cq=n(64);class Nq extends J.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new Sq(Eq.a.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;t{Oq.PARAM_CALLBACK_reset(t,e)}})}};class Oq extends lV{constructor(){super(...arguments),this.paramsConfig=Lq}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(ts.ANIM)){return new Nq(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class Pq extends sV{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,s=n.mult,r=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class Hq extends lV{constructor(){super(...arguments),this.paramsConfig=Vq}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Uq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>Bq[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new Uq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jq extends sV{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,s=i.attribNamesMatchingMask(e.name);for(let t of s)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var s;const r=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>r.length)null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let h=t.getAttribute(c);if(h)this._fill_dest_array(h,o,i),h.needsUpdate=!0;else{const e=o.array.slice(0,l*r);t.setAttribute(c,new C.c(e,r))}}else null===(r=this.states)||void 0===r||r.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,s=e.array,r=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new jq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class Yq extends sV{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Ds[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=ks[n.type];switch(t){case Fs.VERTEX:return void await this.add_point_attribute(i,e,n);case Fs.OBJECT:return void await this.add_object_attribute(i,e,n)}os.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case zs.NUMERIC:for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._addAttribute(Ds[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new Yq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _addAttribute(t,e){const n=ks[this.pv.type];switch(t){case Fs.VERTEX:return await this._addPointAttribute(n,e),this.setCoreGroup(e);case Fs.OBJECT:return await this._addObjectAttribute(n,e),this.setCoreGroup(e)}os.unreachable(t)}async _addPointAttribute(t,e){const n=e.coreObjects();switch(t){case zs.NUMERIC:for(let t=0;t{s[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let r;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a{i[a][t.index()]=e}));else{const t=r.value;for(let e of n)s[e.index()*this.pv.size+a]=t}for(let t=0;t{t.setAttribValue(this.pv.name,e)}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components,n={};const i=this._vector_by_attrib_size(this.pv.size);if(i){for(let e of t)n[e.index()]=i;for(let i=0;i{n[t.index()][r]=e}));else for(let e of t){n[e.index()][r]=s.value}}for(let e=0;e{i[t.index()]=e}));const s=Xs.arrayToIndexedArrays(i),r=t.coreGeometry();r&&r.setIndexedAttribute(this.pv.name,s.values,s.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.computeExpressionForObjects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Fs.VERTEX:this.delete_vertex_attribute(e,t);case Fs.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new fr(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new br(n,t).deleteAttribute(e),t++}))}}}class eX{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,s=t.mult,r=this._data_from_texture(t.texture);if(!r)return;const{data:o,resx:a,resy:l}=r,c=o.length/(a*l),h=e.getAttribute(t.uvAttribName).array,u=h.length/2,d=new Array(u*n);let p,_,m,f,g,v,y,x,b;const w=or.clamp;for(v=0;v{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new nX(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oX;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(oX||(oX={}));const aX=[oX.MIN_MAX_TO_01,oX.VECTOR_TO_LENGTH_1];class lX extends sV{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),s=ss.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of s){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(aX[n.mode]){case oX.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case oX.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,s=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n({name:t,value:e})))}}),this.name=ha.STRING(cX.name),this.changeName=ha.BOOLEAN(cX.changeName),this.newName=ha.STRING(cX.newName,{visibleIf:{changeName:1}})}};class uX extends lV{constructor(){super(...arguments),this.paramsConfig=hX}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(lX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(aX.indexOf(t))}cook(t){this._operation=this._operation||new lX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var dX;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(dX||(dX={}));class pX extends sV{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=ss.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Fs.VERTEX:return this.find_values_from_points(t,e);case Fs.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let s=0;s{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Bs.filter((t=>t.value==this.pv.classFrom))[0].name,e=Bs.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new pX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const vX=new class extends ua{constructor(){super(...arguments),this.name=ha.STRING(),this.ramp=ha.RAMP(),this.changeName=ha.BOOLEAN(0),this.newName=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class yX extends lV{constructor(){super(...arguments),this.paramsConfig=vX}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let s=new Array(e.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,t.hasAttrib(r)||t.addNumericVertexAttrib(r,n,0));let o=0;for(let t of s){e[o].setAttribValue(r,t),o++}}_get_remaped_values(t,e,n){switch(t){case Gs.FLOAT:return this._get_normalized_float(e,n);case Gs.VECTOR2:return this._get_normalized_vector2(e,n);case Gs.VECTOR3:return this._get_normalized_vector3(e,n);case Gs.VECTOR4:return this._get_normalized_vector4(e,n)}os.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var wX=n(18);class TX{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new TX(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new Cy.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class AX{constructor(t){this._root=new TX(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new wX.a(t,e);let s=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,s),null==n||s.length>n&&(s=f.sortBy(s,(e=>e.position().distanceTo(t))),s=s.slice(0,n)),s}}class MX{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const EX=new class extends ua{constructor(){super(...arguments),this.srcGroup=ha.STRING(),this.destGroup=ha.STRING(),this.name=ha.STRING(),this.maxSamplesCount=ha.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=ha.FLOAT(1),this.blendWidth=ha.FLOAT(0)}};class SX extends lV{constructor(){super(...arguments),this.paramsConfig=EX}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new AX(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new MX;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,s){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,s);default:return this._interpolate_with_multiple_points(t,e,n,i,s)}}static _interpolate_with_1_point(t,e,n,i,s){const r=t.position(),o=e.position(),a=r.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,s):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,s,r){if(i<=s)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,s,r);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,s){const r=e.map((e=>this._interpolate_with_1_point(t,e,n,i,s)));return f.max(r)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const CX=new class extends ua{constructor(){super(...arguments),this.stepSize=ha.FLOAT(.1)}};class NX extends lV{constructor(){super(...arguments),this.paramsConfig=CX}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),s=i.min,r=i.max,o=[];for(let t=s.x;t<=r.x;t+=n)for(let e=s.y;e<=r.y;e+=n)for(let i=s.z;i<=r.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Ns.POINTS)}}const LX=new class extends ua{constructor(){super(...arguments),this.attribName=ha.STRING(\\\\\\\"position\\\\\\\"),this.blend=ha.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class OX extends lV{constructor(){super(...arguments),this.paramsConfig=LX}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),s=n.objects();let r,o;for(let t=0;tt.flip())),t}}PX.fromPolygons=function(t){let e=new PX;return e.polygons=t,e};class RX{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new RX(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(IX.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,s=e.z,r=t.x,o=t.y,a=t.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let IX=new RX,FX=new RX;class DX{constructor(t,e,n,i){this.pos=(new RX).copy(t),this.normal=(new RX).copy(e),this.uv=(new RX).copy(n),this.uv.z=0,i&&(this.color=(new RX).copy(i))}clone(){return new DX(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new DX(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class BX{constructor(t,e){this.normal=t,this.w=e}clone(){return new BX(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,s){let r=0,o=[];for(let e=0;eBX.EPSILON?1:0;r|=i,o.push(i)}switch(r){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:s.push(t);break;case 3:let r=[],a=[];for(let e=0;e=3&&i.push(new zX(r,t.shared)),a.length>=3&&s.push(new zX(a,t.shared))}}}BX.EPSILON=1e-5,BX.fromPoints=function(t,e,n){let i=IX.copy(e).sub(t).cross(FX.copy(n).sub(t)).normalize();return new BX(i.clone(),i.dot(t))};class zX{constructor(t,e){this.vertices=t,this.shared=e,this.plane=BX.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new zX(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class kX{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new kX;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;tnew zX(t.vertices.map((t=>new DX(t.pos,t.normal,t.uv))),t.shared))))},PX.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,s=t.vertices,r=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),HX=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var jX;PX.toMesh=function(t,e,n){let i,s,r=t.polygons;{let t=0;r.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=VX(3*t*3),o=VX(3*t*3),a=HX(2*t*3),l=[];if(r.forEach((i=>{let s=i.vertices,r=s.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),r&&void 0!==s[0].color&&(e||(e=VX(3*t*3)));for(let t=3;t<=r;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(s[0].pos),n.write(s[t-2].pos),n.write(s[t-1].pos),o.write(s[0].normal),o.write(s[t-2].normal),o.write(s[t-1].normal),a.write(s[0].uv),a.write(s[t-2].uv),a.write(s[t-1].uv),e&&(e.write(s[0].color)||e.write(s[t-2].color)||e.write(s[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n({name:t,value:e})))}}),this.useBothMaterials=ha.BOOLEAN(XX.useBothMaterials)}};class $X extends lV{constructor(){super(...arguments),this.paramsConfig=YX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(qX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>WX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(WX.indexOf(t))}async cook(t){this._operation=this._operation||new qX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class JX extends sV{constructor(){super(...arguments),this._core_transform=new vU}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(s.x,s.y,s.z,n,n,n),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}JX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},JX.INPUT_CLONED_STATE=Ki.NEVER;const ZX=JX.DEFAULT_PARAMS;const QX=new class extends ua{constructor(){super(...arguments),this.size=ha.FLOAT(ZX.size),this.divisions=ha.INTEGER(ZX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=ha.VECTOR3(ZX.center)}};class KX extends lV{constructor(){super(...arguments),this.paramsConfig=QX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(JX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new JX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class tY{constructor(){}}function eY(t,e,n){return n.min.x=e[t],n.min.y=e[t+1],n.min.z=e[t+2],n.max.x=e[t+3],n.max.y=e[t+4],n.max.z=e[t+5],n}function nY(t){let e=-1,n=-1/0;for(let i=0;i<3;i++){const s=t[i+3]-t[i];s>n&&(n=s,e=i)}return e}function iY(t,e){e.set(t)}function sY(t,e,n){let i,s;for(let r=0;r<3;r++){const o=r+3;i=t[r],s=e[r],n[r]=is?i:s}}function rY(t){const e=t[3]-t[0],n=t[4]-t[1],i=t[5]-t[2];return 2*(e*n+n*i+i*e)}const oY=Math.pow(2,-24);function aY(t,e,n,i,s=null){let r=1/0,o=1/0,a=1/0,l=-1/0,c=-1/0,h=-1/0,u=1/0,d=1/0,p=1/0,_=-1/0,m=-1/0,f=-1/0;const g=null!==s;for(let i=6*e,s=6*(e+n);il&&(l=v),g&&e_&&(_=e);const y=t[i+2],x=t[i+3],b=y-x,w=y+x;bc&&(c=w),g&&ym&&(m=y);const T=t[i+4],A=t[i+5],M=T-A,E=T+A;Mh&&(h=E),g&&Tf&&(f=T)}i[0]=r,i[1]=o,i[2]=a,i[3]=l,i[4]=c,i[5]=h,g&&(s[0]=u,s[1]=d,s[2]=p,s[3]=_,s[4]=m,s[5]=f)}const lY=32,cY=new Array(lY).fill().map((()=>({count:0,bounds:new Float32Array(6),rightCacheBounds:new Float32Array(6),candidate:0}))),hY=new Float32Array(6);function uY(t,e){function n(e,i,d,p=null,_=0){if(!u&&_>=a&&(u=!0,l&&(console.warn(`MeshBVH: Max depth of ${a} reached when generating BVH. Consider increasing maxDepth.`),console.warn(t))),d<=c||_>=a)return e.offset=i,e.count=d,e;const m=function(t,e,n,i,s,r){let o=-1,a=0;if(0===r)o=nY(e),-1!==o&&(a=(e[o]+e[o+3])/2);else if(1===r)o=nY(t),-1!==o&&(a=function(t,e,n,i){let s=0;for(let r=e,o=e+n;r=lY&&(s=31);const r=cY[s];r.count++;const o=r.bounds;for(let t=0;t<3;t++){const i=n[e+2*t],s=n[e+2*t+1],r=i-s,a=i+s;ro[t+3]&&(o[t+3]=a)}}const d=cY[31];iY(d.bounds,d.rightCacheBounds);for(let t=30;t>=0;t--){const e=cY[t],n=cY[t+1];sY(e.bounds,n.rightCacheBounds,e.rightCacheBounds)}let p=0;for(let e=0;e<31;e++){const n=cY[e],i=n.count,c=n.bounds,h=cY[e+1].rightCacheBounds;0!==i&&(0===p?iY(c,hY):sY(c,hY,hY)),p+=i;let u=0,d=0;0!==p&&(u=rY(hY)/r);const _=s-p;0!==_&&(d=rY(h)/r);const m=1+1.25*(u*p+d*_);m=a;)o--;if(!(r65535?new Uint32Array(new i(4*n)):new Uint16Array(new i(2*n)),t.setIndex(new Jw(s,1));for(let t=0;td&&(d=s),a>d&&(d=a);const p=(d-l)/2,_=2*t;o[r+_+0]=l+p,o[r+_+1]=p+(Math.abs(l)+p)*oY,le[t+3]&&(e[t+3]=d)}}return o}(t,i),o=t.index.array,a=e.maxDepth,l=e.verbose,c=e.maxLeafTris,h=e.strategy;let u=!1;const d=[],p=function(t){if(!t.groups||!t.groups.length)return[{offset:0,count:t.index.count/3}];const e=[],n=new Set;for(const e of t.groups)n.add(e.start),n.add(e.start+e.count);const i=Array.from(n.values()).sort(((t,e)=>t-e));for(let t=0;ta&&(a=e);const n=t[i+2];nl&&(l=n);const h=t[i+4];hc&&(c=h)}i[0]=s,i[1]=r,i[2]=o,i[3]=a,i[4]=l,i[5]=c}(r,t.offset,t.count,s),n(e,t.offset,t.count,s),d.push(e)}else for(let t of p){const e=new tY;e.boundingData=new Float32Array(6),aY(r,t.offset,t.count,e.boundingData,s),n(e,t.offset,t.count,s),d.push(e)}return d}const dY=65535;class pY{constructor(){this.min=1/0,this.max=-1/0}setFromPointsField(t,e){let n=1/0,i=-1/0;for(let s=0,r=t.length;si?r:i}this.min=n,this.max=i}setFromPoints(t,e){let n=1/0,i=-1/0;for(let s=0,r=e.length;si?o:i}this.min=n,this.max=i}isSeparated(t){return this.min>t.max||t.min>this.max}}pY.prototype.setFromBox=function(){const t=new Ab;return function(e,n){const i=n.min,s=n.max;let r=1/0,o=-1/0;for(let n=0;n<=1;n++)for(let a=0;a<=1;a++)for(let l=0;l<=1;l++){t.x=i.x*n+s.x*(1-n),t.y=i.y*a+s.y*(1-a),t.z=i.z*l+s.z*(1-l);const c=e.dot(t);r=Math.min(c,r),o=Math.max(c,o)}this.min=r,this.max=o}}();!function(){const t=new pY}();const _Y=function(){const t=new Ab,e=new Ab,n=new Ab;return function(i,s,r){const o=i.start,a=t,l=s.start,c=e;n.subVectors(o,l),t.subVectors(i.end,s.start),e.subVectors(s.end,s.start);const h=n.dot(c),u=c.dot(a),d=c.dot(c),p=n.dot(a),_=a.dot(a)*d-u*u;let m,f;m=0!==_?(h*u-p*d)/_:0,f=(h+m*u)/d,r.x=m,r.y=f}}(),mY=function(){const t=new ub,e=new Ab,n=new Ab;return function(i,s,r,o){_Y(i,s,t);let a=t.x,l=t.y;if(a>=0&&a<=1&&l>=0&&l<=1)return i.at(a,r),void s.at(l,o);if(a>=0&&a<=1)return l<0?s.at(0,o):s.at(1,o),void i.closestPointToPoint(o,!0,r);if(l>=0&&l<=1)return a<0?i.at(0,r):i.at(1,r),void s.closestPointToPoint(r,!0,o);{let t,c;t=a<0?i.start:i.end,c=l<0?s.start:s.end;const h=e,u=n;return i.closestPointToPoint(c,!0,e),s.closestPointToPoint(t,!0,n),h.distanceToSquared(c)<=u.distanceToSquared(t)?(r.copy(h),void o.copy(c)):(r.copy(t),void o.copy(u))}}}(),fY=function(){const t=new Ab,e=new Ab,n=new GT,i=new eL;return function(s,r){const{radius:o,center:a}=s,{a:l,b:c,c:h}=r;i.start=l,i.end=c;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=l,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=c,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;const u=r.getPlane(n);if(Math.abs(u.distanceToPoint(a))<=o){const t=u.projectPoint(a,e);if(r.containsPoint(t))return!0}return!1}}();class gY extends Bw{constructor(...t){super(...t),this.isSeparatingAxisTriangle=!0,this.satAxes=new Array(4).fill().map((()=>new Ab)),this.satBounds=new Array(4).fill().map((()=>new pY)),this.points=[this.a,this.b,this.c],this.sphere=new qb,this.plane=new GT,this.needsUpdate=!1}intersectsSphere(t){return fY(t,this)}update(){const t=this.a,e=this.b,n=this.c,i=this.points,s=this.satAxes,r=this.satBounds,o=s[0],a=r[0];this.getNormal(o),a.setFromPoints(o,i);const l=s[1],c=r[1];l.subVectors(t,e),c.setFromPoints(l,i);const h=s[2],u=r[2];h.subVectors(e,n),u.setFromPoints(h,i);const d=s[3],p=r[3];d.subVectors(n,t),p.setFromPoints(d,i),this.sphere.setFromPoints(this.points),this.plane.setFromNormalAndCoplanarPoint(o,t),this.needsUpdate=!1}}gY.prototype.closestPointToSegment=function(){const t=new Ab,e=new Ab,n=new eL;return function(i,s=null,r=null){const{start:o,end:a}=i,l=this.points;let c,h=1/0;for(let o=0;o<3;o++){const a=(o+1)%3;n.start.copy(l[o]),n.end.copy(l[a]),mY(n,i,t,e),c=t.distanceToSquared(e),c1-1e-10)console.warn(\\\\\\\"SeparatingAxisTriangle.intersectsTriangle: Triangles are coplanar which does not support an output edge. Setting edge to 0, 0, 0.\\\\\\\"),p.start.set(0,0,0),p.end.set(0,0,0);else{const n=this.points;let i=!1;for(let t=0;t<3;t++){const s=n[t],r=n[(t+1)%3];if(c.start.copy(s),c.end.copy(r),e.intersectLine(c,i?h.start:h.end)){if(i)break;i=!0}}const s=d.points;let r=!1;for(let e=0;e<3;e++){const n=s[e],i=s[(e+1)%3];if(c.start.copy(n),c.end.copy(i),t.intersectLine(c,r?u.start:u.end)){if(r)break;r=!0}}if(h.delta(o),u.delta(a),o.dot(a)<0){let t=u.start;u.start=u.end,u.end=t}l.subVectors(h.start,u.start),l.dot(o)>0?p.start.copy(h.start):p.start.copy(u.start),l.subVectors(h.end,u.end),l.dot(o)<0?p.end.copy(h.end):p.end.copy(u.end)}}return!0}}(),gY.prototype.distanceToPoint=function(){const t=new Ab;return function(e){return this.closestPointToPoint(e,t),e.distanceTo(t)}}(),gY.prototype.distanceToTriangle=function(){const t=new Ab,e=new Ab,n=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],i=new eL,s=new eL;return function(r,o=null,a=null){const l=o||a?i:null;if(this.intersectsTriangle(r,l))return(o||a)&&(o&&l.getCenter(o),a&&l.getCenter(a)),0;let c=1/0;for(let e=0;e<3;e++){let i;const s=n[e],l=r[s];this.closestPointToPoint(l,t),i=l.distanceToSquared(t),inew Ab)),this.satAxes=new Array(3).fill().map((()=>new Ab)),this.satBounds=new Array(3).fill().map((()=>new pY)),this.alignedSatBounds=new Array(3).fill().map((()=>new pY)),this.needsUpdate=!1}set(t,e,n){super.set(t,e),this.matrix=n,this.needsUpdate=!0}copy(t){super.copy(t),this.matrix.copy(t.matrix),this.needsUpdate=!0}}vY.prototype.update=function(){const t=this.matrix,e=this.min,n=this.max,i=this.points;for(let s=0;s<=1;s++)for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){const a=i[1*s|2*r|4*o];a.x=s?n.x:e.x,a.y=r?n.y:e.y,a.z=o?n.z:e.z,a.applyMatrix4(t)}const s=this.satBounds,r=this.satAxes,o=i[0];for(let t=0;t<3;t++){const e=r[t],n=s[t],a=i[1<new eL)),n=new Array(12).fill().map((()=>new eL)),i=new Ab,s=new Ab;return function(r,o=0,a=null,l=null){if(this.needsUpdate&&this.update(),this.intersectsBox(r))return(a||l)&&(r.getCenter(s),this.closestPointToPoint(s,i),r.closestPointToPoint(i,s),a&&a.copy(i),l&&l.copy(s)),0;const c=o*o,h=r.min,u=r.max,d=this.points;let p=1/0;for(let t=0;t<8;t++){const e=d[t];s.copy(e).clamp(h,u);const n=e.distanceToSquared(s);if(nn.far?null:t)}function CY(t,e,n,i){const s=t.a,r=t.b,o=t.c;let a=e,l=e+1,c=e+2;n&&(a=n.getX(e),l=n.getX(e+1),c=n.getX(e+2)),s.x=i.getX(a),s.y=i.getY(a),s.z=i.getZ(a),r.x=i.getX(l),r.y=i.getY(l),r.z=i.getZ(l),o.x=i.getX(c),o.y=i.getY(c),o.z=i.getZ(c)}function NY(t,e,n,i,s,r,o){const a=n.index,l=n.attributes.position;for(let n=t,c=e+t;n=0;let c,h;l?(c=IY(t),h=FY(t,a)):(c=FY(t,a),h=IY(t));const u=HY(c,r,i,BY)?UY(c,e,n,i):null;if(u){const t=u.point[o];if(l?t<=r[h+s]:t>=r[h+s+3])return u}const d=HY(h,r,i,BY)?UY(h,e,n,i):null;return u&&d?u.distance<=d.distance?u:d:u||d||null}}const GY=function(){let t,e;const n=[],i=new LY((()=>new Sb));return function(...r){t=i.getPrimitive(),e=i.getPrimitive(),n.push(t,e);const o=s(...r);i.releasePrimitive(t),i.releasePrimitive(e),n.pop(),n.pop();const a=n.length;return a>0&&(e=n[a-1],t=n[a-2]),o};function s(n,i,r,o,a=null,l=0,c=0){function h(t){let e=2*t,n=XY,i=YY;for(;!OY(e,n);)e=2*(t=IY(t));return PY(t,i)}function u(t){let e=2*t,n=XY,i=YY;for(;!OY(e,n);)e=2*(t=FY(t,i));return PY(t,i)+RY(e,n)}let d=2*n,p=qY,_=XY,m=YY;if(OY(d,_)){const e=PY(n,m),i=RY(d,_);return eY(n,p,t),o(e,i,!1,c,l+n,t)}{const d=IY(n),f=FY(n,m);let g,v,y,x,b=d,w=f;if(a&&(y=t,x=e,eY(b,p,y),eY(w,p,x),g=a(y),v=a(x),vs.intersectsBox(t),intersectsTriangle:t=>{t.a.applyMatrix4(c),t.b.applyMatrix4(c),t.c.applyMatrix4(c),t.needsUpdate=!0;for(let n=3*g,i=3*(v+g);nnew gY));class h${static serialize(t,e={}){if(e.isBufferGeometry)return console.warn(\\\\\\\"MeshBVH.serialize: The arguments for the function have changed. See documentation for new signature.\\\\\\\"),h$.serialize(arguments[0],{cloneBuffers:void 0===arguments[2]||arguments[2]});e={cloneBuffers:!0,...e};const n=t.geometry,i=t._roots,s=n.getIndex();let r;return r=e.cloneBuffers?{roots:i.map((t=>t.slice())),index:s.array.slice()}:{roots:i,index:s.array},r}static deserialize(t,e,n={}){if(\\\\\\\"boolean\\\\\\\"==typeof n)return console.warn(\\\\\\\"MeshBVH.deserialize: The arguments for the function have changed. See documentation for new signature.\\\\\\\"),h$.deserialize(arguments[0],arguments[1],{setIndex:void 0===arguments[2]||arguments[2]});n={setIndex:!0,...n};const{index:i,roots:s}=t,r=new h$(e,{...n,[ZY]:!0});if(r._roots=s,n.setIndex){const n=e.getIndex();if(null===n){const n=new Jw(t.index,1,!1);e.setIndex(n)}else n.array!==i&&(n.array.set(i),n.needsUpdate=!0)}return r}constructor(t,e={}){if(!t.isBufferGeometry)throw new Error(\\\\\\\"MeshBVH: Only BufferGeometries are supported.\\\\\\\");if(t.index&&t.index.isInterleavedBufferAttribute)throw new Error(\\\\\\\"MeshBVH: InterleavedBufferAttribute is not supported for the index attribute.\\\\\\\");if((e=Object.assign({strategy:0,maxDepth:40,maxLeafTris:10,verbose:!0,useSharedArrayBuffer:!1,setBoundingBox:!0,[ZY]:!1},e)).useSharedArrayBuffer&&\\\\\\\"undefined\\\\\\\"==typeof SharedArrayBuffer)throw new Error(\\\\\\\"MeshBVH: SharedArrayBuffer is not available.\\\\\\\");this._roots=null,e[ZY]||(this._roots=function(t,e){const n=uY(t,e);let i,s,r;const o=[],a=e.useSharedArrayBuffer?SharedArrayBuffer:ArrayBuffer;for(let t=0;tMath.pow(2,32))throw new Error(\\\\\\\"MeshBVH: Cannot store child pointer greater than 32 bits.\\\\\\\");return s[n+6]=a/4,a=c(a,r),s[n+7]=o,a}}}(t,e),!t.boundingBox&&e.setBoundingBox&&(t.boundingBox=this.getBoundingBox(new Sb))),this.geometry=t}refit(t=null){t&&Array.isArray(t)&&(t=new Set(t));const e=this.geometry,n=e.index.array,i=e.attributes.position,s=i.array,r=i.offset||0;let o,a,l,c,h=3;i.isInterleavedBufferAttribute&&(h=i.data.stride);let u=0;const d=this._roots;for(let t=0,e=d.length;tp&&(p=a),l_&&(_=l),cm&&(m=c)}return(c[e+0]!==i||c[e+1]!==o||c[e+2]!==d||c[e+3]!==p||c[e+4]!==_||c[e+5]!==m)&&(c[e+0]=i,c[e+1]=o,c[e+2]=d,c[e+3]=p,c[e+4]=_,c[e+5]=m,!0)}{const n=e+8,s=a[e+6],r=n+i,l=s+i;let h=o,u=!1,d=!1;t?h||(u=t.has(r),d=t.has(l),h=!u&&!d):(u=!0,d=!0);const _=h||d;let m=!1;(h||u)&&(m=p(n,i,h));let f=!1;_&&(f=p(s,i,h));const g=m||f;if(g)for(let t=0;t<3;t++){const i=n+t,r=s+t,o=c[i],a=c[i+3],l=c[r],h=c[r+3];c[e+t]=oh?a:h}return g}}}traverse(t,e=0){const n=this._roots[e],i=new Uint32Array(n),s=new Uint16Array(n);!function e(r,o=0){const a=2*r,l=s[a+15]===dY;if(l){const e=i[r+6],c=s[a+14];t(o,l,new Float32Array(n,4*r,6),e,c)}else{const s=r+8,a=i[r+6],c=i[r+7];t(o,l,new Float32Array(n,4*r,6),c)||(e(s,o+1),e(a,o+1))}}(0)}raycast(t,e=0){const n=this._roots,i=this.geometry,s=[],r=e.isMaterial,o=Array.isArray(e),a=i.groups,l=r?e.side:e;for(let r=0,c=n.length;r{const r=3*n;return t(e,r,r+1,r+2,i,s)}}t={boundsTraverseOrder:n,intersectsBounds:t,intersectsTriangle:e,intersectsRange:null},console.warn(\\\\\\\"MeshBVH: Shapecast function signature has changed and now takes an object of callbacks as a second argument. See docs for new signature.\\\\\\\")}const s=c$.getPrimitive();let{boundsTraverseOrder:r,intersectsBounds:o,intersectsRange:a,intersectsTriangle:l}=t;if(a&&l){const t=a;a=(e,n,r,o,a)=>!!t(e,n,r,o,a)||NY(e,n,i,l,r,o,s)}else a||(a=l?(t,e,n,r)=>NY(t,e,i,l,n,r,s):(t,e,n)=>n);let c=!1,h=0;for(const t of this._roots){if($Y(t),c=GY(0,i,o,a,r,h),JY(),c)break;h+=t.byteLength}return c$.releasePrimitive(s),c}bvhcast(t,e,n){let{intersectsRanges:i,intersectsTriangles:s}=n;const r=t.geometry,o=r.index,a=r.attributes.position;t$.copy(e).invert();const l=c$.getPrimitive(),c=c$.getPrimitive();if(s){function h(t,n,i,r,h,u,d,p){for(let _=i,m=i+r;_KY.intersectsBox(t),intersectsRange:(e,n,s,r,o,a)=>(QY.copy(a),QY.applyMatrix4(t$),t.shapecast({intersectsBounds:t=>QY.intersectsBox(t),intersectsRange:(t,s,a,l,c)=>i(e,n,t,s,r,o,l,c)}))});return c$.releasePrimitive(l),c$.releasePrimitive(c),u}intersectsBox(t,e){return e$.set(t.min,t.max,e),e$.needsUpdate=!0,this.shapecast({intersectsBounds:t=>e$.intersectsBox(t),intersectsTriangle:t=>e$.intersectsTriangle(t)})}intersectsSphere(t){return this.shapecast({intersectsBounds:e=>t.intersectsBox(e),intersectsTriangle:e=>e.intersectsSphere(t)})}closestPointToGeometry(t,e,n={},i={},s=0,r=1/0){t.boundingBox||t.computeBoundingBox(),e$.set(t.boundingBox.min,t.boundingBox.max,e),e$.needsUpdate=!0;const o=this.geometry,a=o.attributes.position,l=o.index,c=t.attributes.position,h=t.index,u=c$.getPrimitive(),d=c$.getPrimitive();let p=s$,_=r$,m=null,f=null;i&&(m=o$,f=a$);let g=1/0,v=null,y=null;return t$.copy(e).invert(),n$.matrix.copy(t$),this.shapecast({boundsTraverseOrder:t=>e$.distanceToBox(t,Math.min(g,r)),intersectsBounds:(t,e,n)=>n{if(t.boundsTree)return t.boundsTree.shapecast({boundsTraverseOrder:t=>n$.distanceToBox(t,Math.min(g,r)),intersectsBounds:(t,e,n)=>n{for(let o=3*t,x=3*(t+r);o(i$.copy(t).clamp(e.min,e.max),i$.distanceToSquared(t)),intersectsBounds:(t,e,n)=>n{e.closestPointToPoint(t,i$);const i=t.distanceToSquared(i$);return i{eY(0,new Float32Array(e),l$),t.union(l$)})),t}}const u$=h$.prototype.raycast;h$.prototype.raycast=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"raycast\\\\\\\" has changed. See docs for new signature.');const[e,n,i,s]=t;return u$.call(this,i,e.material).forEach((t=>{(t=SY(t,e,n))&&s.push(t)})),s}return u$.apply(this,t)};const d$=h$.prototype.raycastFirst;h$.prototype.raycastFirst=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"raycastFirst\\\\\\\" has changed. See docs for new signature.');const[e,n,i]=t;return SY(d$.call(this,i,e.material),e,n)}return d$.apply(this,t)};const p$=h$.prototype.closestPointToPoint;h$.prototype.closestPointToPoint=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"closestPointToPoint\\\\\\\" has changed. See docs for new signature.'),t.unshift();const e=t[1],n={};return t[1]=n,p$.apply(this,t),e&&e.copy(n.point),n.distance}return p$.apply(this,t)};const _$=h$.prototype.closestPointToGeometry;h$.prototype.closestPointToGeometry=function(...t){const e=t[2],n=t[3];if(e&&e.isVector3||n&&n.isVector3){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"closestPointToGeometry\\\\\\\" has changed. See docs for new signature.');const i={},s={},r=t[1];return t[2]=i,t[3]=s,_$.apply(this,t),e&&e.copy(i.point),n&&n.copy(s.point).applyMatrix4(r),i.distance}return _$.apply(this,t)};const m$=h$.prototype.refit;h$.prototype.refit=function(...t){const e=t[0],n=t[1];if(n&&(n instanceof Set||Array.isArray(n))){console.warn('MeshBVH: The function signature for \\\\\\\"refit\\\\\\\" has changed. See docs for new signature.');const t=new Set;n.forEach((e=>t.add(e))),e&&e.forEach((e=>t.add(e))),m$.call(this,t)}else m$.apply(this,t)},[\\\\\\\"intersectsGeometry\\\\\\\",\\\\\\\"shapecast\\\\\\\",\\\\\\\"intersectsBox\\\\\\\",\\\\\\\"intersectsSphere\\\\\\\"].forEach((t=>{const e=h$.prototype[t];h$.prototype[t]=function(...n){return(null===n[0]||n[0].isMesh)&&(n.shift(),console.warn(`MeshBVH: The function signature for \\\\\\\"${t}\\\\\\\" has changed and no longer takes Mesh. See docs for new signature.`)),e.apply(this,n)}}));const f$=new tw,g$=new ew,v$=MT.prototype.raycast;function y$(t,e){if(this.geometry.boundsTree){if(void 0===this.material)return;g$.copy(this.matrixWorld).invert(),f$.copy(t.ray).applyMatrix4(g$);const n=this.geometry.boundsTree;if(!0===t.firstHitOnly){const i=SY(n.raycastFirst(f$,this.material),this,t);i&&e.push(i)}else{const i=n.raycast(f$,this.material);for(let n=0,s=i.length;n{if(t===i||e)return r++,!0;s&&r++}),n);let o=0;const a=new Float32Array(24*r);let l,c;e.traverse(((t,e,n)=>{const r=t===i||e;if(r||s){eY(0,n,x$);const{min:t,max:e}=x$;for(let n=-1;n<=1;n+=2){const i=n<0?t.x:e.x;for(let n=-1;n<=1;n+=2){const s=n<0?t.y:e.y;for(let n=-1;n<=1;n+=2){const r=n<0?t.z:e.z;a[o+0]=i,a[o+1]=s,a[o+2]=r,o+=3}}}return r}}),n),c=this.displayEdges?new Uint8Array([0,4,1,5,2,6,3,7,0,2,1,3,4,6,5,7,0,1,2,3,4,5,6,7]):new Uint8Array([0,1,2,2,1,3,4,6,5,6,7,5,1,4,5,0,4,1,2,3,6,3,7,6,0,2,4,2,6,4,1,5,3,3,5,7]),l=a.length>65535?new Uint32Array(c.length*r):new Uint16Array(c.length*r);const h=c.length;for(let t=0;te;)this._roots.pop();for(let t=0;t=this._roots.length){const e=new b$(this.mesh,this.edgeMaterial,this.depth,t);this.add(e),this._roots.push(e)}const e=this._roots[t];e.depth=this.depth,e.mesh=this.mesh,e.displayParents=this.displayParents,e.displayEdges=this.displayEdges,e.material=this.displayEdges?this.edgeMaterial:this.meshMaterial,e.update()}}updateMatrixWorld(...t){this.position.copy(this.mesh.position),this.rotation.copy(this.mesh.rotation),this.scale.copy(this.mesh.scale),super.updateMatrixWorld(...t)}copy(t){this.depth=t.depth,this.mesh=t.mesh}clone(){return new w$(this.mesh,this.depth)}dispose(){this.edgeMaterial.dispose(),this.meshMaterial.dispose();const t=this.children;for(let e=0,n=t.length;e{const i=t;if(i.isMesh){if(e.keepOnlyPosition){const t=i.geometry;for(const e in t.attributes)\\\\\\\"position\\\\\\\"!==e&&t.deleteAttribute(e)}n.push(i)}}))}const i=this._makeCompact(n);if(i){i.matrixAutoUpdate=!1,i.raycast=y$;const t=new h$(i.geometry,{verbose:!1});return i.geometry.boundsTree=t,this.createCoreGroupFromObjects([i])}return this.createCoreGroupFromObjects([])}_makeCompact(t){var e,n;const i=[];let s;for(let e of t){s=s||e.material;const t=e.geometry;t.applyMatrix4(e.matrix),i.push(t)}try{const t=fr.mergeGeometries(i);if(t){return this.createObject(t,Ns.MESH,s)}null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(n=this.states)||void 0===n||n.error.set(t.message)}}}T$.DEFAULT_PARAMS={keepOnlyPosition:!1},T$.INPUT_CLONED_STATE=Ki.ALWAYS;const A$=T$.DEFAULT_PARAMS;const M$=new class extends ua{constructor(){super(...arguments),this.keepOnlyPosition=ha.BOOLEAN(A$.keepOnlyPosition)}};class E$ extends lV{constructor(){super(...arguments),this.paramsConfig=M$}static type(){return\\\\\\\"BVH\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create BVH from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(T$.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new T$(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class S$ extends sV{static type(){return\\\\\\\"BVHVisualizer\\\\\\\"}cook(t,e){const n=t[0].objects()[0],i=new w$(n,e.depth);return i.opacity=1,i.update(),this.createCoreGroupFromObjects([i])}}S$.DEFAULT_PARAMS={depth:0},S$.INPUT_CLONED_STATE=Ki.NEVER;const C$=S$.DEFAULT_PARAMS;const N$=new class extends ua{constructor(){super(...arguments),this.depth=ha.INTEGER(C$.depth,{range:[0,20],rangeLocked:[!0,!1]})}};class L$ extends lV{constructor(){super(...arguments),this.paramsConfig=N$}static type(){return\\\\\\\"BVHVisualizer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry with bvh\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(S$.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new S$(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class O$ extends C.a{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}O$.prototype.isInstancedBufferAttribute=!0;const P$=new A.a,R$=new A.a,I$=[],F$=new B.a;class D$ extends B.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new O$(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(F$.geometry=this.geometry,F$.material=this.material,void 0!==F$.material)for(let s=0;st.far||e.push({distance:a,point:z$.clone(),uv:tr.a.getUV(z$,j$,W$,q$,X$,Y$,$$,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function Z$(t,e,n,i,s,r){G$.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(V$.x=r*G$.x-s*G$.y,V$.y=s*G$.x+r*G$.y):V$.copy(G$),t.copy(e),t.x+=V$.x,t.y+=V$.y,t.applyMatrix4(H$)}J$.prototype.isSprite=!0;var Q$=n(92),K$=n(81),tJ=n(46);class eJ{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,s=t.z,r=this.coefficients;return e.copy(r[0]).multiplyScalar(.282095),e.addScaledVector(r[1],.488603*i),e.addScaledVector(r[2],.488603*s),e.addScaledVector(r[3],.488603*n),e.addScaledVector(r[4],n*i*1.092548),e.addScaledVector(r[5],i*s*1.092548),e.addScaledVector(r[6],.315392*(3*s*s-1)),e.addScaledVector(r[7],n*s*1.092548),e.addScaledVector(r[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,s=t.z,r=this.coefficients;return e.copy(r[0]).multiplyScalar(.886227),e.addScaledVector(r[1],1.023328*i),e.addScaledVector(r[2],1.023328*s),e.addScaledVector(r[3],1.023328*n),e.addScaledVector(r[4],.858086*n*i),e.addScaledVector(r[5],.858086*i*s),e.addScaledVector(r[6],.743125*s*s-.247708),e.addScaledVector(r[7],.858086*n*s),e.addScaledVector(r[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,s=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*s,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*s,e[6]=.315392*(3*s*s-1),e[7]=1.092548*n*s,e[8]=.546274*(n*n-i*i)}}eJ.prototype.isSphericalHarmonics3=!0;class nJ extends cv.a{constructor(t=new eJ,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}nJ.prototype.isLightProbe=!0;var iJ=n(63),sJ=n(43);class rJ extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}rJ.prototype.isInstancedBufferGeometry=!0;class oJ extends Vf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Gf.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const s=t.interleavedBuffers[i],r=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],s=new Uint32Array(i).buffer;return n[e]=s,s}(t,s.buffer),o=Object(It.c)(s.type,r),a=new cr.a(o,s.stride);return a.uuid=s.uuid,e[i]=a,a}const s=t.isInstancedBufferGeometry?new rJ:new S.a,r=t.data.index;if(void 0!==r){const t=Object(It.c)(r.type,r.array);s.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let r;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);r=new hr.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(It.c)(n.type,n.array);r=new(n.isInstancedBufferAttribute?O$:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(r.name=n.name),void 0!==n.usage&&r.setUsage(n.usage),void 0!==n.updateRange&&(r.updateRange.offset=n.updateRange.offset,r.updateRange.count=n.updateRange.count),s.setAttribute(e,r)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],r=[];for(let e=0,s=n.length;eNumber.EPSILON){const u=Math.sqrt(h),p=Math.sqrt(l*l+c*c),_=e.x-a/u,m=e.y+o/u,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,s=m+a*f-t.y;const g=i*i+s*s;if(g<=2)return new d.a(i,s);r=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new d.a(i/r,s/r)}const R=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Xg.b(e);s=new iJ.a(n),s.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new iJ.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{IJ.PARAM_CALLBACK_reset(t,e)}})}};class IJ extends lV{constructor(){super(...arguments),this.paramsConfig=RJ}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new NJ,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const FJ={direction:new p.a(0,1,0)},DJ=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],BJ=new p.a(0,0,1);const zJ=new class extends ua{constructor(){super(...arguments),this.camera=ha.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:ds}}),this.direction=ha.VECTOR3(FJ.direction),this.offset=ha.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=ha.BOOLEAN(!0),this.stepSize=ha.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=ha.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=ha.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=ha.BOOLEAN(1),this.update=ha.BUTTON(null,{callback:t=>{kJ.PARAM_CALLBACK_update(t)}})}};class kJ extends lV{constructor(){super(...arguments),this.paramsConfig=zJ,this._plane=new Y.a,this._raycaster=new QL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new vU,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(ts.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!ds.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of DJ){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),s=this._planeCorners[0].distanceTo(this._planeCorners[1]),r=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(s,r)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,BJ,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class UJ extends sV{constructor(){super(...arguments),this._pointPosition=new p.a}static type(){return\\\\\\\"cameraProject\\\\\\\"}cook(t,e){var n,i;const s=t[0],r=e.camera.nodeWithContext(ts.OBJ,null===(n=this.states)||void 0===n?void 0:n.error);if(r){const t=r.object;for(let n of s.points())n.getPosition(this._pointPosition),e.unproject?this._pointPosition.unproject(t):this._pointPosition.project(t),n.setPosition(this._pointPosition)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"cameraNode invalid.\\\\\\\");return s}}UJ.DEFAULT_PARAMS={camera:new yi(\\\\\\\"\\\\\\\"),unproject:!1},UJ.INPUT_CLONED_STATE=Ki.FROM_NODE;const GJ=UJ.DEFAULT_PARAMS;const VJ=new class extends ua{constructor(){super(...arguments),this.camera=ha.NODE_PATH(GJ.camera.path(),{nodeSelection:{context:ts.OBJ,types:ds}}),this.unproject=ha.BOOLEAN(GJ.unproject)}};class HJ extends lV{constructor(){super(...arguments),this.paramsConfig=VJ}static type(){return\\\\\\\"cameraProject\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to project/unproject\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(UJ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new UJ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jJ extends sV{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),s=new Array(3*i.length);s.fill(0);for(let t=0;t0&&(r.push(t-1),r.push(t));r.push(e-1),r.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r),a}}const YJ=new p.a(0,0,1);class $J extends sV{constructor(){super(...arguments),this._core_transform=new vU}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=XJ.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,YJ,t.direction),this.createCoreGroupFromGeometry(e,Ns.LINE_SEGMENTS)}_create_disk(t){const e=new aJ(t.radius,t.segments);return this._core_transform.rotate_geometry(e,YJ,t.direction),this.createCoreGroupFromGeometry(e)}}$J.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const JJ=$J.DEFAULT_PARAMS;const ZJ=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(JJ.radius),this.segments=ha.INTEGER(JJ.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=ha.BOOLEAN(JJ.open),this.arcAngle=ha.FLOAT(JJ.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=ha.VECTOR3(JJ.direction)}};class QJ extends lV{constructor(){super(...arguments),this.paramsConfig=ZJ}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new $J(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var KJ;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(KJ||(KJ={}));const tZ=[KJ.SEGMENTS_COUNT,KJ.SEGMENTS_LENGTH];var eZ;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(eZ||(eZ={}));const nZ=[eZ.ABC,eZ.ACB,eZ.AB,eZ.AC,eZ.BC];class iZ{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let s=0;for(s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new S.a;if(r.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),r.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&r.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=r}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case KJ.SEGMENTS_COUNT:return this.params.segmentsCount+1;case KJ.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}os.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case eZ.ABC:case eZ.ACB:case eZ.AB:case eZ.AC:return this.x.copy(this.an);case eZ.BC:return this.x.copy(this.bn)}os.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case eZ.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case eZ.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case eZ.AB:return void(this.angle=this.angle_ab);case eZ.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case eZ.BC:return void(this.angle=this.angle_bc)}os.unreachable(t)}}const sZ=new class extends ua{constructor(){super(...arguments),this.arc=ha.BOOLEAN(1),this.pointsCountMode=ha.INTEGER(tZ.indexOf(KJ.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:tZ.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=ha.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:tZ.indexOf(KJ.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=ha.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:tZ.indexOf(KJ.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=ha.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=ha.INTEGER(nZ.indexOf(eZ.ABC),{visibleIf:{arc:1,full:0},menu:{entries:nZ.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=ha.BOOLEAN(1),this.addIdnAttribute=ha.BOOLEAN(1),this.center=ha.BOOLEAN(0)}};class rZ extends lV{constructor(){super(...arguments),this.paramsConfig=sZ,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new iZ({arc:this.pv.arc,center:this.pv.center,pointsCountMode:tZ[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:nZ[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Ns.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Ns.POINTS)),this.setObjects(n)}}const oZ=new p.a,aZ=new Oy;class lZ extends sV{constructor(){super(...arguments),this._plane=new Y.a}static type(){return\\\\\\\"clip\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo()[0];this._plane.set(e.direction,e.distance),this._plane.translate(e.origin);const i=this._createClipGeo(n);return i?this.createCoreGroupFromObjects([i]):this.createCoreGroupFromObjects([])}_createClipGeo(t){var e;const n=t;if(!n.geometry.boundsTree)return void(null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"no BVH found on input geo, make sure to use a BVH SOP node\\\\\\\"));const i=n.geometry.boundsTree,s=t=>{let e=0;return i.shapecast({intersectsBounds:t=>this._plane.intersectsBox(t),intersectsTriangle:n=>{let i=0;aZ.start.copy(n.a),aZ.end.copy(n.b),this._plane.intersectLine(aZ,oZ)&&(null==t||t.setXYZ(e,oZ.x,oZ.y,oZ.z),e++,i++),aZ.start.copy(n.b),aZ.end.copy(n.c),this._plane.intersectLine(aZ,oZ)&&(null==t||t.setXYZ(e,oZ.x,oZ.y,oZ.z),i++,e++),aZ.start.copy(n.c),aZ.end.copy(n.a),this._plane.intersectLine(aZ,oZ)&&(null==t||t.setXYZ(e,oZ.x,oZ.y,oZ.z),i++,e++),2!==i&&(e-=i)}}),{index:e}},{index:r}=s(),o=new S.a,a=new C.a(new Float32Array(3*r),3,!1);o.setAttribute(\\\\\\\"position\\\\\\\",a);const l=new Ms.a(o,js.MATERIALS[Ns.LINE_SEGMENTS]);l.frustumCulled=!1;return s(l.geometry.attributes.position),l}}lZ.DEFAULT_PARAMS={origin:new p.a(0,0,0),distance:0,direction:new p.a(0,1,0)},lZ.INPUT_CLONED_STATE=Ki.NEVER;const cZ=lZ.DEFAULT_PARAMS;const hZ=new class extends ua{constructor(){super(...arguments),this.origin=ha.VECTOR3(cZ.origin),this.distance=ha.FLOAT(cZ.distance,{range:[-10,10],rangeLocked:[!1,!1]}),this.direction=ha.VECTOR3(cZ.direction)}};class uZ extends lV{constructor(){super(...arguments),this.paramsConfig=hZ}static type(){return\\\\\\\"clip\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(lZ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new lZ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class dZ extends sV{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}dZ.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const pZ=new D.a(1,1,1),_Z=\\\\\\\"color\\\\\\\",mZ=dZ.DEFAULT_PARAMS;const fZ=new class extends ua{constructor(){super(...arguments),this.fromAttribute=ha.BOOLEAN(mZ.fromAttribute),this.attribName=ha.STRING(mZ.attribName,{visibleIf:{fromAttribute:1}}),this.color=ha.COLOR(mZ.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=ha.BOOLEAN(mZ.asHsv,{visibleIf:{fromAttribute:0}})}};class gZ extends lV{constructor(){super(...arguments),this.paramsConfig=fZ,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(_Z).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,pZ);const n=e.points(),i=e.attribSize(this.pv.attribName),s=e.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(_Z).array;switch(i){case 1:for(let t=0;t{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=r;return a}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(bZ.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):bZ.PSCALE;this._point_scale.multiplyScalar(i);const s=this._matrices.scale;s.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const r=this._matrices.translate;if(r.makeTranslation(n.x,n.y,n.z),e.multiply(r),this._do_rotate_matrices){const n=this._matrices.rotate,i=bZ.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(bZ.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(s)}static create_instance_buffer_geo(t,e,n){const i=e.points(),s=new rJ;s.copy(t),s.instanceCount=1/0;const r=i.length,o=new Float32Array(3*r),a=new Float32Array(3*r),l=new Float32Array(3*r),c=new Float32Array(4*r),h=e.hasAttrib(AZ),u=new p.a(0,0,0),d=new hh.a,_=new p.a(1,1,1),f=new SZ(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(u,d,_),u.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(h?t.attribValue(AZ,this._point_color):wZ).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*r);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):TZ).toArray(t,i)})),s.setAttribute(MZ.UV,new O$(t,2))}s.setAttribute(MZ.POSITION,new O$(o,3)),s.setAttribute(MZ.SCALE,new O$(l,3)),s.setAttribute(MZ.ORIENTATION,new O$(c,4)),s.setAttribute(MZ.COLOR,new O$(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(r*n);i.forEach(((e,i)=>{const s=e.attribValue(t);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(t,new O$(o,n))}));return new fr(s).markAsInstance(),s}}SZ._point_color=new p.a,SZ._point_uv=new d.a;class CZ extends ah{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}(EZ||(EZ={}));const NZ=[EZ.OBJECT,EZ.GEOMETRY],LZ=[{name:\\\\\\\"object\\\\\\\",value:EZ.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:EZ.GEOMETRY}];const OZ=new class extends ua{constructor(){super(...arguments),this.count=ha.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=ha.BOOLEAN(0),this.transformMode=ha.INTEGER(0,{menu:{entries:LZ}}),this.copyAttributes=ha.BOOLEAN(0),this.attributesToCopy=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=ha.BOOLEAN(0)}};class PZ extends lV{constructor(){super(...arguments),this.paramsConfig=OZ,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.ALWAYS,Ki.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points(),i=new SZ(e).matrices();this._attribute_names_to_copy=ss.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=br.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const s=e.attribValue(n);new br(t,i).addAttribute(n,s)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new CZ(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const RZ=\\\\\\\"id\\\\\\\",IZ=\\\\\\\"class\\\\\\\",FZ=\\\\\\\"html\\\\\\\";class DZ extends sV{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(RZ):e.id,s=e.useClassAttrib?t.attribValue(IZ):e.className,r=e.useHTMLAttrib?t.attribValue(FZ):e.html,o=DZ.create_css_object({id:n,className:s,html:r}),a=o.element;if(e.copyAttributes){const n=ss.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return DZ.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new cq(e);return n.matrixAutoUpdate=!1,n}}DZ.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"myCSSObject\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHTMLAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},DZ.INPUT_CLONED_STATE=Ki.NEVER;const BZ=DZ.DEFAULT_PARAMS;const zZ=new class extends ua{constructor(){super(...arguments),this.useIdAttrib=ha.BOOLEAN(BZ.useIdAttrib),this.id=ha.STRING(BZ.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=ha.BOOLEAN(BZ.useClassAttrib),this.className=ha.STRING(BZ.className,{visibleIf:{useClassAttrib:0}}),this.useHTMLAttrib=ha.BOOLEAN(BZ.useHTMLAttrib),this.html=ha.STRING(BZ.html,{visibleIf:{useHTMLAttrib:0},multiline:!0}),this.copyAttributes=ha.BOOLEAN(BZ.copyAttributes),this.attributesToCopy=ha.STRING(BZ.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class kZ extends lV{constructor(){super(...arguments),this.paramsConfig=zZ}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(DZ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new DZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class UZ{constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?zs.STRING:zs.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class GZ{constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new fr(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=ss.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=Xs.remapName(n);let r=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===zs.STRING)if(this._options.doConvert&&ss.matchesOneMask(n,i)){const e=r.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(s,new C.c(e,a))}else{const t=Xs.arrayToIndexedArrays(r);e.setIndexedAttribute(s,t.values,t.indices)}else{const e=r;t.setAttribute(s,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=ss.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const s=[n,t].join(\\\\\\\":\\\\\\\"),r=i[n];ss.matchesOneMask(s,e)||(this._attribute_datas_by_name[s]=UZ.from_value(r))}else ss.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=UZ.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const VZ=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const HZ=new class extends ua{constructor(){super(...arguments),this.data=ha.STRING(VZ)}};class jZ extends lV{constructor(){super(...arguments),this.paramsConfig=HZ}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new GZ;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Ns.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class WZ extends $g{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new GZ(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const s=this._parser.createObject();t(s)})).catch((t=>{li.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const qZ=\\\\\\\"position\\\\\\\";class XZ extends $g{constructor(t,e,n,i){super(t,e,i),this.attribute_names=n,this._node=i,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.loadData(),this.infer_types(),this.read_values();return this.create_points()}async loadData(){const t=await this._urlToLoad(),e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(XZ.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>Xs.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(XZ.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=ha.STRING(JZ,{fileBrowse:{type:[Rr.JSON]}}),this.jsonDataKeysPrefix=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:$Z.indexOf(YZ.JSON)}}),this.skipEntries=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:$Z.indexOf(YZ.JSON)}}),this.convert=ha.BOOLEAN(0,{visibleIf:{dataType:$Z.indexOf(YZ.JSON)}}),this.convertToNumeric=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:$Z.indexOf(YZ.JSON),convert:1}}),this.readAttribNamesFromFile=ha.BOOLEAN(1,{visibleIf:{dataType:$Z.indexOf(YZ.CSV)}}),this.attribNames=ha.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:$Z.indexOf(YZ.CSV),readAttribNamesFromFile:0}}),this.reload=ha.BUTTON(null,{callback:(t,e)=>{QZ.PARAM_CALLBACK_reload(t,e)}})}};class QZ extends lV{constructor(){super(...arguments),this.paramsConfig=ZZ}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){this._load()}_load(){switch($Z[this.pv.dataType]){case YZ.JSON:return this._loadJSON();case YZ.CSV:return this._loadCSV()}}_clearLoadedBlob(){switch($Z[this.pv.dataType]){case YZ.JSON:return this._resetJSON();case YZ.CSV:return this._resetCSV()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_loadJSON(){new WZ(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._onLoad.bind(this),void 0,this._onError.bind(this))}_resetJSON(){new WZ(this._url(),this.scene()).deregisterUrl()}_onLoad(t){this.setGeometry(t,Ns.POINTS)}_onError(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _loadCSV(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new XZ(this._url(),this.scene(),t,this),n=await e.load();n?this.setGeometry(n,Ns.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}_resetCSV(){new XZ(this._url(),this.scene(),[],this).deregisterUrl()}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this._clearLoadedBlob(),this.p.url.setDirty()}}class KZ extends S.a{constructor(t,e,n,i){super();const s=[],r=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function h(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new tQ(n.clone(),i.clone()))}function u(t,e){const n=[],s=.5*Math.abs(i.dot(e));for(let i=0;i0,u=t[i+1].position.dot(e)-s>0,p=t[i+2].position.dot(e)-s>0;switch(c=(h?1:0)+(u?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(h&&(r=t[i+1],o=t[i+2],a=d(t[i],r,e,s),l=d(t[i],o,e,s)),u){r=t[i],o=t[i+2],a=d(t[i+1],r,e,s),l=d(t[i+1],o,e,s),n.push(a),n.push(o.clone()),n.push(r.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(r=t[i],o=t[i+1],a=d(t[i+2],r,e,s),l=d(t[i+2],o,e,s)),n.push(r.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:h||(r=t[i].clone(),o=d(r,t[i+1],e,s),a=d(r,t[i+2],e,s),n.push(r),n.push(o),n.push(a)),u||(r=t[i+1].clone(),o=d(r,t[i+2],e,s),a=d(r,t[i],e,s),n.push(r),n.push(o),n.push(a)),p||(r=t[i+2].clone(),o=d(r,t[i],e,s),a=d(r,t[i+1],e,s),n.push(r),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const s=t.position.dot(n)-i,r=s/(s-(e.position.dot(n)-i));return new tQ(new p.a(t.position.x+r*(e.position.x-t.position.x),t.position.y+r*(e.position.y-t.position.y),t.position.z+r*(e.position.z-t.position.z)),new p.a(t.normal.x+r*(e.normal.x-t.normal.x),t.normal.y+r*(e.normal.y-t.normal.y),t.normal.z+r*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class aQ{constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,s)=>{n==e&&(t[i]=s,i++)})),t}}}var lQ;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(lQ||(lQ={}));const cQ=[lQ.EQUAL,lQ.LESS_THAN,lQ.EQUAL_OR_LESS_THAN,lQ.EQUAL_OR_GREATER_THAN,lQ.GREATER_THAN,lQ.DIFFERENT],hQ={[lQ.EQUAL]:(t,e)=>t==e,[lQ.LESS_THAN]:(t,e)=>tt<=e,[lQ.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[lQ.GREATER_THAN]:(t,e)=>t>e,[lQ.DIFFERENT]:(t,e)=>t!=e},uQ=cQ.map(((t,e)=>({name:t,value:e})));class dQ{constructor(t){this.node=t}evalForEntities(t){const e=ks[this.node.pv.attribType];switch(e){case zs.NUMERIC:return void this._eval_for_numeric(t);case zs.STRING:return void this._eval_for_string(t)}os.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attribString&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Vs[this.node.pv.attribSize-1];switch(e){case Gs.FLOAT:return this._eval_for_points_numeric_float(t);case Gs.VECTOR2:return this._eval_for_points_numeric_vector2(t);case Gs.VECTOR3:return this._eval_for_points_numeric_vector3(t);case Gs.VECTOR4:return this._eval_for_points_numeric_vector4(t)}os.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=cQ[this.node.pv.attribComparisonOperator],r=hQ[s];for(let s of t)i=s.attribValue(e),r(i,n)&&this.node.entitySelectionHelper.select(s)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}}class pQ{constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.computeExpressionForEntities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class _Q{constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new Cy.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class mQ{constructor(t){this.node=t}eval_for_objects(t){const e=Ps[this.node.pv.objectType];for(let n of t){Os(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class fQ{constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const gQ=new p.a(0,1,0),vQ=new p.a(0,-1,0);class yQ{constructor(t){this.node=t,this._matDoubleSideTmpSetter=new fQ,this._point_position=new p.a,this._raycaster=new QL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const s=n.geometry;s.computeBoundingBox();const r=s.boundingBox;for(let e of t)e.getPosition(this._point_position),r.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,gQ)&&this._isPositionInObject(this._point_position,i,vQ)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const s=this._raycaster.intersectObject(e,!1,this._intersections);if(!s)return!1;if(0==s.length)return!1;const r=null===(i=s[0].face)||void 0===i?void 0:i.normal;if(!r)return!1;return this._raycaster.ray.direction.dot(r)>=0}}const xQ=new class extends ua{constructor(){super(...arguments),this.class=ha.INTEGER(Ds.indexOf(Fs.VERTEX),{menu:{entries:Bs}}),this.invert=ha.BOOLEAN(0),this.byObjectType=ha.BOOLEAN(0,{visibleIf:{class:Ds.indexOf(Fs.OBJECT)}}),this.objectType=ha.INTEGER(Ps.indexOf(Ns.MESH),{menu:{entries:Rs},visibleIf:{class:Ds.indexOf(Fs.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=ha.BOOLEAN(0),this.expression=ha.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=ha.BOOLEAN(0),this.attribType=ha.INTEGER(ks.indexOf(zs.NUMERIC),{menu:{entries:Us},visibleIf:{byAttrib:1}}),this.attribName=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=ha.INTEGER(1,{range:Hs,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.NUMERIC)}}),this.attribComparisonOperator=ha.INTEGER(cQ.indexOf(lQ.EQUAL),{menu:{entries:uQ},visibleIf:{byAttrib:!0,attribType:ks.indexOf(zs.NUMERIC),attribSize:Gs.FLOAT}}),this.attribValue1=ha.FLOAT(0,{visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.NUMERIC),attribSize:1}}),this.attribValue2=ha.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.NUMERIC),attribSize:2}}),this.attribValue3=ha.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.NUMERIC),attribSize:3}}),this.attribValue4=ha.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.NUMERIC),attribSize:4}}),this.attribString=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:ks.indexOf(zs.STRING)},separatorAfter:!0}),this.byBbox=ha.BOOLEAN(0,{visibleIf:{class:Ds.indexOf(Fs.VERTEX)}}),this.bboxSize=ha.VECTOR3([1,1,1],{visibleIf:{class:Ds.indexOf(Fs.VERTEX),byBbox:!0}}),this.bboxCenter=ha.VECTOR3([0,0,0],{visibleIf:{class:Ds.indexOf(Fs.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=ha.BOOLEAN(0,{visibleIf:{class:Ds.indexOf(Fs.VERTEX)}}),this.keepPoints=ha.BOOLEAN(0,{visibleIf:{class:Ds.indexOf(Fs.OBJECT)}})}};class bQ extends lV{constructor(){super(...arguments),this.paramsConfig=xQ,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new aQ(this),this.byExpressionHelper=new pQ(this),this.byAttributeHelper=new dQ(this),this.byObjectTypeHelper=new mQ(this),this.byBboxHelper=new _Q(this),this.byBoundingObjectHelper=new yQ(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Fs.VERTEX:await this._eval_for_points(e,n);break;case Fs.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,s=[];for(let t=0;t0){const e=fr.geometryFromPoints(a,Os(t.constructor));e&&(t.geometry=e,s.push(t))}}}this.setObjects(s)}_point_object(t){const e=t.points(),n=fr.geometryFromPoints(e,Ns.POINTS);if(n)return this.createObject(n,Ns.POINTS)}}const wQ=new class extends ua{constructor(){super(...arguments),this.start=ha.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=ha.BOOLEAN(0),this.count=ha.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class TQ extends lV{constructor(){super(...arguments),this.paramsConfig=wQ}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class AQ{constructor(){this.pluginCallbacks=[],this.register((function(t){return new QQ(t)})),this.register((function(t){return new KQ(t)})),this.register((function(t){return new tK(t)})),this.register((function(t){return new eK(t)})),this.register((function(t){return new nK(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new ZQ,s=[];for(let t=0,e=this.pluginCallbacks.length;t0&&(this.options.trs=!0),this.processInput(t);const i=this;Promise.all(this.pending).then((function(){const t=i.buffers,n=i.json,s=i.options,r=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(r);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===s.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=$Q(t.result),s=new DataView(new ArrayBuffer(8));s.setUint32(0,i.byteLength,!0),s.setUint32(4,5130562,!0);const r=$Q(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let n=0,i=t.length;n255?32:i}return e.buffer}(JSON.stringify(n)),32),o=new DataView(new ArrayBuffer(8));o.setUint32(0,r.byteLength,!0),o.setUint32(4,1313821514,!0);const a=new ArrayBuffer(12),l=new DataView(a);l.setUint32(0,1179937895,!0),l.setUint32(4,2,!0);const c=12+o.byteLength+r.byteLength+s.byteLength+i.byteLength;l.setUint32(8,c,!0);const h=new Blob([a,o,r,s,i],{type:\\\\\\\"application/octet-stream\\\\\\\"}),u=new window.FileReader;u.readAsArrayBuffer(h),u.onloadend=function(){e(u.result)}}}else if(n.buffers&&n.buffers.length>0){const t=new window.FileReader;t.readAsDataURL(o),t.onloadend=function(){const i=t.result;n.buffers[0].uri=i,e(n)}}else e(n)}))}serializeUserData(t,e){if(0===Object.keys(t.userData).length)return;const n=this.options,i=this.extensionsUsed;try{const s=JSON.parse(JSON.stringify(t.userData));if(n.includeCustomExtensions&&s.gltfExtensions){void 0===e.extensions&&(e.extensions={});for(const t in s.gltfExtensions)e.extensions[t]=s.gltfExtensions[t],i[t]=!0;delete s.gltfExtensions}Object.keys(s).length>0&&(e.extras=s)}catch(e){console.warn(\\\\\\\"THREE.GLTFExporter: userData of '\\\\\\\"+t.name+\\\\\\\"' won't be serialized because of JSON.stringify error - \\\\\\\"+e.message)}}getUID(t){return this.uids.has(t)||this.uids.set(t,this.uid++),this.uids.get(t)}isNormalizedNormalAttribute(t){if(this.cache.attributesNormalized.has(t))return!1;const e=new Ab;for(let n=0,i=t.count;n5e-4)return!1;return!0}createNormalizedNormalAttribute(t){const e=this.cache;if(e.attributesNormalized.has(t))return e.attributesNormalized.get(t);const n=t.clone(),i=new Ab;for(let t=0,e=n.count;t4?i=t.array[s*t.itemSize+n]:0===n?i=t.getX(s):1===n?i=t.getY(s):2===n?i=t.getZ(s):3===n&&(i=t.getW(s)),e===PQ?l.setFloat32(c,i,!0):e===RQ?l.setUint32(c,i,!0):e===OQ?l.setUint16(c,i,!0):e===LQ&&l.setUint8(c,i),c+=o}const h={buffer:this.processBuffer(l.buffer),byteOffset:this.byteOffset,byteLength:a};void 0!==s&&(h.target=s),s===IQ&&(h.byteStride=t.itemSize*o),this.byteOffset+=a,r.bufferViews.push(h);return{id:r.bufferViews.length-1,byteLength:0}}processBufferViewImage(t){const e=this,n=e.json;return n.bufferViews||(n.bufferViews=[]),new Promise((function(i){const s=new window.FileReader;s.readAsArrayBuffer(t),s.onloadend=function(){const t=$Q(s.result),r={buffer:e.processBuffer(t),byteOffset:e.byteOffset,byteLength:t.byteLength};e.byteOffset+=t.byteLength,i(n.bufferViews.push(r)-1)}}))}processAccessor(t,e,n,i){const s=this.options,r=this.json;let o;if(t.array.constructor===Float32Array)o=PQ;else if(t.array.constructor===Uint32Array)o=RQ;else if(t.array.constructor===Uint16Array)o=OQ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=LQ}if(void 0===n&&(n=0),void 0===i&&(i=t.count),s.truncateDrawRange&&void 0!==e&&null===e.index){const s=n+i,r=e.drawRange.count===1/0?t.count:e.drawRange.start+e.drawRange.count;n=Math.max(n,e.drawRange.start),(i=Math.min(s,r)-n)<0&&(i=0)}if(0===i)return null;const a=function(t,e,n){const i={min:new Array(t.itemSize).fill(Number.POSITIVE_INFINITY),max:new Array(t.itemSize).fill(Number.NEGATIVE_INFINITY)};for(let s=e;s4?n=t.array[s*t.itemSize+e]:0===e?n=t.getX(s):1===e?n=t.getY(s):2===e?n=t.getZ(s):3===e&&(n=t.getW(s)),i.min[e]=Math.min(i.min[e],n),i.max[e]=Math.max(i.max[e],n)}return i}(t,n,i);let l;void 0!==e&&(l=t===e.index?FQ:IQ);const c=this.processBufferView(t,o,n,i,l),h={bufferView:c.id,byteOffset:c.byteOffset,componentType:o,count:i,max:a.max,min:a.min,type:{1:\\\\\\\"SCALAR\\\\\\\",2:\\\\\\\"VEC2\\\\\\\",3:\\\\\\\"VEC3\\\\\\\",4:\\\\\\\"VEC4\\\\\\\",16:\\\\\\\"MAT4\\\\\\\"}[t.itemSize]};return!0===t.normalized&&(h.normalized=!0),r.accessors||(r.accessors=[]),r.accessors.push(h)-1}processImage(t,e,n){const i=this,s=i.cache,r=i.json,o=i.options,a=i.pending;s.images.has(t)||s.images.set(t,{});const l=s.images.get(t),c=e===Rx?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",h=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[h])return l[h];r.images||(r.images=[]);const u={mimeType:c};if(o.embedImages){const s=JQ=JQ||document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.min(t.width,o.maxTextureSize),s.height=Math.min(t.height,o.maxTextureSize);const r=s.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(r.translate(0,s.height),r.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)r.drawImage(t,0,0,s.width,s.height);else{e!==Rx&&e!==Px&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===Rx)for(let e=0;e1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const r=n.materials.push(i)-1;return e.materials.set(t,r),r}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e0){const n=[],i=[],s={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)s[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===r.groups.length)return null;const m=_?t.material:[t.material],f=_?r.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=h),null!==r.index){let i=this.getUID(r.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(r.index,r,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(s,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:hb.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const s=(t=AQ.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,r=[],o=[];for(let t=0;t0){const e=[];for(let i=0,s=t.children.length;i0&&(s.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,s)}));const r=e.nodes.push(s)-1;return i.set(t,r),r}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const s=[];for(let e=0,i=t.children.length;e0&&(i.nodes=s),this.serializeUserData(t,i)}processObjects(t){const e=new FE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(r.range=t.distance)):t.isSpotLight&&(r.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(r.range=t.distance),r.spot={},r.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,r.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),s[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},s[this.name]=!0);const o=i.extensions[this.name].lights;o.push(r),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class KQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class tK{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,s={};e.pbrMetallicRoughness.baseColorFactor&&(s.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const r=[1,1,1];if(t.specular.toArray(r,0),s.specularFactor=r,s.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(s.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),s.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class eK{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),s.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class nK{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),s.thicknessTexture=e}s.attenuationDistance=t.attenuationDistance,s.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}function iK(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}AQ.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),s=new t.TimeBufferType(t.times.length+1),r=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){s[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){s.set(t.times.slice(0,l+1),0),s[l+1]=e,s.set(t.times.slice(l+1),l+2),r.set(t.values.slice(0,(l+1)*i),0),r.set(o.evaluate(e),(l+1)*i),r.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=s,t.values=r,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},s=t.tracks;for(let t=0;t{rK.PARAM_CALLBACK_export(t)}})}};class rK extends lV{constructor(){super(...arguments),this.paramsConfig=sK}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new vs;for(let t of n)i.add(t);(new AQ).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",iK(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){iK(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const oK=new class extends ua{constructor(){super(...arguments),this.makeFacesUnique=ha.BOOLEAN(0),this.addFaceCenterAttribute=ha.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=ha.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=ha.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=ha.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class aK extends lV{constructor(){super(...arguments),this.paramsConfig=oK}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),s=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],r=n.itemSize,o=new Float32Array(s*r);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e{const a=t.object(),l=t.coreGeometry();if(a.isMesh&&l){i=l.faces(),l.hasAttrib(e)||l.addNumericAttrib(e,3,-1);for(let t=0;t{const n=t.object(),i=t.coreGeometry();if(n.isMesh&&i){const t=i.faces();i.hasAttrib(e)||i.addNumericAttrib(e,1,-1);for(let n=0;n{const c=t.object(),h=t.coreGeometry();if(c.isMesh&&h){r=h.faces(),h.hasAttrib(e)||h.addNumericAttrib(e,3,-1);for(let t=0;t{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==lK.JSON&&this._options.format==lK.AUTO)uK.increment_in_progress_loads_count(),await uK.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new NJ(this.loadingManager).parse(n,(e=>{uK.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{uK.decrement_in_progress_loads_count(),e(t)}));else{const s=await this._loaderForFormat();if(s)uK.increment_in_progress_loads_count(),await uK.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(e=>{this.on_load_success(e).then((e=>{uK.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{li.warn(\\\\\\\"error loading\\\\\\\",n,t),uK.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==lK.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case hK.PDB:return this.on_load_succes_pdb(t);case hK.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case hK.DRC:return this.on_load_succes_drc(i);default:return[new B.a(i)]}const s=t;if(null!=s.scene)switch(e){case hK.GLTF:case hK.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=t;if(r.geometryAtoms||r.geometryBonds)switch(e){case hK.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(t){return[new B.a(t,uK._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new ys.a(t.geometryAtoms,uK._default_mat_point),new Ms.a(t.geometryBonds,uK._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case lK.AUTO:return this.moduleNamesFromExt(e);case lK.DRC:return[Hn.DRACOLoader];case lK.FBX:return[Hn.FBXLoader];case lK.JSON:return[];case lK.GLTF:return[Hn.GLTFLoader];case lK.GLTF_WITH_DRACO:return[Hn.GLTFLoader,Hn.DRACOLoader];case lK.OBJ:return[Hn.OBJLoader];case lK.PDB:return[Hn.PDBLoader];case lK.PLY:return[Hn.PLYLoader];case lK.STL:return[Hn.STLLoader]}os.unreachable(t)}static moduleNamesFromExt(t){switch(t){case hK.DRC:return[Hn.DRACOLoader];case hK.FBX:return[Hn.FBXLoader];case hK.GLTF:return[Hn.GLTFLoader];case hK.GLB:return[Hn.GLTFLoader,Hn.DRACOLoader];case hK.OBJ:return[Hn.OBJLoader];case hK.PDB:return[Hn.PDBLoader];case hK.PLY:return[Hn.PLYLoader];case hK.STL:return[Hn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case lK.AUTO:return this._loaderForExt();case lK.DRC:return this.loader_for_drc(this._node);case lK.FBX:return this.loader_for_fbx();case lK.JSON:return;case lK.GLTF:return this.loader_for_gltf();case lK.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case lK.OBJ:return this.loader_for_obj();case lK.PDB:return this.loader_for_pdb();case lK.PLY:return this.loader_for_ply();case lK.STL:return this.loader_for_stl()}os.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case hK.DRC:return this.loader_for_drc(this._node);case hK.FBX:return this.loader_for_fbx();case hK.GLTF:return this.loader_for_gltf();case hK.GLB:return this.loader_for_glb(this._node);case hK.OBJ:return this.loader_for_obj();case hK.PDB:return this.loader_for_pdb();case hK.PLY:return this.loader_for_ply();case hK.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=li.modulesRegister.module(Hn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=li.modulesRegister.module(Hn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=li.modulesRegister.module(Hn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.DRACOPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return uK.loader_for_drc(t)}static async loader_for_glb(t){const e=li.modulesRegister.module(Hn.GLTFLoader),n=li.modulesRegister.module(Hn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=li.libs.root(),s=li.libs.DRACOGLTFPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return uK.loader_for_glb(t)}loader_for_obj(){const t=li.modulesRegister.module(Hn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=li.modulesRegister.module(Hn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=li.modulesRegister.module(Hn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=li.modulesRegister.module(Hn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():ng.isChrome()?4:1}static _init_concurrent_loads_delay(){return ng.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}uK._default_mat_mesh=new Ts.a,uK._default_mat_point=new bs.a,uK._default_mat_line=new As.a,uK.MAX_CONCURRENT_LOADS_COUNT=uK._init_max_concurrent_loads_count(),uK.CONCURRENT_LOADS_DELAY=uK._init_concurrent_loads_delay(),uK.in_progress_loads_count=0,uK._queue=[];class dK extends sV{static type(){return\\\\\\\"file\\\\\\\"}static loader(t,e,n){return new uK({url:t.url,format:t.format},e,n)}cook(t,e){const n=dK.loader(e,this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._onLoad(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._onError(t,e)}))}))}clearLoadedBlob(t){dK.loader(t,this.scene(),this._node).deregisterUrl()}_onLoad(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensureGeometryHasIndex(t),t.matrixAutoUpdate=!1}));return t}_onError(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensureGeometryHasIndex(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}dK.DEFAULT_PARAMS={url:`${qg}/models/wolf.obj`,format:lK.AUTO};const pK=dK.DEFAULT_PARAMS;const _K=new class extends ua{constructor(){super(...arguments),this.url=ha.STRING(pK.url,{fileBrowse:{type:[Rr.GEOMETRY]}}),this.format=ha.STRING(pK.format,{menuString:{entries:cK.map((t=>({name:t,value:t})))}}),this.reload=ha.BUTTON(null,{callback:t=>{mK.PARAM_CALLBACK_reload(t)}})}};class mK extends lV{constructor(){super(...arguments),this.paramsConfig=_K}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=$g.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return uK.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}operation(){return this._operation=this._operation||new dK(this.scene(),this.states,this)}async cook(t){const e=await this.operation().cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.operation().clearLoadedBlob(this.pv),this.p.url.setDirty()}}const fK=new class extends ua{constructor(){super(...arguments),this.url=ha.STRING(`${qg}/models/\\\\`@name\\\\`.obj`,{fileBrowse:{type:[Rr.GEOMETRY]},expression:{forEntities:!0}}),this.format=ha.STRING(lK.AUTO,{menuString:{entries:cK.map((t=>({name:t,value:t})))}}),this.reload=ha.BUTTON(null,{callback:t=>{gK.PARAM_CALLBACK_reload(t)}})}};class gK extends lV{constructor(){super(...arguments),this.paramsConfig=fK}static type(){return\\\\\\\"fileMulti\\\\\\\"}async requiredModules(){const t=$g.extension(this.p.url.rawInput()||\\\\\\\"\\\\\\\"),e=this.pv.format;return uK.moduleNamesFromFormat(e,t)}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>this.p.url.rawInput()))}))}))}async cook(t){const e=t[0],n=new SZ(e).matrices(),i=e.points(),s=[],r=new Map,o=new Map,a=new Map,l=this.p.url;if(l.hasExpression()&&l.expressionController){const t=new Set;await l.expressionController.computeExpressionForPoints(i,((e,n)=>{const i=e.index();r.has(i)?this.states.error.set(\\\\\\\"input points have duplicate indices. Make sure to merge inputs together.\\\\\\\"):(r.set(i,n),t.add(n),gK._incrementUrlUsageCount(n,a))})),t.forEach((t=>{s.push(t)}))}else{const t=this.pv.url;s.push(t),gK._incrementUrlUsageCount(t,a)}const c=[],h=s.map((t=>this._loadFromUrlPromises(t,o)));await Promise.all(h);for(let t of i){const e=t.index(),i=r.get(e)||this.pv.url,s=n[e],l=a.get(i)||1;let h=o.get(i);h&&(l>1&&(h=h.clone()),h.applyMatrix4(s),c.push(h))}this.setObjects(c)}static _incrementUrlUsageCount(t,e){const n=e.get(t);null!=n?e.set(t,n+1):e.set(t,1)}async _loadFromUrlPromises(t,e){const n=await this._loadObject(t),i=new Fn.a;i.matrixAutoUpdate=!1,i.name=t;for(let t of n)i.add(t);e.set(t,i)}_loadObject(t){const e=new uK({url:t,format:this.pv.format},this.scene(),this);return new Promise((n=>{e.load((t=>{const e=this._onLoad(t);n(e)}),(e=>{this._onError(e,t)}))}))}_onLoad(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensureGeometryHasIndex(t),t.matrixAutoUpdate=!1}));return t}_onError(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e} (${t})`)}_ensureGeometryHasIndex(t){const e=t.geometry;e&&pr.createIndexIfNone(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const vK=new class extends ua{constructor(){super(...arguments),this.dist=ha.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class yK extends lV{constructor(){super(...arguments),this.paramsConfig=vK}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,s={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");s[n]=s[n]||[],s[n].push(t)}const r=[];if(Object.keys(s).forEach((t=>{r.push(s[t][0])})),e.geometry.dispose(),r.length>0){const t=fr.geometryFromPoints(r,Os(e.constructor));return t&&(e.geometry=t),e}}}class xK{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,s=Math.floor(this._param_size.x/n),r=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==r||r==s-1?0==r?a.push([r+1+(t-1)*s,r+(t-1)*s,r+t*s]):a.push([r+t*s,r+(t-1)*s,r-1+t*s]):(a.push([r+t*s,r+(t-1)*s,r-1+t*s]),a.push([r+t*s,r+1+(t-1)*s,r+(t-1)*s])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const bK=new p.a(0,1,0);const wK=new class extends ua{constructor(){super(...arguments),this.size=ha.VECTOR2([1,1]),this.hexagonRadius=ha.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=ha.VECTOR3([0,1,0]),this.pointsOnly=ha.BOOLEAN(0)}};class TK extends lV{constructor(){super(...arguments),this.paramsConfig=wK,this._core_transform=new vU}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new xK(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,bK,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Ns.POINTS):this.setGeometry(t)}else this.setObjects([])}}var AK;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(AK||(AK={}));const MK=[AK.ADD_PARENT,AK.REMOVE_PARENT,AK.ADD_CHILD];class EK extends sV{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=MK[e.mode];switch(i){case AK.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case AK.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case AK.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}os.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new Fn.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=ha.INTEGER(CK.levels,{range:[0,5],visibleIf:[{mode:MK.indexOf(AK.ADD_PARENT)},{mode:MK.indexOf(AK.REMOVE_PARENT)}]}),this.objectMask=ha.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:MK.indexOf(AK.ADD_CHILD)}}),this.debugObjectMask=ha.BOOLEAN(0,{visibleIf:{mode:MK.indexOf(AK.ADD_CHILD)}})}};class LK extends lV{constructor(){super(...arguments),this.paramsConfig=NK}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(EK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=MK[this.pv.mode];return SK.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new EK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const OK=new class extends ua{constructor(){super(...arguments),this.texture=ha.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP}}),this.mult=ha.FLOAT(1)}};class PK extends lV{constructor(){super(...arguments),this.paramsConfig=OK}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==ts.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:s,resx:r,resy:o}=i,a=s.length/(r*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,h=l.getAttribute(\\\\\\\"uv\\\\\\\"),u=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=h.array,p=u.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((r-1)*f),y=Math.floor((o-1)*(1-g)),x=y*r+v,b=s[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Bf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function RK(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class IK extends S.a{constructor(t,e,n,i,s){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const r=[],o=[],a=new Map;function l(t,e,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=t.clone().lerp(n,i/s),a=e.clone().lerp(n,i/s),l=s-i;for(let t=0;t<=l;t++)r[i][t]=0===t&&i===s?o:o.clone().lerp(a,t/l)}for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const QK=new class extends ua{constructor(){super(...arguments),this.length=ha.FLOAT(1,{range:[0,10]}),this.pointsCount=ha.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=ha.VECTOR3([0,0,0]),this.direction=ha.VECTOR3([0,1,0])}};class KK extends lV{constructor(){super(...arguments),this.paramsConfig=QK}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s)}const s=new S.a;s.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),s.setIndex(n),this.setGeometry(s,Ns.LINE_SEGMENTS)}}const t0=new class extends ua{constructor(){super(...arguments),this.distance0=ha.FLOAT(1),this.distance1=ha.FLOAT(2),this.autoUpdate=ha.BOOLEAN(1),this.update=ha.BUTTON(null,{callback:t=>{e0.PARAM_CALLBACK_update(t)}}),this.camera=ha.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class e0 extends lV{constructor(){super(...arguments),this.paramsConfig=t0,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}_create_LOD(){const t=new Cs;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${r.states.error.message()}')`)}else null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!br.isInGroup(n.group,t))return;const i=n.cloneMat?yr.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const s=t;this._old_mat_by_old_new_id.set(i.uuid,s.material),s.material=i,yr.apply_render_hook(t,i),yr.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}n0.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new yi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},n0.INPUT_CLONED_STATE=Ki.FROM_NODE;const i0=n0.DEFAULT_PARAMS;const s0=new class extends ua{constructor(){super(...arguments),this.group=ha.STRING(i0.group),this.assignMat=ha.BOOLEAN(i0.assignMat),this.material=ha.NODE_PATH(i0.material.path(),{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=ha.BOOLEAN(i0.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=ha.BOOLEAN(i0.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=ha.BOOLEAN(i0.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=ha.BOOLEAN(i0.swapCurrentTex),this.texSrc0=ha.STRING(i0.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=ha.STRING(i0.texDest0,{visibleIf:{swapCurrentTex:1}})}};class r0 extends lV{constructor(){super(...arguments),this.paramsConfig=s0}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(n0.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new n0(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class o0 extends sV{static type(){return\\\\\\\"merge\\\\\\\"}cook(t,e){let n=[];for(let i of t)if(i){const t=i.objects();if(e.compact)for(let e of t)e.traverse((t=>{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._makeCompact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_makeCompact(t){const e=new Map,n=new Map,i=[];for(let s of t)s.traverse((t=>{if(t instanceof Fn.a)return;const s=t;if(s.geometry){const t=Os(s.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,s.material),h.pushOnArrayAtEntry(n,t,s)}}}));const s=[];return i.forEach((t=>{var i,r;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const r=fr.mergeGeometries(n);if(r){const n=e.get(t),i=this.createObject(r,t,n);s.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(r=this.states)||void 0===r||r.error.set(t.message)}}})),s}}o0.DEFAULT_PARAMS={compact:!1},o0.INPUT_CLONED_STATE=Ki.FROM_NODE;const a0=\\\\\\\"geometry to merge\\\\\\\",l0=o0.DEFAULT_PARAMS;const c0=new class extends ua{constructor(){super(...arguments),this.compact=ha.BOOLEAN(l0.compact),this.inputsCount=ha.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{h0.PARAM_CALLBACK_setInputsCount(t)}})}};class h0 extends lV{constructor(){super(...arguments),this.paramsConfig=c0}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[a0,a0,a0,a0]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(o0.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new o0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const u0=new Float32Array(36),d0=new Float32Array(36),p0=new Float32Array(36);function _0(t,e,n){return t+(e-t)*n}class m0 extends S.a{constructor(t,e,n){super(),this.resolution=t,this.isMarchingCubes=!0,this.isolation=80,this.size=-1,this.size2=-1,this.size3=-1,this.halfsize=-1,this.delta=-1,this.yd=-1,this.zd=-1,this.count=-1,this.maxCount=-1,this.enableUvs=void 0!==e&&e,this.enableColors=void 0!==n&&n,this.size=this.resolution,this.size2=this.size*this.size,this.size3=this.size2*this.size,this.halfsize=this.size/2,this.delta=2/this.size,this.yd=this.size,this.zd=this.size2,this.field=new Float32Array(this.size3),this.normal_cache=new Float32Array(3*this.size3),this.palette=new Float32Array(3*this.size3),this.maxCount=this.size3,this.count=0,this.positionArray=new Float32Array(3*this.maxCount),this.setAttribute(\\\\\\\"position\\\\\\\",new C.a(this.positionArray,3)),this.normalArray=new Float32Array(3*this.maxCount),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(this.normalArray,3)),this.enableUvs&&(this.uvArray=new Float32Array(2*this.maxCount),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(this.uvArray,2))),this.enableColors&&(this.colorArray=new Float32Array(3*this.maxCount),this.setAttribute(\\\\\\\"color\\\\\\\",new C.a(this.colorArray,3)));const i=(t,e,n,i,c)=>{const h=i+1,u=i+this.yd,d=i+this.zd,p=h+this.yd,_=h+this.zd,m=i+this.yd+this.zd,f=h+this.yd+this.zd;let g=0;const v=this.field[i],y=this.field[h],x=this.field[u],b=this.field[p],w=this.field[d],T=this.field[_],A=this.field[m],M=this.field[f];v{const t=this.size-2;for(let e=1;e{const h=(n-o)/(a-o),u=this.normal_cache;u0[e+0]=i+h*this.delta,u0[e+1]=s,u0[e+2]=r,d0[e+0]=_0(u[t+0],u[t+3],h),d0[e+1]=_0(u[t+1],u[t+4],h),d0[e+2]=_0(u[t+2],u[t+5],h),p0[e+0]=_0(this.palette[3*l+0],this.palette[3*c+0],h),p0[e+1]=_0(this.palette[3*l+1],this.palette[3*c+1],h),p0[e+2]=_0(this.palette[3*l+2],this.palette[3*c+2],h)},r=(t,e,n,i,s,r,o,a,l,c)=>{const h=(n-o)/(a-o),u=this.normal_cache;u0[e+0]=i,u0[e+1]=s+h*this.delta,u0[e+2]=r;const d=t+3*this.yd;d0[e+0]=_0(u[t+0],u[d+0],h),d0[e+1]=_0(u[t+1],u[d+1],h),d0[e+2]=_0(u[t+2],u[d+2],h),p0[e+0]=_0(this.palette[3*l+0],this.palette[3*c+0],h),p0[e+1]=_0(this.palette[3*l+1],this.palette[3*c+1],h),p0[e+2]=_0(this.palette[3*l+2],this.palette[3*c+2],h)},o=(t,e,n,i,s,r,o,a,l,c)=>{const h=(n-o)/(a-o),u=this.normal_cache;u0[e+0]=i,u0[e+1]=s,u0[e+2]=r+h*this.delta;const d=t+3*this.zd;d0[e+0]=_0(u[t+0],u[d+0],h),d0[e+1]=_0(u[t+1],u[d+1],h),d0[e+2]=_0(u[t+2],u[d+2],h),p0[e+0]=_0(this.palette[3*l+0],this.palette[3*c+0],h),p0[e+1]=_0(this.palette[3*l+1],this.palette[3*c+1],h),p0[e+2]=_0(this.palette[3*l+2],this.palette[3*c+2],h)},a=t=>{const e=3*t;0===this.normal_cache[e]&&(this.normal_cache[e+0]=this.field[t-1]-this.field[t+1],this.normal_cache[e+1]=this.field[t-this.yd]-this.field[t+this.yd],this.normal_cache[e+2]=this.field[t-this.zd]-this.field[t+this.zd])},l=(t,e,n,i,s,r)=>{const o=3*this.count;if(this.positionArray&&this.normalArray){if(this.positionArray[o+0]=t[i],this.positionArray[o+1]=t[i+1],this.positionArray[o+2]=t[i+2],this.positionArray[o+3]=t[s],this.positionArray[o+4]=t[s+1],this.positionArray[o+5]=t[s+2],this.positionArray[o+6]=t[r],this.positionArray[o+7]=t[r+1],this.positionArray[o+8]=t[r+2],this.normalArray[o+0]=e[i+0],this.normalArray[o+1]=e[i+1],this.normalArray[o+2]=e[i+2],this.normalArray[o+3]=e[s+0],this.normalArray[o+4]=e[s+1],this.normalArray[o+5]=e[s+2],this.normalArray[o+6]=e[r+0],this.normalArray[o+7]=e[r+1],this.normalArray[o+8]=e[r+2],this.enableUvs&&this.uvArray){const e=2*this.count;this.uvArray[e+0]=t[i+0],this.uvArray[e+1]=t[i+2],this.uvArray[e+2]=t[s+0],this.uvArray[e+3]=t[s+2],this.uvArray[e+4]=t[r+0],this.uvArray[e+5]=t[r+2]}this.enableColors&&this.colorArray&&(this.colorArray[o+0]=n[i+0],this.colorArray[o+1]=n[i+1],this.colorArray[o+2]=n[i+2],this.colorArray[o+3]=n[s+0],this.colorArray[o+4]=n[s+1],this.colorArray[o+5]=n[s+2],this.colorArray[o+6]=n[r+0],this.colorArray[o+7]=n[r+1],this.colorArray[o+8]=n[r+2]),this.count+=3}}}createPolygons(){this._createPolygons()}addBall(t,e,n,i,s,r){const o=Math.sign(i);i=Math.abs(i);const a=!(null==r);let l=new D.a(t,e,n);if(a)try{r&&(l=r)}catch(i){l=new D.a(t,e,n)}const c=this.size*Math.sqrt(i/s),h=n*this.size,u=e*this.size,d=t*this.size;let p=Math.floor(h-c);p<1&&(p=1);let _=Math.floor(h+c);_>this.size-1&&(_=this.size-1);let m=Math.floor(u-c);m<1&&(m=1);let f=Math.floor(u+c);f>this.size-1&&(f=this.size-1);let g=Math.floor(d-c);g<1&&(g=1);let v,y,x,b,w,T,A,M,E,S,C,N=Math.floor(d+c);for(N>this.size-1&&(N=this.size-1),x=p;x<_;x++)for(w=this.size2*x,M=x/this.size-n,E=M*M,y=m;y0){this.field[b+v]+=C*o;const t=Math.sqrt((v-d)*(v-d)+(y-u)*(y-u)+(x-h)*(x-h))/c,e=1-t*t*t*(t*(6*t-15)+10);this.palette[3*(b+v)+0]+=l.r*e,this.palette[3*(b+v)+1]+=l.g*e,this.palette[3*(b+v)+2]+=l.b*e}}}const f0=new 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-1,-1,-1,-1,1,10,2,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,3,8,9,1,8,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,9,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,3,8,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]),v0=new p.a;class y0 extends sV{static type(){return\\\\\\\"metaball\\\\\\\"}cook(t,e){const n=t[0],i=new m0(e.resolution,e.enableUVs,e.enableColors);i.isolation=e.isolation;const s=n.points();for(let t of s){t.getPosition(v0);let n=e.metaStrength;if(e.useMetaStrengthAttrib){let e=t.attribValue(\\\\\\\"metaStrength\\\\\\\");m.isNumber(e)&&(n*=e)}let s=e.metaSubstract;if(e.useMetaSubstractAttrib){let e=t.attribValue(\\\\\\\"metaSubstract\\\\\\\");m.isNumber(e)&&(s*=e)}i.addBall(v0.x,v0.y,v0.z,n,s,void 0)}return i.createPolygons(),this.createCoreGroupFromGeometry(i)}}y0.DEFAULT_PARAMS={resolution:10,isolation:1,useMetaStrengthAttrib:!1,metaStrength:1,useMetaSubstractAttrib:!1,metaSubstract:1,enableUVs:!1,enableColors:!1},y0.INPUT_CLONED_STATE=Ki.NEVER;const x0=y0.DEFAULT_PARAMS;const b0=new class extends ua{constructor(){super(...arguments),this.resolution=ha.FLOAT(x0.resolution,{range:[0,100],rangeLocked:[!0,!1]}),this.isolation=ha.FLOAT(x0.isolation,{range:[0,10],rangeLocked:[!0,!1]}),this.useMetaStrengthAttrib=ha.BOOLEAN(x0.useMetaStrengthAttrib),this.metaStrength=ha.FLOAT(x0.metaStrength,{range:[0,10],rangeLocked:[!0,!1]}),this.useMetaSubstractAttrib=ha.BOOLEAN(x0.useMetaSubstractAttrib),this.metaSubstract=ha.FLOAT(x0.metaSubstract,{range:[0,10],rangeLocked:[!0,!1]}),this.enableUVs=ha.BOOLEAN(x0.enableUVs),this.enableColors=ha.BOOLEAN(x0.enableColors)}};class w0 extends lV{constructor(){super(...arguments),this.paramsConfig=b0}static type(){return\\\\\\\"metaball\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to create metaballs from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(y0.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new y0(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class T0{constructor(t=Math){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.grad4=[[0,1,1,1],[0,1,1,-1],[0,1,-1,1],[0,1,-1,-1],[0,-1,1,1],[0,-1,1,-1],[0,-1,-1,1],[0,-1,-1,-1],[1,0,1,1],[1,0,1,-1],[1,0,-1,1],[1,0,-1,-1],[-1,0,1,1],[-1,0,1,-1],[-1,0,-1,1],[-1,0,-1,-1],[1,1,0,1],[1,1,0,-1],[1,-1,0,1],[1,-1,0,-1],[-1,1,0,1],[-1,1,0,-1],[-1,-1,0,1],[-1,-1,0,-1],[1,1,1,0],[1,1,-1,0],[1,-1,1,0],[1,-1,-1,0],[-1,1,1,0],[-1,1,-1,0],[-1,-1,1,0],[-1,-1,-1,0]],this.p=[];for(let e=0;e<256;e++)this.p[e]=Math.floor(256*t.random());this.perm=[];for(let t=0;t<512;t++)this.perm[t]=this.p[255&t];this.simplex=[[0,1,2,3],[0,1,3,2],[0,0,0,0],[0,2,3,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,3,0],[0,2,1,3],[0,0,0,0],[0,3,1,2],[0,3,2,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,3,2,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,0,3],[0,0,0,0],[1,3,0,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,3,0,1],[2,3,1,0],[1,0,2,3],[1,0,3,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,3,1],[0,0,0,0],[2,1,3,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,1,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,0,1,2],[3,0,2,1],[0,0,0,0],[3,1,2,0],[2,1,0,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,1,0,2],[0,0,0,0],[3,2,0,1],[3,2,1,0]]}dot(t,e,n){return t[0]*e+t[1]*n}dot3(t,e,n,i){return t[0]*e+t[1]*n+t[2]*i}dot4(t,e,n,i,s){return t[0]*e+t[1]*n+t[2]*i+t[3]*s}noise(t,e){let n,i,s;const r=(t+e)*(.5*(Math.sqrt(3)-1)),o=Math.floor(t+r),a=Math.floor(e+r),l=(3-Math.sqrt(3))/6,c=(o+a)*l,h=t-(o-c),u=e-(a-c);let d,p;h>u?(d=1,p=0):(d=0,p=1);const _=h-d+l,m=u-p+l,f=h-1+2*l,g=u-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-h*h-u*u;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],h,u));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let M=.5-f*f-g*g;return M<0?s=0:(M*=M,s=M*M*this.dot(this.grad3[w],f,g)),70*(n+i+s)}noise3d(t,e,n){let i,s,r,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),h=Math.floor(n+a),u=1/6,d=(l+c+h)*u,p=t-(l-d),_=e-(c-d),m=n-(h-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),M=r[A][0]>=3?1:0,E=r[A][1]>=3?1:0,S=r[A][2]>=3?1:0,C=r[A][3]>=3?1:0,N=r[A][0]>=2?1:0,L=r[A][1]>=2?1:0,O=r[A][2]>=2?1:0,P=r[A][3]>=2?1:0,R=r[A][0]>=1?1:0,I=r[A][1]>=1?1:0,F=r[A][2]>=1?1:0,D=r[A][3]>=1?1:0,B=x-M+l,z=b-E+l,k=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-P+2*l,W=x-R+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,st=o[K+M+o[tt+E+o[et+S+o[nt+C]]]]%32,rt=o[K+N+o[tt+L+o[et+O+o[nt+P]]]]%32,ot=o[K+R+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(s[it],x,b,w,T));let ct=.6-B*B-z*z-k*k-U*U;ct<0?h=0:(ct*=ct,h=ct*ct*this.dot4(s[st],B,z,k,U));let ht=.6-G*G-V*V-H*H-j*j;ht<0?u=0:(ht*=ht,u=ht*ht*this.dot4(s[rt],G,V,H,j));let ut=.6-W*W-q*q-X*X-Y*Y;ut<0?d=0:(ut*=ut,d=ut*ut*this.dot4(s[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(s[at],$,J,Z,Q)),27*(c+h+u+d+p)}}var A0;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(A0||(A0={}));const M0=[A0.ADD,A0.SET,A0.MULT,A0.SUBSTRACT,A0.DIVIDE];const E0=new class extends ua{constructor(){super(...arguments),this.amplitude=ha.FLOAT(1),this.tamplitudeAttrib=ha.BOOLEAN(0),this.amplitudeAttrib=ha.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=ha.VECTOR3([1,1,1]),this.offset=ha.VECTOR3([0,0,0]),this.octaves=ha.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=ha.FLOAT(.5,{range:[0,1]}),this.freqIncrease=ha.FLOAT(2,{range:[0,10]}),this.seed=ha.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=ha.BOOLEAN(0),this.attribName=ha.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=ha.BOOLEAN(0),this.restP=ha.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=ha.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=ha.INTEGER(M0.indexOf(A0.ADD),{menu:{entries:M0.map((t=>({name:t,value:M0.indexOf(t)})))}}),this.computeNormals=ha.BOOLEAN(1)}},S0=new p.a,C0=new p.a;class N0 extends lV{constructor(){super(...arguments),this.paramsConfig=E0,this._simplexBySeed=new Map,this._restPos=new p.a,this._restValue2=new d.a,this._restValue4=new _.a,this._noiseValueV=new p.a,this._currentAttribValueF=0,this._currentAttribValueV2=new d.a,this._currentAttribValueV3=new p.a,this._currentAttribValueV4=new _.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}setOperation(t){this.p.operation.set(M0.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=zs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=e.attribSize(this.pv.attribName),r=n[0];if(!r)return void this.setCoreGroup(e);const o=r.attribValue(i);if(m.isString(o))return void this.states.error.set(\\\\\\\"cannot add noise to a string attribute\\\\\\\");const a={octaves:this.pv.octaves,ampAttenuation:this.pv.ampAttenuation,freqIncrease:this.pv.freqIncrease};switch(s){case 1:this._cookForFloat(n,a);break;case 2:this._cookForV2(n,a);break;case 3:this._cookForV3(n,a);break;case 4:this._cookForV4(n,a)}if(!this.io.inputs.cloneRequired(0))for(let t of e.geometries())t.getAttribute(i).needsUpdate=!0;this.pv.computeNormals&&e.computeVertexNormals(),this.setCoreGroup(e)}_cookForFloat(t,e){const n=this._getSimplex(),i=this.pv.useRestAttributes,s=this.pv.useNormals,r=this.pv.tamplitudeAttrib,o=this.pv.amplitude,a=M0[this.pv.operation],l=this.pv.attribName;for(let c of t){i?(c.attribValue(this.pv.restP,S0),s&&c.attribValue(this.pv.restN,C0),this._currentAttribValueF=S0.x):(c.getPosition(S0),s&&c.attribValue(Ws.NORMAL,C0),this._currentAttribValueF=c.attribValue(l));const t=r?this._amplitudeFromAttrib(c,o):o,h=this._noiseValue(s,n,t,e,S0,C0).x,u=N0._newAttribValueFromFloat(a,this._currentAttribValueF,h);c.setAttribValue(l,u)}}_cookForV2(t,e){const n=this._getSimplex(),i=this.pv.useRestAttributes,s=this.pv.useNormals,r=this.pv.tamplitudeAttrib,o=this.pv.amplitude,a=M0[this.pv.operation],l=this.pv.attribName;for(let c of t){i?(c.attribValue(this.pv.restP,S0),s&&c.attribValue(this.pv.restN,C0),this._currentAttribValueV2.set(S0.x,S0.y)):(c.getPosition(S0),s&&c.attribValue(Ws.NORMAL,C0),c.attribValue(l,this._currentAttribValueV2));const t=r?this._amplitudeFromAttrib(c,o):o,h=this._noiseValue(s,n,t,e,S0,C0);this._restValue2.set(h.x,h.y);const u=this._restValue2,d=N0._newAttribValueFromVector2(a,this._currentAttribValueV2,u);c.setAttribValue(l,d)}}_cookForV3(t,e){const n=this._getSimplex(),i=this.pv.useRestAttributes,s=this.pv.useNormals,r=this.pv.tamplitudeAttrib,o=this.pv.amplitude,a=M0[this.pv.operation],l=this.pv.attribName;for(let c of t){i?(c.attribValue(this.pv.restP,S0),s&&c.attribValue(this.pv.restN,C0),this._currentAttribValueV3.copy(S0)):(c.getPosition(S0),s&&c.attribValue(Ws.NORMAL,C0),c.attribValue(l,this._currentAttribValueV3));const t=r?this._amplitudeFromAttrib(c,o):o,h=this._noiseValue(s,n,t,e,S0,C0),u=N0._newAttribValueFromVector3(a,this._currentAttribValueV3,h);c.setAttribValue(l,u)}}_cookForV4(t,e){const n=this._getSimplex(),i=this.pv.useRestAttributes,s=this.pv.useNormals,r=this.pv.tamplitudeAttrib,o=this.pv.amplitude,a=M0[this.pv.operation],l=this.pv.attribName;for(let c of t){i?(c.attribValue(this.pv.restP,S0),s&&c.attribValue(this.pv.restN,C0),this._currentAttribValueV4.set(S0.x,S0.y,S0.z,0)):(c.getPosition(S0),s&&c.attribValue(Ws.NORMAL,C0),c.attribValue(l,this._currentAttribValueV4));const t=r?this._amplitudeFromAttrib(c,o):o,h=this._noiseValue(s,n,t,e,S0,C0);this._restValue4.set(h.x,h.y,h.z,0);const u=this._restValue4,d=N0._newAttribValueFromVector4(a,this._currentAttribValueV4,u);c.setAttribValue(l,d)}}_noiseValue(t,e,n,i,s,r){if(this._restPos.copy(s).add(this.pv.offset).multiply(this.pv.freq),t&&r){const t=n*this._fbm(e,i,this._restPos.x,this._restPos.y,this._restPos.z);return this._noiseValueV.copy(r),this._noiseValueV.multiplyScalar(t)}return this._noiseValueV.set(n*this._fbm(e,i,this._restPos.x+545,this._restPos.y+125454,this._restPos.z+2142),n*this._fbm(e,i,this._restPos.x-425,this._restPos.y-25746,this._restPos.z+95242),n*this._fbm(e,i,this._restPos.x+765132,this._restPos.y+21,this._restPos.z-9245)),this._noiseValueV}static _newAttribValueFromFloat(t,e,n){switch(t){case A0.ADD:return e+n;case A0.SET:return n;case A0.MULT:return e*n;case A0.DIVIDE:return e/n;case A0.SUBSTRACT:return e-n}os.unreachable(t)}static _newAttribValueFromVector2(t,e,n){switch(t){case A0.ADD:return e.add(n);case A0.SET:return n;case A0.MULT:return e.multiply(n);case A0.DIVIDE:return e.divide(n);case A0.SUBSTRACT:return e.sub(n)}os.unreachable(t)}static _newAttribValueFromVector3(t,e,n){switch(t){case A0.ADD:return e.add(n);case A0.SET:return n;case A0.MULT:return e.multiply(n);case A0.DIVIDE:return e.divide(n);case A0.SUBSTRACT:return e.sub(n)}os.unreachable(t)}static _newAttribValueFromVector4(t,e,n){switch(t){case A0.ADD:return e.add(n);case A0.SET:return n;case A0.MULT:return e.multiplyScalar(n.x);case A0.DIVIDE:return e.divideScalar(n.x);case A0.SUBSTRACT:return e.sub(n)}os.unreachable(t)}_amplitudeFromAttrib(t,e){const n=t.attribValue(this.pv.amplitudeAttrib);return m.isNumber(n)?n*e:n instanceof d.a||n instanceof p.a||n instanceof _.a?n.x*e:1}_fbm(t,e,n,i,s){let r=0,o=1;for(let a=0;a{s[3*t.index()+0]=e}));else{let t;for(let e=0;e{s[3*t.index()+1]=e}));else{let t;for(let e=0;e{s[3*t.index()+2]=e}));else{let t;for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.geometry],(()=>this.p.geometry.rawInput()))}))}))}async cook(t){const e=this.p.geometry.found_node();if(e)if(e.context()==ts.SOP){const t=await e.compute();this.import_input(e,t)}else this.states.error.set(\\\\\\\"found node is not a geometry\\\\\\\");else this.states.error.set(`node not found at path '${this.pv.geometry}'`)}import_input(t,e){let n;null!=(n=e.coreContentCloned())?this.setCoreGroup(n):this.states.error.set(\\\\\\\"invalid target\\\\\\\")}}class B0 extends sV{static type(){return\\\\\\\"objectProperties\\\\\\\"}cook(t,e){const n=t[0];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}B0.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},B0.INPUT_CLONED_STATE=Ki.FROM_NODE;const z0=B0.DEFAULT_PARAMS;const k0=new class extends ua{constructor(){super(...arguments),this.applyToChildren=ha.BOOLEAN(z0.applyToChildren,{separatorAfter:!0}),this.tname=ha.BOOLEAN(z0.tname),this.name=ha.STRING(z0.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=ha.BOOLEAN(z0.trenderOrder),this.renderOrder=ha.INTEGER(z0.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=ha.BOOLEAN(z0.tfrustumCulled),this.frustumCulled=ha.BOOLEAN(z0.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=ha.BOOLEAN(z0.tmatrixAutoUpdate),this.matrixAutoUpdate=ha.BOOLEAN(z0.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=ha.BOOLEAN(z0.tvisible),this.visible=ha.BOOLEAN(z0.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=ha.BOOLEAN(z0.tcastShadow),this.castShadow=ha.BOOLEAN(z0.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=ha.BOOLEAN(z0.treceiveShadow),this.receiveShadow=ha.BOOLEAN(z0.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class U0 extends lV{constructor(){super(...arguments),this.paramsConfig=k0}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(B0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new B0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const G0=new class extends ua{};class V0 extends lV{constructor(){super(...arguments),this.paramsConfig=G0,this._input_configs_by_operation_container=new WeakMap}static type(){return Bl}initializeNode(){this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class H0 extends _f{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),s=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const r=i.type(),o=`${r} ${s} = ${this.read_attribute(t,r,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Pf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const s=this._texture_allocations_controller.variable(n);if(!s)return Pf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=s.component(),n=s.allocation();if(n){const s=n.textureName(),r=new Nf(t,Uo.SAMPLER_2D,s);i.addDefinitions(t,[r]);return`texture2D( ${s}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new Sf(t,Uo.VEC2,H0.UV_ATTRIB),i=new Lf(t,Uo.VEC2,H0.UV_VARYING);e.addDefinitions(t,[n,i],Mf.VERTEX),e.addDefinitions(t,[i],Mf.FRAGMENT),e.addBodyLines(t,[`${H0.UV_VARYING} = ${H0.UV_ATTRIB}`],Mf.VERTEX)}}H0.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",H0.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",H0.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class j0{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new H0(H0.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,yr.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i=e&&(null==this._lastSimulatedFrame&&(this._lastSimulatedFrame=e-1),null==this._lastSimulatedTime&&(this._lastSimulatedTime=this.node.scene().time()),t>this._lastSimulatedFrame&&this._computeSimulation(t-this._lastSimulatedFrame))}_computeSimulation(t=1){if(!this._gpuCompute||null==this._lastSimulatedTime)return;this._updateSimulationMaterialUniforms();for(let e=0;et)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._usedTexturesSize.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._initParticlesUVs(),this.node.renderController.reset_render_material();const n=await li.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new q0(this._usedTexturesSize.x,this._usedTexturesSize.y,this._renderer);if(i.setDataType(this._dataType()),this._gpuCompute=i,this._gpuCompute){this._lastSimulatedFrame=void 0,this._variablesByName.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this._variablesByName.delete(e)})),this._allVariables=[],null===(t=this._shadersByName)||void 0===t||t.forEach(((t,e)=>{if(this._gpuCompute){const n=this._gpuCompute.addVariable(this._textureNameForShaderName(e),t,this._createdTexturesByName.get(e));this._variablesByName.set(e,n),this._allVariables.push(n)}})),null===(e=this._variablesByName)||void 0===e||e.forEach(((t,e)=>{this._gpuCompute&&this._gpuCompute.setVariableDependencies(t,this._allVariables)})),this._createTextureRenderTargets(),this._fillTextures(),this._createSimulationMaterialUniforms();var s=this._gpuCompute.init();null!==s&&(console.error(s),this.node.states.error.set(s))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Mi(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._onGraphNodeDirty.bind(this)))}_onGraphNodeDirty(){this.node.isOnStartFrame()?this.node.setDirty():this.computeSimilationIfRequired()}materials(){const t=[];return this._variablesByName.forEach(((e,n)=>{t.push(e.material)})),t}_createSimulationMaterialUniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persistedTextureAllocationsController)return;const n=[];this._variablesByName.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[xR.TIME]={value:this.node.scene().time()},t.uniforms[xR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const s=t.param_uniform_pairs;for(let t of s){const s=t[0],r=t[1],o=this.node.params.get(s),a=e[r];for(let t of n)t.uniforms[r]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)tg.callback(o,t.uniforms[r])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._createdTexturesByName.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}_updateSimulationMaterialUniforms(){for(let t of this._allVariables)t.material.uniforms[xR.TIME].value=this.node.scene().time(),t.material.uniforms[xR.DELTA_TIME].value=this._deltaTime}_initParticlesUVs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));s++);const r=H0.UV_ATTRIB;if(this._particlesCoreGroup)for(let e of this._particlesCoreGroup.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?O$:C.a;n.setAttribute(r,new i(t,2))}}createdTexturesByName(){return this._createdTexturesByName}_fillTextures(){const t=this._textureAllocationsController();t&&this._createdTexturesByName.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const s=i.variables();if(!s)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const r=e.image.data;for(let t of s){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let s=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);r[s]=t}else e.attribValue(n).toArray(r,s);s+=4}}}}}))}resetGpuCompute(){this._gpuCompute=void 0,this._simulationRestartRequired=!0}setRestartNotRequired(){this._simulationRestartRequired=!1}resetGpuComputeAndSetDirty(){this.resetGpuCompute(),this.node.setDirty()}resetParticleGroups(){this._particlesCoreGroup=void 0}initialized(){return null!=this._particlesCoreGroup&&null!=this._gpuCompute}_createTextureRenderTargets(){this._createdTexturesByName.forEach(((t,e)=>{t.dispose()})),this._createdTexturesByName.clear(),this._variablesByName.forEach(((t,e)=>{this._gpuCompute&&this._createdTexturesByName.set(e,this._gpuCompute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpuCompute)for(let e of t)this._createdTexturesByName.set(e.shaderName(),this._gpuCompute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persistedTextureAllocationsController}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restartSimulationIfRequired(){this._simulationRestartRequired&&this._restartSimulation()}_restartSimulation(){this._lastSimulatedTime=void 0,this._createTextureRenderTargets();this._getPoints()&&(this._fillTextures(),this._variablesByName.forEach(((t,e)=>{const n=this._createdTexturesByName.get(e);this._gpuCompute&&n&&(this._gpuCompute.renderTexture(n,t.renderTargets[0]),this._gpuCompute.renderTexture(n,t.renderTargets[1]))})))}_getPoints(){if(!this._particlesCoreGroup)return;let t=this._particlesCoreGroup.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const s=[];for(let t of i)for(let e of t.points())s.push(e);return s}return[]}}class J0{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new J0(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const s=t.graph.nodeFromId(i);if(s){const t=s.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class Z0{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ps.ATTRIBUTE?[wf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new Z0;for(let n of t){const t=J0.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const Q0=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class K0{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==CF.type())),n=t.filter((t=>t.type()!=CF.type())),i=n.map((t=>t.name())).sort(),s=new Map;for(let t of n)s.set(t.name(),t);for(let t of i){const n=s.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=K0._sortNodes(t),e=K0._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case CF.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new J0(i.name(),Wo[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case wf.type():{const i=e,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const e=new J0(i.attribute_name,Wo[r.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case OI.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(Q0.includes(t)){const s=i.io.outputs.namedOutputConnectionPointsByName(t);if(s){const i=s.type(),r=new J0(t,Wo[i]);r.addGraphNodeId(e),n.push(r)}}}break}case wf.type():{const i=t,s=i.output_connection_point();if(s){const t=new J0(i.attribute_name,Wo[s.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)h.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;et.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new K0;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=Z0.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=Z0.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class t1 extends Qf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],s=new F,r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]),s.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(s,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){li.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return K0.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const e1=new class extends ua{constructor(){super(...arguments),this.startFrame=ha.FLOAT(Nl.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=ha.BOOLEAN(1),this.maxTexturesSize=ha.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=ha.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=ha.INTEGER(0,{menu:{entries:X0.map(((t,e)=>({value:e,name:t})))}}),this.reset=ha.BUTTON(null,{callback:(t,e)=>{n1.PARAM_CALLBACK_reset(t)}}),this.material=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1})}};class n1 extends lV{constructor(){super(...arguments),this.paramsConfig=e1,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new t1(this),this.globals_handler=new H0(H0.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new $0(this),this.renderController=new j0(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=ts.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return jn.GL_PARTICLES}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.resetGpuComputeAndSetDirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.isOnStartFrame()||await this.renderController.init_render_material())}isOnStartFrame(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.setRestartNotRequired();const e=t[0];this.compileIfRequired();const n=this.isOnStartFrame();n&&this.gpuController.resetParticleGroups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restartSimulationIfRequired(),this.gpuController.computeSimilationIfRequired(),n?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.resetGpuCompute(),this.gpuController.resetParticleGroups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.setPersistedTextureAllocationController(e))}_find_export_nodes(){const t=Df.findAttributeExportNodes(this),e=Df.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class i1 extends sV{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,s;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(s of(i=new fr(n),i.points())){const t=s.normal(),n=s.position().clone().add(t.multiplyScalar(e.amount));s.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}i1.DEFAULT_PARAMS={amount:0};const s1=i1.DEFAULT_PARAMS;const r1=new class extends ua{constructor(){super(...arguments),this.amount=ha.FLOAT(s1.amount,{range:[-1,1]})}};class o1 extends lV{constructor(){super(...arguments),this.paramsConfig=r1}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){this._operation=this._operation||new i1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const a1=new p.a(0,0,1),l1=new p.a(0,0,1),c1=new p.a(0,1,0);class h1 extends sV{constructor(){super(...arguments),this._core_transform=new vU,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,a1,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),s=this._create_plane(i,e);this._core_transform.rotate_geometry(s,l1,c1);const r=this._core_transform.translation_matrix(this._center);return s.applyMatrix4(r),this.createCoreGroupFromGeometry(s)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}h1.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},h1.INPUT_CLONED_STATE=Ki.NEVER;const u1=h1.DEFAULT_PARAMS;const d1=new class extends ua{constructor(){super(...arguments),this.size=ha.VECTOR2(u1.size),this.useSegmentsCount=ha.BOOLEAN(u1.useSegmentsCount),this.stepSize=ha.FLOAT(u1.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=ha.VECTOR2(u1.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=ha.VECTOR3(u1.direction),this.center=ha.VECTOR3(u1.center)}};class p1 extends lV{constructor(){super(...arguments),this.paramsConfig=d1}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(h1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new h1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class _1 extends sV{static type(){return\\\\\\\"playerCapsule\\\\\\\"}cook(t,e){return this.createCoreGroupFromGeometry(Fy(e))}}_1.DEFAULT_PARAMS={radius:.5,height:1};const m1=_1.DEFAULT_PARAMS;const f1=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(m1.radius,{range:[0,1],rangeLocked:[!0,!1]}),this.height=ha.FLOAT(m1.height,{range:[0,1],rangeLocked:[!0,!1]})}};class g1 extends lV{constructor(){super(...arguments),this.paramsConfig=f1}static type(){return\\\\\\\"playerCapsule\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new _1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const v1=\\\\\\\"position\\\\\\\";const y1=new class extends ua{constructor(){super(...arguments),this.updateX=ha.BOOLEAN(0),this.x=ha.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=ha.BOOLEAN(0),this.y=ha.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=ha.BOOLEAN(0),this.z=ha.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=ha.BOOLEAN(1)}};class x1 extends lV{constructor(){super(...arguments),this.paramsConfig=y1,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t{h[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=ha.VECTOR3(C1.center.toArray()),this.longitude=ha.FLOAT(C1.longitude,{range:[0,360]}),this.latitude=ha.FLOAT(C1.latitude,{range:[-180,180]}),this.depth=ha.FLOAT(C1.depth,{range:[0,10]})}};class L1 extends lV{constructor(){super(...arguments),this.paramsConfig=N1}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(S1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new S1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class O1{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,s=null;for(let r=0;r{r[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{r[t].push(e)}))})),o>0&&(s.push(o-1),s.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=r[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(s),o}static line_segment_to_geometries(t){var e;const n=[],i=new fr(t),s=i.attribNames(),r=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(r,i,s,e),n.push(t)}))}return n}}class P1{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new fr(this.geometry0),e=new fr(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const s=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+u),p.push(t[1]),p.push(n[1]+u),p.push(n[0]+u)}));f.intersection(r.attribNames(),o.attribNames()).forEach((t=>{const e=r.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const R1=new p.a(0,0,0),I1=new p.a(1,1,1);const F1=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(1),this.segmentsRadial=ha.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=ha.BOOLEAN(0)}};class D1 extends lV{constructor(){super(...arguments),this.paramsConfig=F1,this._core_transform=new vU,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof Ms.a&&this._create_tube(t);const n=fr.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Ns.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new fr(n).points(),s=null===(e=n.getIndex())||void 0===e?void 0:e.array,r=O1.accumulated_curve_point_indices(s);for(let t of r){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=XJ.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,s=this._core_transform.matrix(e,R1,I1,1,gU),r=n.clone();r.applyMatrix4(s),i.push(r)}for(let t=0;t0){const e=i[t],n=i[t-1],s=this._skin(n,e);this._geometries.push(s)}}_skin(t,e){const n=new S.a;return new P1(n,t,e).process(),n}}const B1=\\\\\\\"dist\\\\\\\";class z1 extends sV{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new fQ,this._raycaster=function(){const t=new QL;return t.firstHitOnly=!0,t}(),this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,s;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(B1)||t.addNumericVertexAttrib(B1,1,-1));const r=t.points();for(let t of r)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),s=this._raycaster.intersectObjects(e.objects(),!0)[0],s){if(n.transformPoints&&t.setPosition(s.point),n.addDistAttribute){const e=this._pointPos.distanceTo(s.point);console.log(e),t.setAttribValue(B1,e)}n.transferFaceNormals&&s.face&&t.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}z1.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},z1.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const k1=z1.DEFAULT_PARAMS;const U1=new class extends ua{constructor(){super(...arguments),this.useNormals=ha.BOOLEAN(k1.useNormals),this.direction=ha.VECTOR3(k1.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=ha.BOOLEAN(k1.transformPoints),this.transferFaceNormals=ha.BOOLEAN(k1.transferFaceNormals),this.addDistAttribute=ha.BOOLEAN(k1.addDistAttribute)}};class G1 extends lV{constructor(){super(...arguments),this.paramsConfig=U1}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(z1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new z1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const V1={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},H1=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",j1=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",W1={minFilter:w.V,magFilter:w.V,format:w.ic};class q1 extends B.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new Y.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new tt.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Q(n,i,W1),Object(On.i)(n)&&Object(On.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new CG(new d.a(n,i)),this.material=new F({uniforms:I.clone(V1),fragmentShader:j1,vertexShader:H1}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,s,r){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,h=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=h,t.setRenderTarget(l);var u=o.viewport;void 0!==u&&t.state.viewport(u),this.visible=!0}}}class X1 extends sV{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],s=await li.renderersController.firstRenderer();if(!s)return this.createCoreGroupFromObjects(i);const r=n.objectsWithGeo();for(let t of r){const n=new q1(t.geometry,{clipBias:e.clipBias,renderer:s,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}X1.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},X1.INPUT_CLONED_STATE=Ki.NEVER;const Y1=X1.DEFAULT_PARAMS;const $1=new class extends ua{constructor(){super(...arguments),this.active=ha.BOOLEAN(Y1.active),this.clipBias=ha.FLOAT(Y1.clipBias),this.color=ha.COLOR(Y1.color.toArray()),this.opacity=ha.FLOAT(Y1.opacity),this.pixelRatio=ha.INTEGER(Y1.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=ha.BOOLEAN(Y1.tblur),this.blur=ha.FLOAT(Y1.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=ha.FLOAT(Y1.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=ha.BOOLEAN(Y1.tblur2,{visibleIf:{tblur:1}}),this.blur2=ha.FLOAT(Y1.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=ha.FLOAT(Y1.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class J1 extends lV{constructor(){super(...arguments),this.paramsConfig=$1}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(X1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new X1(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Z1=n(85);var Q1;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(Q1||(Q1={}));const K1=[Q1.POINTS_COUNT,Q1.SEGMENT_LENGTH];var t2;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(t2||(t2={}));const e2=[t2.CENTRIPETAL,t2.CHORDAL,t2.CATMULLROM];const n2=new class extends ua{constructor(){super(...arguments),this.method=ha.INTEGER(K1.indexOf(Q1.POINTS_COUNT),{menu:{entries:K1.map(((t,e)=>({name:t,value:e})))}}),this.curveType=ha.INTEGER(e2.indexOf(t2.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:e2.map(((t,e)=>({name:t,value:e})))}}),this.tension=ha.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=ha.INTEGER(100,{visibleIf:{method:K1.indexOf(Q1.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=ha.FLOAT(1,{visibleIf:{method:K1.indexOf(Q1.SEGMENT_LENGTH)}})}};class i2 extends lV{constructor(){super(...arguments),this.paramsConfig=n2}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;ei[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=ur(o);return this.createObject(a,Ns.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=e2[this.pv.curveType],i=this.pv.tension,s=new Z1.a(e,!1,n,i),r=this._get_points_from_curve(s);let o=[];const a=[];for(let t=0;t0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=K1[this.pv.method];switch(e){case Q1.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case Q1.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}os.unreachable(e)}}class s2 extends sV{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}s2.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const r2=s2.DEFAULT_PARAMS;const o2=new class extends ua{constructor(){super(...arguments),this.tposition=ha.BOOLEAN(r2.tposition),this.position=ha.STRING(r2.position,{visibleIf:{tposition:!0}}),this.restP=ha.STRING(r2.restP,{visibleIf:{tposition:!0}}),this.tnormal=ha.BOOLEAN(r2.tnormal),this.normal=ha.STRING(r2.normal,{visibleIf:{tnormal:!0}}),this.restN=ha.STRING(r2.restN,{visibleIf:{tnormal:!0}})}};class a2 extends lV{constructor(){super(...arguments),this.paramsConfig=o2}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new s2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class l2 extends sV{constructor(){super(...arguments),this._core_transform=new vU}static type(){return\\\\\\\"roundedBox\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.size,n=new Iy(e.x,e.y,e.z,t.divisions,t.bevel);return n.translate(t.center.x,t.center.y,t.center.z),n.computeVertexNormals(),n}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new Iy(s.x,s.y,s.z,n,e.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}l2.DEFAULT_PARAMS={size:new p.a(1,1,1),divisions:2,bevel:.1,center:new p.a(0,0,0)},l2.INPUT_CLONED_STATE=Ki.NEVER;const c2=l2.DEFAULT_PARAMS;const h2=new class extends ua{constructor(){super(...arguments),this.size=ha.VECTOR3(c2.size),this.divisions=ha.INTEGER(c2.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=ha.FLOAT(c2.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=ha.VECTOR3(c2.center)}};class u2 extends lV{constructor(){super(...arguments),this.paramsConfig=h2}static type(){return\\\\\\\"roundedBox\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(l2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new l2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class d2 extends sV{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(t,e){const n=t[0];let i=n.faces();const s=[];let r=0;const o=new Map;for(let t of i){const e=t.area();o.set(t.index(),e)}const a=f.sortBy(i,(t=>o.get(t.index())||-1));let l=0;for(let t of a)r+=o.get(t.index()),s[l]=r,l++;const c=[];let h=[];e.transferAttributes&&(h=n.attribNamesMatchingMask(e.attributesToTransfer));const u=new Map,d=new Map;for(let t of h)u.set(t,[]),d.set(t,n.attribSize(t));const p=new MX,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=or.randFloat(_+t)*r;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Ns.POINTS);return this.createCoreGroupFromObjects([v])}}d2.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},d2.INPUT_CLONED_STATE=Ki.FROM_NODE;const p2=d2.DEFAULT_PARAMS;const _2=new class extends ua{constructor(){super(...arguments),this.pointsCount=ha.INTEGER(p2.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=ha.INTEGER(p2.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=ha.BOOLEAN(p2.transferAttributes),this.attributesToTransfer=ha.STRING(p2.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=ha.BOOLEAN(p2.addIdAttribute),this.addIdnAttribute=ha.BOOLEAN(p2.addIdnAttribute)}};class m2 extends lV{constructor(){super(...arguments),this.paramsConfig=_2}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this._operation=this._operation||new d2(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var f2;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(f2||(f2={}));const g2=[f2.MATRIX,f2.AXIS];var v2;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(v2||(v2={}));const y2=[v2.BBOX_CENTER,v2.BBOX_CENTER_OFFSET,v2.CUSTOM];class x2 extends sV{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new Y.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=g2[e.mode];switch(n){case f2.MATRIX:return this._applyMatrixShear(t,e);case f2.AXIS:return this._applyAxisShear(t,e)}os.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new fr(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=y2[e.centerMode];switch(n){case v2.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case v2.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case v2.CUSTOM:return this._getAxisModeCenterCustom(e)}os.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var b2;x2.DEFAULT_PARAMS={mode:g2.indexOf(f2.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:y2.indexOf(v2.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},x2.INPUT_CLONED_STATE=Ki.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(b2||(b2={}));const w2=x2.DEFAULT_PARAMS;const T2=new class extends ua{constructor(){super(...arguments),this.mode=ha.INTEGER(w2.mode,{menu:{entries:g2.map(((t,e)=>({name:t,value:e})))}}),this.xy=ha.FLOAT(w2.xy,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.xz=ha.FLOAT(w2.xz,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.yx=ha.FLOAT(w2.yx,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.yz=ha.FLOAT(w2.yz,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.zx=ha.FLOAT(w2.zx,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.zy=ha.FLOAT(w2.zy,{visibleIf:{mode:g2.indexOf(f2.MATRIX)}}),this.centerMode=ha.INTEGER(w2.centerMode,{visibleIf:{mode:g2.indexOf(f2.AXIS)},menu:{entries:y2.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=ha.VECTOR3(w2.centerOffset.toArray(),{visibleIf:{mode:g2.indexOf(f2.AXIS),centerMode:y2.indexOf(v2.BBOX_CENTER_OFFSET)}}),this.center=ha.VECTOR3(w2.center.toArray(),{visibleIf:{mode:g2.indexOf(f2.AXIS),centerMode:y2.indexOf(v2.CUSTOM)}}),this.planeAxis=ha.VECTOR3(w2.planeAxis.toArray(),{visibleIf:{mode:g2.indexOf(f2.AXIS)}}),this.axis=ha.VECTOR3(w2.axis.toArray(),{visibleIf:{mode:g2.indexOf(f2.AXIS)}}),this.axisAmount=ha.FLOAT(w2.axisAmount,{range:[-1,1],visibleIf:{mode:g2.indexOf(f2.AXIS)}})}};class A2 extends lV{constructor(){super(...arguments),this.paramsConfig=T2}static type(){return b2.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(x2.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set(g2.indexOf(t))}cook(t){this._operation=this._operation||new x2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const M2=new class extends ua{};class E2 extends lV{constructor(){super(...arguments),this.paramsConfig=M2}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=O1.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const s=e[n-1],r=this._skin(s,t);i.push(r)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),s=this._get_line_segments(n),r=f.sortBy([i,s],(t=>-t.length)),o=r[0],a=r[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,s=this._skin(e,i);l.push(s)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new P1(n,t,e).process(),n}}var S2;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(S2||(S2={}));const C2=[S2.X,S2.Y,S2.Z];class N2 extends sV{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new br(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const s=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const r=C2[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),r){case S2.X:o=this._pointPos.x;break;case S2.Y:o=this._pointPos.y;break;case S2.Z:o=this._pointPos.z}this._positions[a]=o,h.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const u=f.uniq(l);for(let t of u){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(s.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=ha.BOOLEAN(L2.invert)}};class P2 extends lV{constructor(){super(...arguments),this.paramsConfig=O2}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new N2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const R2=new class extends ua{constructor(){super(...arguments),this.startFrame=ha.INTEGER(Nl.START_FRAME)}};class I2 extends uV{constructor(){super(...arguments),this.paramsConfig=R2,this._last_simulated_frame=null,this.childrenDisplayController=new pV(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Fm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e({name:t,value:z2[t]})))}}),this.radius=ha.FLOAT(G2.radius,{visibleIf:{type:z2.default}}),this.resolution=ha.VECTOR2(G2.resolution,{visibleIf:{type:z2.default}}),this.open=ha.BOOLEAN(G2.open,{visibleIf:{type:z2.default}}),this.phiStart=ha.FLOAT(G2.phiStart,{range:[0,2*Math.PI],visibleIf:{type:z2.default,open:!0}}),this.phiLength=ha.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:z2.default,open:!0}}),this.thetaStart=ha.FLOAT(G2.thetaStart,{range:[0,Math.PI],visibleIf:{type:z2.default,open:!0}}),this.thetaLength=ha.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:z2.default,open:!0}}),this.detail=ha.INTEGER(G2.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:z2.isocahedron}}),this.center=ha.VECTOR3(G2.center)}};class H2 extends lV{constructor(){super(...arguments),this.paramsConfig=V2}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(U2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new U2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const j2=new class extends ua{constructor(){super(...arguments),this.attribType=ha.INTEGER(ks.indexOf(zs.NUMERIC),{menu:{entries:Us}}),this.attribName=ha.STRING(\\\\\\\"\\\\\\\")}};class W2 extends lV{constructor(){super(...arguments),this.paramsConfig=j2,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const s=t.object(),r=e.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=Gs.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of r)t=e.indexedAttribValue(n),h.pushOnArrayAtEntry(i,t,e);else for(let e of r)t=e.attribValue(n),h.pushOnArrayAtEntry(i,t,e)}const a=Os(s.constructor);i.forEach(((t,e)=>{const i=fr.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);br.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const q2=new A.a,X2=new K.a,Y2=new p.a;class $2 extends J.a{constructor(){super(),this.uuid=On.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new G.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e0)for(let t=0;t0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t=0;t--){const e=r[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,s),_=c(_,3,r),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),r){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(h(e.normal)),a){const t=e.vertexNormals;n.push(h(t[0]),h(t[1]),h(t[2]))}if(l&&n.push(u(e.color)),p){const t=e.vertexColors;n.push(u(t[0]),u(t[1]),u(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=r),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new $2).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],s=t.morphTargets[n];for(let t=0,e=s.length;t0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}$2.prototype.isGeometry=!0;class J2{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,s;const r=t.faces;for(i=0;i0,r=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){u=[];for(let t=0;t0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,s,r){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(s),r[t].edges.push(o),r[e].edges.push(o)}function i(t,e,n,i,s){t.push(new Z2(e,n,i,void 0,void 0,s))}function s(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function r(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}Q2.prototype.smooth=function(o){var a,l,c,h,u,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,M,E,S,C,N,L,O,P,R,I,F,D,B,z=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(z)for(var k=0;k3&&(O=3/(8*_)),P=1-_*O,R=O,_<=2&&2==_&&(P=3/4,R=1/8),B=D.clone().multiplyScalar(P),w.set(0,0,0),k=0;k<_;k++)A=(I=F[k]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(R),B.add(w),b.push(B)}h=b.concat(x);var U,G,V,H,j,W,q,X=b.length;u=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{o9.PARAM_CALLBACK_reset(t)}})}};class o9 extends lV{constructor(){super(...arguments),this.paramsConfig=r9}static type(){return ns.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.inputGraphNode(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var a9=n(82);class l9 extends $g{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new a9.a(this.loadingManager),s=await this._urlToLoad();i.load(s,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new a9.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new Fn.a;for(let t=0;t{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}h9.DEFAULT_PARAMS={url:c9,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const u9=h9.DEFAULT_PARAMS;const d9=new class extends ua{constructor(){super(...arguments),this.url=ha.STRING(u9.url,{fileBrowse:{type:[Rr.SVG]}}),this.reload=ha.BUTTON(null,{callback:(t,e)=>{p9.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=ha.BOOLEAN(u9.drawFillShapes),this.fillShapesWireframe=ha.BOOLEAN(u9.fillShapesWireframe),this.drawStrokes=ha.BOOLEAN(u9.drawStrokes),this.strokesWireframe=ha.BOOLEAN(u9.strokesWireframe)}};class p9 extends lV{constructor(){super(...arguments),this.paramsConfig=d9}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Hn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new h9(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const _9=\\\\\\\"geometry to switch to\\\\\\\";const m9=new class extends ua{constructor(){super(...arguments),this.input=ha.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class f9 extends lV{constructor(){super(...arguments),this.paramsConfig=m9}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[_9,_9,_9,_9]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class g9 extends IK{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const v9=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(1),this.detail=ha.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=ha.BOOLEAN(0),this.center=ha.VECTOR3([0,0,0])}};class y9 extends lV{constructor(){super(...arguments),this.paramsConfig=v9}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new g9(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Ns.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class x9 extends fJ{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var b9=n(48);class w9 extends Vf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Gf.a(this.manager);r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=s.parse(n);e&&e(i)}),n,i)}parse(t){return new T9(t)}}class T9{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),s=e/n.resolution,r=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*s,o=[];let a=0,l=0;for(let t=0;t{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await li.modulesRegister.module(Hn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await li.modulesRegister.module(Hn.SVGLoader);if(t)return t}}var E9;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(E9||(E9={}));const S9=[E9.MESH,E9.FLAT,E9.LINE,E9.STROKE],C9=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const N9=new class extends ua{constructor(){super(...arguments),this.font=ha.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Rr.FONT]}}),this.text=ha.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=ha.INTEGER(0,{menu:{entries:S9.map(((t,e)=>({name:t,value:e})))}}),this.size=ha.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=ha.FLOAT(.1,{visibleIf:{type:S9.indexOf(E9.MESH)}}),this.segments=ha.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:S9.indexOf(E9.MESH)}}),this.strokeWidth=ha.FLOAT(.02,{visibleIf:{type:S9.indexOf(E9.STROKE)}})}};class L9 extends lV{constructor(){super(...arguments),this.paramsConfig=N9,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(S9[this.pv.type]){case E9.MESH:return this._create_geometry_from_type_mesh(t);case E9.FLAT:return this._create_geometry_from_type_flat(t);case E9.LINE:return this._create_geometry_from_type_line(t);case E9.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new x9(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(C9)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new bJ(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let s=0;s0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||s||(s=n[e.textureName]))}));if(s)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=s;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=s)}n.needsUpdate=!0}}));return n}}O9.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},O9.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const P9=O9.DEFAULT_PARAMS;const R9=new class extends ua{constructor(){super(...arguments),this.textureName=ha.STRING(P9.textureName)}};class I9 extends lV{constructor(){super(...arguments),this.paramsConfig=R9}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(O9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new O9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class F9 extends sV{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const s=i.map((t=>this._update_object(t,e)));return await Promise.all(s),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await li.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}F9.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Qm,tmagFilter:!1,magFilter:Zm},F9.INPUT_CLONED_STATE=Ki.FROM_NODE;const D9=F9.DEFAULT_PARAMS;const B9=new class extends ua{constructor(){super(...arguments),this.applyToChildren=ha.BOOLEAN(D9.applyToChildren,{separatorAfter:!0}),this.tencoding=ha.BOOLEAN(D9.tencoding),this.encoding=ha.INTEGER(D9.encoding,{visibleIf:{tencoding:1},menu:{entries:og.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=ha.BOOLEAN(D9.tmapping),this.mapping=ha.INTEGER(D9.mapping,{visibleIf:{tmapping:1},menu:{entries:lg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=ha.BOOLEAN(D9.twrap),this.wrapS=ha.INTEGER(D9.wrapS,{visibleIf:{twrap:1},menu:{entries:ag.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=ha.INTEGER(D9.wrapT,{visibleIf:{twrap:1},menu:{entries:ag.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=ha.BOOLEAN(D9.tanisotropy),this.useRendererMaxAnisotropy=ha.BOOLEAN(D9.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=ha.INTEGER(D9.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=ha.BOOLEAN(0),this.minFilter=ha.INTEGER(D9.minFilter,{visibleIf:{tminFilter:1},menu:{entries:tf}}),this.tmagFilter=ha.BOOLEAN(0),this.magFilter=ha.INTEGER(D9.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Km}})}};class z9 extends lV{constructor(){super(...arguments),this.paramsConfig=B9}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(F9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new F9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const k9=new p.a(0,0,1);class U9 extends sV{constructor(){super(...arguments),this._core_transform=new vU}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=new TJ(n,i,s,r);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,k9,e.direction),this.createCoreGroupFromGeometry(o)}}U9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},U9.INPUT_CLONED_STATE=Ki.FROM_NODE;const G9=U9.DEFAULT_PARAMS;const V9=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(G9.radius,{range:[0,1]}),this.radiusTube=ha.FLOAT(G9.radiusTube,{range:[0,1]}),this.segmentsRadial=ha.INTEGER(G9.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=ha.INTEGER(G9.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=ha.VECTOR3(G9.direction),this.center=ha.VECTOR3(G9.center)}};class H9 extends lV{constructor(){super(...arguments),this.paramsConfig=V9}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new U9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class j9 extends sV{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=e.p,a=e.q,l=new AJ(n,i,s,r,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}j9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},j9.INPUT_CLONED_STATE=Ki.FROM_NODE;const W9=j9.DEFAULT_PARAMS;const q9=new class extends ua{constructor(){super(...arguments),this.radius=ha.FLOAT(W9.radius),this.radiusTube=ha.FLOAT(W9.radiusTube),this.segmentsRadial=ha.INTEGER(W9.segmentsRadial,{range:[1,128]}),this.segmentsTube=ha.INTEGER(W9.segmentsTube,{range:[1,32]}),this.p=ha.INTEGER(W9.p,{range:[1,10]}),this.q=ha.INTEGER(W9.q,{range:[1,10]}),this.center=ha.VECTOR3(W9.center)}};class X9 extends lV{constructor(){super(...arguments),this.paramsConfig=q9}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new j9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Y9;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(Y9||(Y9={}));const $9=[Y9.SET_PARAMS,Y9.UPDATE_MATRIX];class J9 extends sV{constructor(){super(...arguments),this._core_transform=new vU,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=_U[e.applyOn];switch(n){case uU.GEOMETRIES:return this._update_geometries(t,e);case uU.OBJECTS:return this._update_objects(t,e)}os.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=iV._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=$9[e.objectMode];switch(n){case Y9.SET_PARAMS:return this._update_objects_params(t,e);case Y9.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}os.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=fU[e.rotationOrder];this._r.copy(e.r).multiplyScalar(On.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,fU[t.rotationOrder])}}J9.DEFAULT_PARAMS={applyOn:_U.indexOf(uU.GEOMETRIES),objectMode:$9.indexOf(Y9.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:fU.indexOf(mU.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},J9.INPUT_CLONED_STATE=Ki.FROM_NODE;const Z9=J9.DEFAULT_PARAMS;const Q9=new class extends ua{constructor(){super(...arguments),this.applyOn=ha.INTEGER(Z9.applyOn,{menu:{entries:_U.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=ha.INTEGER(Z9.objectMode,{visibleIf:{applyOn:_U.indexOf(uU.OBJECTS)},menu:{entries:$9.map(((t,e)=>({name:t,value:e})))}}),this.group=ha.STRING(Z9.group,{visibleIf:{applyOn:_U.indexOf(uU.GEOMETRIES)}}),this.rotationOrder=ha.INTEGER(Z9.rotationOrder,{menu:{entries:fU.map(((t,e)=>({name:t,value:e})))}}),this.t=ha.VECTOR3(Z9.t),this.r=ha.VECTOR3(Z9.r),this.s=ha.VECTOR3(Z9.s),this.scale=ha.FLOAT(Z9.scale,{range:[0,10]}),this.pivot=ha.VECTOR3(Z9.pivot,{visibleIf:{applyOn:_U.indexOf(uU.GEOMETRIES)}})}};class K9 extends lV{constructor(){super(...arguments),this.paramsConfig=Q9}static type(){return b2.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(J9.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>_U[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(_U.indexOf(t))}setObjectMode(t){this.p.objectMode.set($9.indexOf(t))}cook(t){this._operation=this._operation||new J9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const t3=new class extends ua{constructor(){super(...arguments),this.useSecondInput=ha.BOOLEAN(1),this.reference=ha.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},visibleIf:{useSecondInput:0}})}};class e3 extends lV{constructor(){super(...arguments),this.paramsConfig=t3}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let s=0;s({name:t,value:e})))}};function s3(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const r3=new class extends ua{constructor(){super(...arguments),this.applyOn=ha.INTEGER(_U.indexOf(uU.GEOMETRIES),{menu:{entries:_U.map(((t,e)=>({name:t,value:e})))}}),this.count=ha.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(0)}),this.r0=ha.VECTOR3([0,0,0],{...s3(0)}),this.rotationOrder1=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(1)}),this.r1=ha.VECTOR3([0,0,0],{...s3(1)}),this.rotationOrder2=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(2)}),this.r2=ha.VECTOR3([0,0,0],{...s3(2)}),this.rotationOrder3=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(3)}),this.r3=ha.VECTOR3([0,0,0],{...s3(3)}),this.rotationOrder4=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(4)}),this.r4=ha.VECTOR3([0,0,0],{...s3(4)}),this.rotationOrder5=ha.INTEGER(n3,{separatorBefore:!0,...i3,...s3(5)}),this.r5=ha.VECTOR3([0,0,0],{...s3(5)})}};class o3 extends lV{constructor(){super(...arguments),this.paramsConfig=r3,this._core_transform=new vU,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>_U[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=_U[this.pv.applyOn];switch(n){case uU.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case uU.OBJECTS:return this._apply_matrix_to_objects(t,e)}os.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[Ws.POSITION,Ws.NORMAL,Ws.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&Xs.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class h3 extends lV{constructor(){super(...arguments),this.paramsConfig=c3,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}setMode(t){this.p.mode.set(l3.indexOf(t))}cook(t){const e=l3[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case a3.RESET_OBJECT:return this._reset_objects(e);case a3.CENTER_GEO:return this._center_geos(e,!1);case a3.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}os.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),vU.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let s=i;const r=t[1];r&&(s=r.objectsWithGeo());for(let t=0;te.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let s=0,r=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,r=Math.max(r,e.y+e.h),s=Math.max(s,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?bi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.inputGraphNode(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const s=t;n=bi.findNode(i,s,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new po,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(li.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return Ur.create(this.param,e,t,n)}}class z3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class k3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const U3=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],G3=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class V3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const s=t[0],r=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(s)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,r,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(U3.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?G3.indexOf(n)>=0?t[n]:-1:t}return-1}}class H3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],s=t[1];let r=null;try{r=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(r){const t=r.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(s){const t=n[s];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class j3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new po,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const s=t[0],r=this.getReferencedParam(s);if(r){r.isDirty()&&await r.compute();const t=r.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class W3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],s=t[2],r=this.node(),o=r?bi.findNode(r,n):null;let a;o&&o.type()==PZ.type()&&(a=o.stamp_value(s)),null==a&&(a=i),e(a)}else e(0)}))}}class q3 extends B3{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class X3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(ng.isMobile())}))}}class Y3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(ng.isTouchDevice())}))}}class $3 extends B3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class J3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class Z3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.objectsCount())}}else e(0)}))}}class Q3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(ss.tailDigits(t))}else e(0)}else e(0)}))}}class K3 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class t4 extends B3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class e4 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class n4 extends B3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.pointsCount())}}else e(0)}))}}class i4 extends B3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class s4 extends B3{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class r4 extends B3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class o4 extends B3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let s=t[2]||1;return n&&(e=n.substr(i,s)),e}}class a4 extends B3{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class l4{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),s=b.clone(i);if(0==this._validateNames(s))return;b.clone(this._created_spare_param_names).concat(s).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=nV.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(s.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},s=b.merge(n,i);let r=this._init_value_serialized_by_param_name.get(t.name());null==r&&(r=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:r,raw_input:o,options:s})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Cr.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class c4{constructor(t,e){this.node=t,this._globals_handler=new Pf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new l4(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var h4;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(h4||(h4={}));class u4{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class d4{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class p4{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)h.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)h.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class _4{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new p4(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const m4={[h4.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[h4.DEFINE]:\\\\\\\";\\\\\\\",[h4.BODY]:\\\\\\\";\\\\\\\"},f4={[h4.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[h4.DEFINE]:\\\\\\\"\\\\\\\",[h4.BODY]:\\\\\\\"\\\\t\\\\\\\"};class g4{static node_comment(t,e){let n=`// ${t.path()}`,i=f4[e];if(e==h4.BODY){let e=this.node_distance_to_material(t);t.type()==ns.OUTPUT&&(e+=1),i=i.repeat(e)}return e==h4.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=f4[n];if(n==h4.BODY&&(s=s.repeat(this.node_distance_to_material(t))),e=`${s}${e}`,i){const t=e[e.length-1],i=m4[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==h4.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=ns.INPUT&&t.type()!=ns.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class v4{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new NF,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)h.pushOnArrayAtEntry(n,t,i)}const s=new Map;for(let t of i)s.set(t.graphNodeId(),!0);for(let e of t)s.get(e.graphNodeId())||(i.push(e),s.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new _4(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,Af.FUNCTION,h4.FUNCTION_DECLARATION),this.addDefinitions(t,e,Af.UNIFORM,h4.DEFINE),this.addDefinitions(t,e,Af.VARYING,h4.DEFINE),this.addDefinitions(t,e,Af.ATTRIBUTE,h4.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,h4.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const s=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)s.push(e)}}if(s.length>0){const t=new Tf(s),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const r=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),h.pushOnArrayAtEntry(r,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=r.get(e);if(n){const t=n[0];if(t){const e=g4.node_comment(t.node,i);h.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=g4.line_wrap(t.node,e.line,i);h.pushOnArrayAtEntry(a,i,n)}const s=g4.post_line_separator(i);h.pushOnArrayAtEntry(a,i,s)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let s=0;s0){const r=this._lines.get(e),o=g4.node_comment(t,n);if(h.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((e=>{e=g4.line_wrap(t,e,n),h.pushOnArrayAtEntry(r,n,e)})),n!=h4.BODY||!i){const t=g4.post_line_separator(n);h.pushOnArrayAtEntry(r,n,t)}}}}class y4{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&h.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=Mf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const s=this._graph_id_by_depth.get(e);s&&s.forEach((e=>{var s;if(null===(s=this._graph_ids_by_shader_name.get(t))||void 0===s?void 0:s.get(e)){const s=this._graph.nodeFromId(e);this.add_nodes_with_children(s,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==ns.INPUT){const s=t.parent();if(s){const r=this.sorted_nodes_for_shader_name_for_parent(s,i);for(let s of r)s.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(s,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const s=this._graph_id_by_depth.get(n);s&&s.forEach((n=>{var s;if(!e||(null===(s=this._graph_ids_by_shader_name.get(e))||void 0===s?void 0:s.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const s=i[0];return t.context()==s.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(h.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),s=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(s.length>0)for(let e of s)h.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==ns.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const x4=new Map([[Mf.VERTEX,\\\\\\\"#include \\\\\\\"],[Mf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),b4=new Map([[Mf.VERTEX,\\\\\\\"#include \\\\\\\"],[Mf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),w4=new Map([[Mf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[Mf.FRAGMENT,[]]]);class T4 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case Mf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case Mf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case CF.type():case OF.type():e.push(t);break;case wf.type():case Ff.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case OI.type():e.push(t);break;case wf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new y4(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new v4(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Df.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"normal\\\\\\\",Uo.VEC3),new qo(\\\\\\\"color\\\\\\\",Uo.VEC3),new qo(\\\\\\\"alpha\\\\\\\",Uo.FLOAT),new qo(\\\\\\\"uv\\\\\\\",Uo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(T4.output_input_connection_points())}static create_globals_node_output_connections(){return[new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"normal\\\\\\\",Uo.VEC3),new qo(\\\\\\\"color\\\\\\\",Uo.VEC3),new qo(\\\\\\\"uv\\\\\\\",Uo.VEC2),new qo(\\\\\\\"mvPosition\\\\\\\",Uo.VEC4),new qo(\\\\\\\"worldPosition\\\\\\\",Uo.VEC4),new qo(\\\\\\\"worldNormal\\\\\\\",Uo.VEC3),new qo(\\\\\\\"gl_Position\\\\\\\",Uo.VEC4),new qo(\\\\\\\"gl_FragCoord\\\\\\\",Uo.VEC4),new qo(\\\\\\\"cameraPosition\\\\\\\",Uo.VEC3),new qo(\\\\\\\"resolution\\\\\\\",Uo.VEC2),new qo(\\\\\\\"time\\\\\\\",Uo.FLOAT)]}create_globals_node_output_connections(){return T4.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new u4(Mf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Ff.INPUT_NAME],[]),new u4(Mf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[Mf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new d4(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new d4(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new d4(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new d4(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new d4(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Pf.IF_RULE.uv})]}create_variable_configs(){return T4.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return x4.get(t)}insert_body_after(t){return b4.get(t)}lines_to_remove(t){return w4.get(t)}_replace_template(t,e){const n=this.builder_lines(e,h4.FUNCTION_DECLARATION),i=this.builder_lines(e,h4.DEFINE),s=this.builder_lines(e,h4.BODY);let r=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let h=!1,u=!1;for(let t of r){1==h&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),h=!1),1==u&&(s&&this._insert_lines(o,s),u=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(h=!0),l&&t.indexOf(l)>=0&&(u=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=U[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var A4,M4;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(A4||(A4={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(M4||(M4={}));const E4=[M4.GL_FRAGCOORD,M4.GL_POINTCOORD];class S4 extends T4{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const s=i.createMaterial();s.name=e,t.customMaterials[e]=s}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const s=t.customMaterials[n];s&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(s),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Df.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Df.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const s=this._shaders_by_name.get(Mf.VERTEX),r=this._shaders_by_name.get(Mf.FRAGMENT);s&&r&&(t.vertexShader=s,t.fragmentShader=this.processFilterFragmentShader(r),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const s=t.io.inputs.named_input(n),r=t.variableForInput(n),o=this.variable_config(n);let a=null;if(s)a=mf.vector3(r);else if(o.default_from_attribute()){const s=t.io.inputs.namedInputConnectionPointsByName(n);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),s=o.if_condition();s&&e.addBodyLines(t,[`#if ${s}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const r=o.postLines();r&&e.addBodyLines(t,r),s&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,s=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(s)for(let n of s)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),s=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),r=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${r} = ${s}`])}handle_globals_output_name(t){var e;switch(t.output_name){case M4.TIME:return void this.handleTime(t);case M4.RESOLUTION:return void this.handle_resolution(t);case M4.MV_POSITION:return void this.handle_mvPosition(t);case M4.GL_POSITION:return void this.handle_gl_Position(t);case M4.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case M4.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Nf(t.globals_node,Uo.FLOAT,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),h.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Nf(t.globals_node,Uo.VEC2,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==Mf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Lf(e,Uo.VEC4,t.var_name),s=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],Mf.VERTEX),n.addBodyLines(e,[s],Mf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==Mf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Lf(e,Uo.VEC4,t.var_name),s=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],Mf.VERTEX),n.addBodyLines(e,[s],Mf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==Mf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==Mf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let s of l){const l=t.glVarName(s),c=e.current_shader_name,h={globals_node:t,shaders_collection_controller:e,output_name:s,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:r,body_lines_by_shader_name:a};this.handle_globals_output_name(h)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==Mf.VERTEX&&E4.includes(t)||i.push(t);return i}}const C4=new Map([[Mf.VERTEX,\\\\\\\"#include \\\\\\\"],[Mf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const N4=new Map([[Mf.VERTEX,\\\\\\\"#include \\\\\\\"],[Mf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var L4=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const O4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),P4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const R4=new Map([]);R4.set(A4.DISTANCE,class extends S4{templateShader(){const t=H.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return C4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),R4.set(A4.DEPTH,class extends S4{templateShader(){const t=H.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return N4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),R4.set(A4.DEPTH_DOF,class extends S4{templateShader(){return{vertexShader:\\\\\\\"#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:L4,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return O4.get(t)}insert_body_after(t){return P4.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class I4 extends S4{custom_assembler_class_by_custom_name(){return R4}}class F4 extends I4{templateShader(){const t=H.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class D4 extends I4{templateShader(){const t=H.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class B4 extends I4{templateShader(){const t=H.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var z4=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class k4 extends I4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",k4.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?H.physical:H.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),k4.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=T4.output_input_connection_points();e.push(new qo(\\\\\\\"metalness\\\\\\\",Uo.FLOAT,1)),e.push(new qo(\\\\\\\"roughness\\\\\\\",Uo.FLOAT,1)),e.push(new qo(\\\\\\\"emissive\\\\\\\",Uo.VEC3,[1,1,1])),k4.USE_SSS&&e.push(new qo(\\\\\\\"SSSModel\\\\\\\",Uo.SSS_MODEL,z4)),this.isPhysical()&&(e.push(new qo(\\\\\\\"transmission\\\\\\\",Uo.FLOAT,1)),e.push(new qo(\\\\\\\"thickness\\\\\\\",Uo.FLOAT,1))),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){const t=[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"];return this.isPhysical()&&(t.push(\\\\\\\"transmission\\\\\\\"),t.push(\\\\\\\"thickness\\\\\\\")),[new u4(Mf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new u4(Mf.FRAGMENT,t,[Mf.VERTEX])]}create_variable_configs(){const t=T4.create_variable_configs();return t.push(new d4(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new d4(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new d4(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),k4.USE_SSS&&t.push(new d4(\\\\\\\"SSSModel\\\\\\\",{default:z4,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),this.isPhysical()&&(t.push(new d4(\\\\\\\"transmission\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_transmission = \\\\\\\"})),t.push(new d4(\\\\\\\"thickness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_thickness = \\\\\\\"}))),t}}k4.USE_SSS=!0;class U4 extends k4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshPhysicalBuilderMatNode\\\\\\\",U4.filterFragmentShader)}isPhysical(){return!0}static filterFragmentShader(t){return t=t.replace(\\\\\\\"#include \\\\\\\",function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");let n=0;for(let t of e)t.includes(\\\\\\\"float transmissionFactor = transmission;\\\\\\\")&&(t=\\\\\\\"float transmissionFactor = transmission * POLY_transmission;\\\\\\\",e[n]=t),t.includes(\\\\\\\"float thicknessFactor = thickness;\\\\\\\")&&(t=\\\\\\\"float thicknessFactor = thickness * POLY_thickness;\\\\\\\",e[n]=t),n++;return e.join(\\\\\\\"\\\\n\\\\\\\")}(k))}}const G4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),V4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const H4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),j4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const W4=new Map([[Mf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[Mf.FRAGMENT,[]]]),q4=new Map;q4.set(A4.DISTANCE,class extends S4{templateShader(){const t=H.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return G4.get(t)}insert_body_after(t){return V4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),q4.set(A4.DEPTH_DOF,class extends S4{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:L4,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return H4.get(t)}insert_body_after(t){return j4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class X4 extends S4{custom_assembler_class_by_custom_name(){return q4}templateShader(){const t=H.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=T4.output_input_connection_points();e.push(new qo(\\\\\\\"gl_PointSize\\\\\\\",Uo.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return T4.create_globals_node_output_connections().concat([new qo(M4.GL_POINTCOORD,Uo.VEC2)])}create_shader_configs(){return[new u4(Mf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new u4(Mf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[Mf.VERTEX])]}create_variable_configs(){return T4.create_variable_configs().concat([new d4(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return W4.get(t)}}const Y4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),$4=new Map([[Mf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const J4=new Map([]);J4.set(A4.DEPTH_DOF,class extends S4{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:L4,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return Y4.get(t)}insert_body_after(t){return $4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const Z4=new Map([[Mf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[Mf.FRAGMENT,[]]]);class Q4 extends S4{templateShader(){const t=H.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return J4}create_shader_configs(){return[new u4(Mf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new u4(Mf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[Mf.VERTEX])]}static output_input_connection_points(){return[new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"color\\\\\\\",Uo.VEC3),new qo(\\\\\\\"alpha\\\\\\\",Uo.FLOAT),new qo(\\\\\\\"uv\\\\\\\",Uo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(Q4.output_input_connection_points())}static create_globals_node_output_connections(){return[new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"color\\\\\\\",Uo.VEC3),new qo(\\\\\\\"uv\\\\\\\",Uo.VEC2),new qo(\\\\\\\"gl_FragCoord\\\\\\\",Uo.VEC4),new qo(\\\\\\\"resolution\\\\\\\",Uo.VEC2),new qo(\\\\\\\"time\\\\\\\",Uo.FLOAT)]}create_globals_node_output_connections(){return Q4.create_globals_node_output_connections()}create_variable_configs(){return[new d4(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new d4(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new d4(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new d4(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Pf.IF_RULE.uv})]}lines_to_remove(t){return Z4.get(t)}}class K4 extends T4{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include \\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._root_nodes)switch(s.type()){case CF.type():i.push(s);break;case wf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._leaf_nodes)switch(s.type()){case OI.type():i.push(s);break;case wf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}setup_shader_names_and_variables(){var t;const e=new y4(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new K0,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"velocity\\\\\\\",Uo.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"velocity\\\\\\\",Uo.VEC3),new qo(\\\\\\\"time\\\\\\\",Uo.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new K0}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,s){var r;if(n){const n=t.variableForInput(e),o=mf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=s.current_shader_name;if(e&&(null===(r=e.allocation())||void 0===r?void 0:r.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;s.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const s=n;this.add_export_body_line(t,n,i,s,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const s=t.gl_type(),r=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,s,r,e),a=t.glVarName(t.output_name),l=`${s} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(r),h=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==h){const n=this.textureAllocationsController().variable(r);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Nf(t,Uo.FLOAT,e);n.addDefinitions(t,[i]);const s=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[s]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,e,n);if(o){const i=`${r} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class t5 extends T4{templateShader(){return{fragmentShader:\\\\\\\"#include \\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(Mf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==Mf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}rg.handleDependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new qo(\\\\\\\"color\\\\\\\",Uo.VEC3),new qo(\\\\\\\"alpha\\\\\\\",Uo.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new qo(\\\\\\\"gl_FragCoord\\\\\\\",Uo.VEC2),new qo(\\\\\\\"time\\\\\\\",Uo.FLOAT)])}create_shader_configs(){return[new u4(Mf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new d4(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new d4(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let s;\\\\\\\"color\\\\\\\"==i&&(s=`diffuseColor.xyz = ${mf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${mf.any(n)}`),s&&e.addBodyLines(t,[s])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],s=[];for(let e of t.io.outputs.used_output_names()){const r=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":s.push(new Nf(t,Uo.FLOAT,e)),i.push(`float ${r} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,r)}}e.addDefinitions(t,s,n),e.addBodyLines(t,i)}}const e5=new Map([]);class n5 extends S4{custom_assembler_class_by_custom_name(){return e5}}const i5=new Map([[Mf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[Mf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),s5=new Map([[Mf.FRAGMENT,[]]]);class r5 extends n5{templateShader(){return{vertexShader:Bk,fragmentShader:zk,uniforms:I.clone(kk)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return yr.add_user_data_render_hook(e,Gk.render_hook.bind(Gk)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new qo(\\\\\\\"density\\\\\\\",Uo.FLOAT,1)])}static create_globals_node_output_connections(){return[new qo(\\\\\\\"position\\\\\\\",Uo.VEC3),new qo(\\\\\\\"pos_normalized\\\\\\\",Uo.VEC3),new qo(\\\\\\\"time\\\\\\\",Uo.FLOAT)]}create_globals_node_output_connections(){return r5.create_globals_node_output_connections()}insert_body_after(t){return i5.get(t)}lines_to_remove(t){return s5.get(t)}create_shader_configs(){return[new u4(Mf.VERTEX,[],[]),new u4(Mf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[Mf.VERTEX])]}static create_variable_configs(){return[new d4(\\\\\\\"position\\\\\\\",{}),new d4(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return r5.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map;let l,c;for(let s of t.io.outputs.used_output_names()){const u=t.glVarName(s),d=e.current_shader_name;switch(s){case\\\\\\\"time\\\\\\\":l=new Nf(t,Uo.FLOAT,s),d&&h.pushOnArrayAtEntry(o,d,l),c=`float ${u} = ${s}`;for(let t of r)h.pushOnArrayAtEntry(o,t,l),h.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==Mf.FRAGMENT&&n.push(`vec3 ${u} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==Mf.FRAGMENT&&n.push(`vec3 ${u} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class o5{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(k_,ec),t.registerNode(G_,sc),t.registerNode(H_,ec),t.registerNode(W_,ec),t.registerNode(tm,ec),t.registerNode(nm,tc),t.registerNode(sm,ec),t.registerNode(om,sc),t.registerNode(lm,ic),t.registerNode(mm,ic),t.registerNode(gm,ec),t.registerNode(ym,tc),t.registerNode(Sm,ic),t.registerNode(Lm,nc),t.registerNode(Om,nc),t.registerNode(Pm,nc),t.registerNode(Rm,nc),t.registerNode(sf,nc),t.registerNode(rf,nc)}}).run(t),class{static run(t){t.registerNode(rg,rc),t.registerNode(Og,oc),t.registerNode(Rg,oc),t.registerNode(kg,oc),t.registerNode(Hg,oc),t.registerNode(sv,oc),t.registerNode(lv,oc),t.registerNode(pv,ac),t.registerNode(mv,ac),t.registerNode(vv,ac),t.registerNode(xv,ac),t.registerNode(Tv,rc),t.registerNode(Ev,oc),t.registerNode(Nv,rc),t.registerNode(Pv,lc),t.registerNode(Iv,lc),t.registerNode(Rv,lc),t.registerNode(Fv,lc),t.registerNode(Dv,lc),t.registerNode(Bv,lc)}}.run(t),class{static run(t){t.registerNode(Gv,dc),t.registerNode(jv,uc),t.registerNode(qv,uc),t.registerNode($v,uc),t.registerNode(ry,cc),t.registerNode(vy,cc),t.registerNode(gy,cc),t.registerNode(xy,uc),t.registerNode(Tl,hc),t.registerNode(ex,cc),t.registerNode(hl,hc),t.registerNode(rx,uc),t.registerNode(ax,uc),t.registerNode(OL,cc),t.registerNode(Ja,hc),t.registerNode(FL,dc),t.registerNode(kL,uc),t.registerNode(YL,hc),t.registerNode(el,hc),t.registerNode(gO,uc),t.registerNode(rl,dc),t.registerNode(bO,dc),t.registerNode(PO,dc),t.registerNode(IO,uc),t.registerNode(BO,uc),t.registerNode(Ml,hc),t.registerNode(kO,uc),t.registerNode(ml,hc),t.registerNode(VO,pc),t.registerNode(HO,pc),t.registerNode(jO,pc),t.registerNode(WO,pc),t.registerNode(qO,pc),t.registerNode(XO,pc)}}.run(t),class{static run(t){t.registerNode(PP,xc),t.registerNode(SR,bc),t.registerNode(RP,Tc),t.registerNode(dR,xc),t.registerNode(PR,Tc),t.registerNode(vR,yc),t.registerNode(IP,Tc),t.registerNode(FP,Tc),t.registerNode(wf,gc,{except:[`${ts.COP}/builder`]}),t.registerNode(sP,mc),t.registerNode(DP,xc),t.registerNode(lR,xc),t.registerNode(DR,_c),t.registerNode(HR,yc),t.registerNode(jR,xc),t.registerNode(XR,gc),t.registerNode(BP,Tc),t.registerNode(JR,fc),t.registerNode(ZR,xc),t.registerNode(zP,mc),t.registerNode(tI,fc),t.registerNode(KP,fc),t.registerNode(pR,xc),t.registerNode(tR,fc),t.registerNode(lI,xc),t.registerNode(kP,xc),t.registerNode(UP,xc),t.registerNode(cR,fc),t.registerNode(uI,xc),t.registerNode(pI,xc),t.registerNode(mI,xc),t.registerNode(gI,xc),t.registerNode(JO,mc),t.registerNode(oP,mc),t.registerNode(lP,mc),t.registerNode(hP,mc),t.registerNode(GP,xc),t.registerNode(xI,_c),t.registerNode(NI,yc),t.registerNode(VP,xc),t.registerNode(OI,gc),t.registerNode(RI,_c),t.registerNode(FI,_c),t.registerNode(zI,yc),t.registerNode(UI,Ac),t.registerNode(nP,mc),t.registerNode(KO,mc),t.registerNode(HP,xc),t.registerNode(XI,fc),t.registerNode(YI,fc),t.registerNode(JI,_c),t.registerNode(jP,xc),t.registerNode(WP,xc),t.registerNode(eR,xc),t.registerNode(QI,xc),t.registerNode(nR,xc),t.registerNode(iR,xc),t.registerNode(iF,xc),t.registerNode(tF,xc),t.registerNode(mR,xc),t.registerNode(oF,xc),t.registerNode(aF,xc),t.registerNode(EF,Ac),t.registerNode(MF,fc),t.registerNode(qP,xc),t.registerNode(yR,yc),t.registerNode(CF,gc),t.registerNode(OF,gc),t.registerNode(sR,xc),t.registerNode(DF,wc),t.registerNode(UF,wc),t.registerNode(GF,wc),t.registerNode(VF,wc),t.registerNode(WF,gc),t.registerNode(YF,gc),t.registerNode(XP,mc),t.registerNode(rR,fc),t.registerNode(PF,fc),t.registerNode(JF,_c),t.registerNode(rD,fc),t.registerNode(aD,xc),t.registerNode(YP,xc),t.registerNode($P,Tc),t.registerNode(hR,xc),t.registerNode(cD,fc),t.registerNode(JP,xc),t.registerNode(FF,vc),t.registerNode(oR,fc),t.registerNode(MI,yc),t.registerNode(uD,yc,LD),t.registerNode(wI,yc,LD),t.registerNode(_R,xc),t.registerNode(pD,yc),t.registerNode(ZP,Tc),t.registerNode(mD,_c),t.registerNode(yD,gc),t.registerNode(wD,yc),t.registerNode(Ff,gc),t.registerNode(MD,gc),t.registerNode(gP,mc),t.registerNode(wP,mc),t.registerNode(vP,mc),t.registerNode(bP,mc),t.registerNode(TP,mc),t.registerNode(yP,mc),t.registerNode(xP,mc),t.registerNode(SD,fc),t.registerNode(ND,fc)}}.run(t),class{static run(t){t.registerNode(ID,Mc),t.registerNode(DD,Mc),t.registerNode(zD,Mc),t.registerNode(WD,Mc)}}.run(t),class{static run(t){t.registerNode(tB,Sc),t.registerNode(dB,Sc),t.registerNode(HB,Cc),t.registerNode($B,Ec),t.registerNode(ez,Cc),t.registerNode(az,Ec),t.registerNode(wz,Cc),t.registerNode(Ez,Cc),t.registerNode(Pz,Cc),t.registerNode(Dz,Ec),t.registerNode(Xz,Cc),t.registerNode(Zz,Ec),t.registerNode(ek,Cc),t.registerNode(rk,Ec),t.registerNode(gk,Cc),t.registerNode(Tk,Cc),t.registerNode(Sk,Lc),t.registerNode(Lk,Ec),t.registerNode(Rk,Ec),t.registerNode(Dk,Cc),t.registerNode(jk,Oc),t.registerNode(Xk,Oc),t.registerNode(Jk,Nc),t.registerNode(Zk,Nc),t.registerNode(Qk,Nc),t.registerNode(Kk,Nc),t.registerNode(tU,Nc),t.registerNode(eU,Nc)}}.run(t),class{static run(t){t.registerNode(cU,Dc),t.registerNode(OU,Dc),t.registerNode(VU,Dc),t.registerNode($U,Dc),t.registerNode(eG,Dc),t.registerNode(cG,Dc),t.registerNode(yG,Rc),t.registerNode(AG,zc),t.registerNode(IG,Pc),t.registerNode(kG,Fc),t.registerNode(VG,zc),t.registerNode(XG,zc),t.registerNode(xV,Pc),t.registerNode(FV,Rc),t.registerNode(kV,zc),t.registerNode(VV,Pc),t.registerNode(KH,Ic),t.registerNode(ij,Ic),t.registerNode(oj,Ic),t.registerNode(hj,Bc),t.registerNode(uj,Bc),t.registerNode(dj,Bc),t.registerNode(pj,Bc),t.registerNode(_j,Bc),t.registerNode(mj,Bc)}}.run(t),class{static run(t){t.registerNode(Tj,eh),t.registerNode(Sj,eh),t.registerNode(Lj,th),t.registerNode(Rj,th),t.registerNode(Dj,nh),t.registerNode(zj,nh),t.registerNode(Gj,nh),t.registerNode(Hj,nh),t.registerNode(Qj,eh),t.registerNode($j,eh),t.registerNode(nW,eh),t.registerNode(rW,nh),t.registerNode(lW,th),t.registerNode(hW,Kc),t.registerNode(dW,nh),t.registerNode(vW,nh),t.registerNode(xW,nh),t.registerNode(wW,nh),t.registerNode(MW,eh),t.registerNode(CW,eh),t.registerNode(LW,nh),t.registerNode(RW,eh),t.registerNode(DW,th),t.registerNode(zW,nh),t.registerNode(VW,Kc),t.registerNode(qW,eh),t.registerNode(YW,Kc),t.registerNode(ZW,eh),t.registerNode(tq,ih),t.registerNode(eq,ih),t.registerNode(nq,ih),t.registerNode(iq,ih),t.registerNode(sq,ih),t.registerNode(rq,ih)}}.run(t),class{static run(t){t.registerNode(dq,kc),t.registerNode(AH,Gc),t.registerNode(pq,Uc),t.registerNode(lq,Uc),t.registerNode(_q,Uc),t.registerNode(mq,Uc),t.registerNode(fq,Uc),t.registerNode(gq,Uc)}}.run(t),class{static run(t){t.registerOperation(vq),t.registerOperation(Pq),t.registerOperation(Uq),t.registerOperation(jq),t.registerOperation(Yq),t.registerOperation(lX),t.registerOperation(nX),t.registerOperation(pX),t.registerOperation(qX),t.registerOperation(JX),t.registerOperation(T$),t.registerOperation(S$),t.registerOperation(UJ),t.registerOperation(jJ),t.registerOperation($J),t.registerOperation(lZ),t.registerOperation(DZ),t.registerOperation(eQ),t.registerOperation(dK),t.registerOperation(EK),t.registerOperation(DK),t.registerOperation(UK),t.registerOperation(qK),t.registerOperation(o0),t.registerOperation(y0),t.registerOperation(n0),t.registerOperation(P0),t.registerOperation(B0),t.registerOperation(i1),t.registerOperation(h1),t.registerOperation(_1),t.registerOperation(b1),t.registerOperation(S1),t.registerOperation(z1),t.registerOperation(X1),t.registerOperation(s2),t.registerOperation(l2),t.registerOperation(d2),t.registerOperation(x2),t.registerOperation(N2),t.registerOperation(U2),t.registerOperation(K2),t.registerOperation(h9),t.registerOperation(O9),t.registerOperation(F9),t.registerOperation(U9),t.registerOperation(j9),t.registerOperation(J9),t.registerOperation(m3),t.registerOperation(w3),t.registerOperation(E3),t.registerNode(bq,qc),t.registerNode(Tq,Hc),t.registerNode(Oq,Hc),t.registerNode(Fq,jc),t.registerNode(Hq,jc),t.registerNode(Xq,jc),t.registerNode(Qq,jc),t.registerNode(tX,jc),t.registerNode(rX,jc),t.registerNode(uX,jc),t.registerNode(gX,jc),t.registerNode(yX,jc),t.registerNode(bX,jc),t.registerNode(SX,jc),t.registerNode(NX,$c),t.registerNode(OX,$c),t.registerNode($X,$c),t.registerNode(KX,Zc),t.registerNode(E$,Vc),t.registerNode(L$,Vc),t.registerNode(IJ,Yc),t.registerNode(kJ,Zc),t.registerNode(HJ,$c),t.registerNode(qJ,Zc),t.registerNode(QJ,Zc),t.registerNode(rZ,Zc),t.registerNode(uZ,$c),t.registerNode(gZ,$c),t.registerNode(xZ,Zc),t.registerNode(PZ,$c),t.registerNode(kZ,Zc),t.registerNode(jZ,qc),t.registerNode(QZ,qc),t.registerNode(sQ,Yc),t.registerNode(oQ,Yc),t.registerNode(bQ,$c),t.registerNode(TQ,$c),t.registerNode(rK,Vc),t.registerNode(aK,$c),t.registerNode(mK,qc),t.registerNode(gK,qc),t.registerNode(yK,$c),t.registerNode(TK,Zc),t.registerNode(PK,$c),t.registerNode(LK,Yc),t.registerNode(kK,Zc),t.registerNode(HK,Qc),t.registerNode(WK,Qc),t.registerNode($K,$c),t.registerNode(ZK,$c),t.registerNode(KK,Zc),t.registerNode(e0,Vc),t.registerNode(r0,Qc),t.registerNode(h0,Yc),t.registerNode(w0,Zc),t.registerNode(N0,Yc),t.registerNode(O0,$c),t.registerNode(I0,Yc),t.registerNode(D0,qc),t.registerNode(U0,$c),t.registerNode(V0,Vc,{userAllowed:!1}),t.registerNode(n1,Wc),t.registerNode(o1,$c),t.registerNode(p1,Zc),t.registerNode(g1,$c),t.registerNode(L1,$c),t.registerNode(x1,$c),t.registerNode(A1,Xc),t.registerNode(fV,Vc),t.registerNode(D1,$c),t.registerNode(G1,$c),t.registerNode(J1,Qc),t.registerNode(i2,$c),t.registerNode(a2,jc),t.registerNode(u2,qc),t.registerNode(m2,$c),t.registerNode(E2,$c),t.registerNode(A2,$c),t.registerNode(I2,Vc),t.registerNode(D2,Vc),t.registerNode(P2,$c),t.registerNode(H2,Zc),t.registerNode(W2,$c),t.registerNode(n9,$c),t.registerNode(s9,Yc),t.registerNode(o9,Yc),t.registerNode(hV,Yc),t.registerNode(p9,qc),t.registerNode(f9,Yc),t.registerNode(y9,Zc),t.registerNode(L9,Zc),t.registerNode(I9,$c),t.registerNode(z9,$c),t.registerNode(H9,Zc),t.registerNode(X9,Zc),t.registerNode(K9,$c),t.registerNode(e3,$c),t.registerNode(o3,$c),t.registerNode(h3,$c),t.registerNode(p3,Zc),t.registerNode(g3,$c),t.registerNode(b3,$c),t.registerNode(M3,$c),t.registerNode(C3,$c),t.registerNode(O3,Jc),t.registerNode(P3,Jc),t.registerNode(R3,Jc),t.registerNode(I3,Jc),t.registerNode(F3,Jc),t.registerNode(D3,Jc)}}.run(t)}}.run(li),class{static run(t){t.registerCamera(KH),t.registerCamera(ij)}}.run(li),class{static run(t){t.expressionsRegister.register(z3,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(k3,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(V3,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(H3,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(j3,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(W3,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(q3,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(X3,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(Y3,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register($3,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(J3,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(Z3,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(Q3,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(K3,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(t4,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(e4,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(n4,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(i4,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(s4,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(r4,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(o4,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(a4,\\\\\\\"windowSize\\\\\\\")}}.run(li),class{static run(t){t.assemblersRegister.register(jn.GL_MESH_BASIC,c4,F4),t.assemblersRegister.register(jn.GL_MESH_LAMBERT,c4,D4),t.assemblersRegister.register(jn.GL_MESH_PHONG,c4,B4),t.assemblersRegister.register(jn.GL_MESH_STANDARD,c4,k4),t.assemblersRegister.register(jn.GL_MESH_PHYSICAL,c4,U4),t.assemblersRegister.register(jn.GL_PARTICLES,c4,K4),t.assemblersRegister.register(jn.GL_POINTS,c4,X4),t.assemblersRegister.register(jn.GL_LINE,c4,Q4),t.assemblersRegister.register(jn.GL_TEXTURE,c4,t5),t.assemblersRegister.register(jn.GL_VOLUME,c4,r5)}}.run(li))}}o5._started=!1,o5.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2825829"}},"type":2,"external":true,"timestamp":1723918143943},{"data":{"url":"blob:https://ipfs.arkivo.art/08f25cef-0576-4b9d-8fbe-163e6c4e5ad3","host":"","path":"https://ipfs.arkivo.art/08f25cef-0576-4b9d-8fbe-163e6c4e5ad3","type":"http","query":"","method":"GET","headers":{"origin":"https://ipfs.arkivo.art","referer":"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36"},"fragment":"","postData":null,"protocol":"blob:"},"type":1,"external":false,"timestamp":1723918143952},{"data":{"url":"blob:https://ipfs.arkivo.art/eeae3f4a-db88-4be4-8689-3e1eb70e9367","host":"","path":"https://ipfs.arkivo.art/eeae3f4a-db88-4be4-8689-3e1eb70e9367","type":"http","query":"","method":"GET","headers":{"referer":"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36"},"fragment":"","postData":null,"protocol":"blob:"},"type":1,"external":false,"timestamp":1723918144921},{"data":{"url":"blob:https://ipfs.arkivo.art/694f8fc5-9607-4522-8949-ba201170809b","host":"","path":"https://ipfs.arkivo.art/694f8fc5-9607-4522-8949-ba201170809b","type":"http","query":"","method":"GET","headers":{"origin":"https://ipfs.arkivo.art","referer":"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36"},"fragment":"","postData":null,"protocol":"blob:"},"type":1,"external":false,"timestamp":1723918144922},{"data":{"url":"blob:https://ipfs.arkivo.art/08f25cef-0576-4b9d-8fbe-163e6c4e5ad3","body":"\"var __create = Object.create;\\nvar __defProp = Object.defineProperty;\\nvar __getProtoOf = Object.getPrototypeOf;\\nvar __hasOwnProp = Object.prototype.hasOwnProperty;\\nvar __getOwnPropNames = Object.getOwnPropertyNames;\\nvar __getOwnPropDesc = Object.getOwnPropertyDescriptor;\\nvar __markAsModule = (target) => __defProp(target, \\\\\\\"__esModule\\\\\\\", {value: true});\\nvar __commonJS = (callback, module) => () => {\\n if (!module) {\\n module = {exports: {}};\\n callback(module.exports, module);\\n }\\n return module.exports;\\n};\\nvar __exportStar = (target, module, desc) => {\\n __markAsModule(target);\\n if (module && typeof module === \\\\\\\"object\\\\\\\" || typeof module === \\\\\\\"function\\\\\\\") {\\n for (let key of __getOwnPropNames(module))\\n if (!__hasOwnProp.call(target, key) && key !== \\\\\\\"default\\\\\\\")\\n __defProp(target, key, {get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable});\\n }\\n return target;\\n};\\nvar __toModule = (module) => {\\n if (module && module.__esModule)\\n return module;\\n return __exportStar(__defProp(module != null ? __create(__getProtoOf(module)) : {}, \\\\\\\"default\\\\\\\", {value: module, enumerable: true}), module);\\n};\\n\\n// ../../../node_modules/lodash/_listCacheClear.js\\nvar require_listCacheClear = __commonJS((exports, module) => {\\n function listCacheClear() {\\n this.__data__ = [];\\n this.size = 0;\\n }\\n module.exports = listCacheClear;\\n});\\n\\n// ../../../node_modules/lodash/eq.js\\nvar require_eq = __commonJS((exports, module) => {\\n function eq(value, other) {\\n return value === other || value !== value && other !== other;\\n }\\n module.exports = eq;\\n});\\n\\n// ../../../node_modules/lodash/_assocIndexOf.js\\nvar require_assocIndexOf = __commonJS((exports, module) => {\\n var eq = require_eq();\\n function assocIndexOf(array, key) {\\n var length = array.length;\\n while (length--) {\\n if (eq(array[length][0], key)) {\\n return length;\\n }\\n }\\n return -1;\\n }\\n module.exports = assocIndexOf;\\n});\\n\\n// ../../../node_modules/lodash/_listCacheDelete.js\\nvar require_listCacheDelete = __commonJS((exports, module) => {\\n var assocIndexOf = require_assocIndexOf();\\n var arrayProto = Array.prototype;\\n var splice = arrayProto.splice;\\n function listCacheDelete(key) {\\n var data = this.__data__, index = assocIndexOf(data, key);\\n if (index < 0) {\\n return false;\\n }\\n var lastIndex = data.length - 1;\\n if (index == lastIndex) {\\n data.pop();\\n } else {\\n splice.call(data, index, 1);\\n }\\n --this.size;\\n return true;\\n }\\n module.exports = listCacheDelete;\\n});\\n\\n// ../../../node_modules/lodash/_listCacheGet.js\\nvar require_listCacheGet = __commonJS((exports, module) => {\\n var assocIndexOf = require_assocIndexOf();\\n function listCacheGet(key) {\\n var data = this.__data__, index = assocIndexOf(data, key);\\n return index < 0 ? void 0 : data[index][1];\\n }\\n module.exports = listCacheGet;\\n});\\n\\n// ../../../node_modules/lodash/_listCacheHas.js\\nvar require_listCacheHas = __commonJS((exports, module) => {\\n var assocIndexOf = require_assocIndexOf();\\n function listCacheHas(key) {\\n return assocIndexOf(this.__data__, key) > -1;\\n }\\n module.exports = listCacheHas;\\n});\\n\\n// ../../../node_modules/lodash/_listCacheSet.js\\nvar require_listCacheSet = __commonJS((exports, module) => {\\n var assocIndexOf = require_assocIndexOf();\\n function listCacheSet(key, value) {\\n var data = this.__data__, index = assocIndexOf(data, key);\\n if (index < 0) {\\n ++this.size;\\n data.push([key, value]);\\n } else {\\n data[index][1] = value;\\n }\\n return this;\\n }\\n module.exports = listCacheSet;\\n});\\n\\n// ../../../node_modules/lodash/_ListCache.js\\nvar require_ListCache = __commonJS((exports, module) => {\\n var listCacheClear = require_listCacheClear();\\n var listCacheDelete = require_listCacheDelete();\\n var listCacheGet = require_listCacheGet();\\n var listCacheHas = require_listCacheHas();\\n var listCacheSet = require_listCacheSet();\\n function ListCache(entries) {\\n var index = -1, length = entries == null ? 0 : entries.length;\\n this.clear();\\n while (++index < length) {\\n var entry = entries[index];\\n this.set(entry[0], entry[1]);\\n }\\n }\\n ListCache.prototype.clear = listCacheClear;\\n ListCache.prototype[\\\\\\\"delete\\\\\\\"] = listCacheDelete;\\n ListCache.prototype.get = listCacheGet;\\n ListCache.prototype.has = listCacheHas;\\n ListCache.prototype.set = listCacheSet;\\n module.exports = ListCache;\\n});\\n\\n// ../../../node_modules/lodash/_stackClear.js\\nvar require_stackClear = __commonJS((exports, module) => {\\n var ListCache = require_ListCache();\\n function stackClear() {\\n this.__data__ = new ListCache();\\n this.size = 0;\\n }\\n module.exports = stackClear;\\n});\\n\\n// ../../../node_modules/lodash/_stackDelete.js\\nvar require_stackDelete = __commonJS((exports, module) => {\\n function stackDelete(key) {\\n var data = this.__data__, result = data[\\\\\\\"delete\\\\\\\"](key);\\n this.size = data.size;\\n return result;\\n }\\n module.exports = stackDelete;\\n});\\n\\n// ../../../node_modules/lodash/_stackGet.js\\nvar require_stackGet = __commonJS((exports, module) => {\\n function stackGet(key) {\\n return this.__data__.get(key);\\n }\\n module.exports = stackGet;\\n});\\n\\n// ../../../node_modules/lodash/_stackHas.js\\nvar require_stackHas = __commonJS((exports, module) => {\\n function stackHas(key) {\\n return this.__data__.has(key);\\n }\\n module.exports = stackHas;\\n});\\n\\n// ../../../node_modules/lodash/_freeGlobal.js\\nvar require_freeGlobal = __commonJS((exports, module) => {\\n var freeGlobal = typeof global == \\\\\\\"object\\\\\\\" && global && global.Object === Object && global;\\n module.exports = freeGlobal;\\n});\\n\\n// ../../../node_modules/lodash/_root.js\\nvar require_root = __commonJS((exports, module) => {\\n var freeGlobal = require_freeGlobal();\\n var freeSelf = typeof self == \\\\\\\"object\\\\\\\" && self && self.Object === Object && self;\\n var root = freeGlobal || freeSelf || Function(\\\\\\\"return this\\\\\\\")();\\n module.exports = root;\\n});\\n\\n// ../../../node_modules/lodash/_Symbol.js\\nvar require_Symbol = __commonJS((exports, module) => {\\n var root = require_root();\\n var Symbol2 = root.Symbol;\\n module.exports = Symbol2;\\n});\\n\\n// ../../../node_modules/lodash/_getRawTag.js\\nvar require_getRawTag = __commonJS((exports, module) => {\\n var Symbol2 = require_Symbol();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n var nativeObjectToString = objectProto.toString;\\n var symToStringTag = Symbol2 ? Symbol2.toStringTag : void 0;\\n function getRawTag(value) {\\n var isOwn = hasOwnProperty.call(value, symToStringTag), tag = value[symToStringTag];\\n try {\\n value[symToStringTag] = void 0;\\n var unmasked = true;\\n } catch (e) {\\n }\\n var result = nativeObjectToString.call(value);\\n if (unmasked) {\\n if (isOwn) {\\n value[symToStringTag] = tag;\\n } else {\\n delete value[symToStringTag];\\n }\\n }\\n return result;\\n }\\n module.exports = getRawTag;\\n});\\n\\n// ../../../node_modules/lodash/_objectToString.js\\nvar require_objectToString = __commonJS((exports, module) => {\\n var objectProto = Object.prototype;\\n var nativeObjectToString = objectProto.toString;\\n function objectToString(value) {\\n return nativeObjectToString.call(value);\\n }\\n module.exports = objectToString;\\n});\\n\\n// ../../../node_modules/lodash/_baseGetTag.js\\nvar require_baseGetTag = __commonJS((exports, module) => {\\n var Symbol2 = require_Symbol();\\n var getRawTag = require_getRawTag();\\n var objectToString = require_objectToString();\\n var nullTag = \\\\\\\"[object Null]\\\\\\\";\\n var undefinedTag = \\\\\\\"[object Undefined]\\\\\\\";\\n var symToStringTag = Symbol2 ? Symbol2.toStringTag : void 0;\\n function baseGetTag(value) {\\n if (value == null) {\\n return value === void 0 ? undefinedTag : nullTag;\\n }\\n return symToStringTag && symToStringTag in Object(value) ? getRawTag(value) : objectToString(value);\\n }\\n module.exports = baseGetTag;\\n});\\n\\n// ../../../node_modules/lodash/isObject.js\\nvar require_isObject = __commonJS((exports, module) => {\\n function isObject(value) {\\n var type = typeof value;\\n return value != null && (type == \\\\\\\"object\\\\\\\" || type == \\\\\\\"function\\\\\\\");\\n }\\n module.exports = isObject;\\n});\\n\\n// ../../../node_modules/lodash/isFunction.js\\nvar require_isFunction = __commonJS((exports, module) => {\\n var baseGetTag = require_baseGetTag();\\n var isObject = require_isObject();\\n var asyncTag = \\\\\\\"[object AsyncFunction]\\\\\\\";\\n var funcTag = \\\\\\\"[object Function]\\\\\\\";\\n var genTag = \\\\\\\"[object GeneratorFunction]\\\\\\\";\\n var proxyTag = \\\\\\\"[object Proxy]\\\\\\\";\\n function isFunction(value) {\\n if (!isObject(value)) {\\n return false;\\n }\\n var tag = baseGetTag(value);\\n return tag == funcTag || tag == genTag || tag == asyncTag || tag == proxyTag;\\n }\\n module.exports = isFunction;\\n});\\n\\n// ../../../node_modules/lodash/_coreJsData.js\\nvar require_coreJsData = __commonJS((exports, module) => {\\n var root = require_root();\\n var coreJsData = root[\\\\\\\"__core-js_shared__\\\\\\\"];\\n module.exports = coreJsData;\\n});\\n\\n// ../../../node_modules/lodash/_isMasked.js\\nvar require_isMasked = __commonJS((exports, module) => {\\n var coreJsData = require_coreJsData();\\n var maskSrcKey = function() {\\n var uid = /[^.]+$/.exec(coreJsData && coreJsData.keys && coreJsData.keys.IE_PROTO || \\\\\\\"\\\\\\\");\\n return uid ? \\\\\\\"Symbol(src)_1.\\\\\\\" + uid : \\\\\\\"\\\\\\\";\\n }();\\n function isMasked(func) {\\n return !!maskSrcKey && maskSrcKey in func;\\n }\\n module.exports = isMasked;\\n});\\n\\n// ../../../node_modules/lodash/_toSource.js\\nvar require_toSource = __commonJS((exports, module) => {\\n var funcProto = Function.prototype;\\n var funcToString = funcProto.toString;\\n function toSource(func) {\\n if (func != null) {\\n try {\\n return funcToString.call(func);\\n } catch (e) {\\n }\\n try {\\n return func + \\\\\\\"\\\\\\\";\\n } catch (e) {\\n }\\n }\\n return \\\\\\\"\\\\\\\";\\n }\\n module.exports = toSource;\\n});\\n\\n// ../../../node_modules/lodash/_baseIsNative.js\\nvar require_baseIsNative = __commonJS((exports, module) => {\\n var isFunction = require_isFunction();\\n var isMasked = require_isMasked();\\n var isObject = require_isObject();\\n var toSource = require_toSource();\\n var reRegExpChar = /[\\\\\\\\^$.*+?()[\\\\]{}|]/g;\\n var reIsHostCtor = /^\\\\[object .+?Constructor\\\\]$/;\\n var funcProto = Function.prototype;\\n var objectProto = Object.prototype;\\n var funcToString = funcProto.toString;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n var reIsNative = RegExp(\\\\\\\"^\\\\\\\" + funcToString.call(hasOwnProperty).replace(reRegExpChar, \\\\\\\"\\\\\\\\$&\\\\\\\").replace(/hasOwnProperty|(function).*?(?=\\\\\\\\\\\\()| for .+?(?=\\\\\\\\\\\\])/g, \\\\\\\"$1.*?\\\\\\\") + \\\\\\\"$\\\\\\\");\\n function baseIsNative(value) {\\n if (!isObject(value) || isMasked(value)) {\\n return false;\\n }\\n var pattern = isFunction(value) ? reIsNative : reIsHostCtor;\\n return pattern.test(toSource(value));\\n }\\n module.exports = baseIsNative;\\n});\\n\\n// ../../../node_modules/lodash/_getValue.js\\nvar require_getValue = __commonJS((exports, module) => {\\n function getValue(object, key) {\\n return object == null ? void 0 : object[key];\\n }\\n module.exports = getValue;\\n});\\n\\n// ../../../node_modules/lodash/_getNative.js\\nvar require_getNative = __commonJS((exports, module) => {\\n var baseIsNative = require_baseIsNative();\\n var getValue = require_getValue();\\n function getNative(object, key) {\\n var value = getValue(object, key);\\n return baseIsNative(value) ? value : void 0;\\n }\\n module.exports = getNative;\\n});\\n\\n// ../../../node_modules/lodash/_Map.js\\nvar require_Map = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var root = require_root();\\n var Map2 = getNative(root, \\\\\\\"Map\\\\\\\");\\n module.exports = Map2;\\n});\\n\\n// ../../../node_modules/lodash/_nativeCreate.js\\nvar require_nativeCreate = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var nativeCreate = getNative(Object, \\\\\\\"create\\\\\\\");\\n module.exports = nativeCreate;\\n});\\n\\n// ../../../node_modules/lodash/_hashClear.js\\nvar require_hashClear = __commonJS((exports, module) => {\\n var nativeCreate = require_nativeCreate();\\n function hashClear() {\\n this.__data__ = nativeCreate ? nativeCreate(null) : {};\\n this.size = 0;\\n }\\n module.exports = hashClear;\\n});\\n\\n// ../../../node_modules/lodash/_hashDelete.js\\nvar require_hashDelete = __commonJS((exports, module) => {\\n function hashDelete(key) {\\n var result = this.has(key) && delete this.__data__[key];\\n this.size -= result ? 1 : 0;\\n return result;\\n }\\n module.exports = hashDelete;\\n});\\n\\n// ../../../node_modules/lodash/_hashGet.js\\nvar require_hashGet = __commonJS((exports, module) => {\\n var nativeCreate = require_nativeCreate();\\n var HASH_UNDEFINED = \\\\\\\"__lodash_hash_undefined__\\\\\\\";\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function hashGet(key) {\\n var data = this.__data__;\\n if (nativeCreate) {\\n var result = data[key];\\n return result === HASH_UNDEFINED ? void 0 : result;\\n }\\n return hasOwnProperty.call(data, key) ? data[key] : void 0;\\n }\\n module.exports = hashGet;\\n});\\n\\n// ../../../node_modules/lodash/_hashHas.js\\nvar require_hashHas = __commonJS((exports, module) => {\\n var nativeCreate = require_nativeCreate();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function hashHas(key) {\\n var data = this.__data__;\\n return nativeCreate ? data[key] !== void 0 : hasOwnProperty.call(data, key);\\n }\\n module.exports = hashHas;\\n});\\n\\n// ../../../node_modules/lodash/_hashSet.js\\nvar require_hashSet = __commonJS((exports, module) => {\\n var nativeCreate = require_nativeCreate();\\n var HASH_UNDEFINED = \\\\\\\"__lodash_hash_undefined__\\\\\\\";\\n function hashSet(key, value) {\\n var data = this.__data__;\\n this.size += this.has(key) ? 0 : 1;\\n data[key] = nativeCreate && value === void 0 ? HASH_UNDEFINED : value;\\n return this;\\n }\\n module.exports = hashSet;\\n});\\n\\n// ../../../node_modules/lodash/_Hash.js\\nvar require_Hash = __commonJS((exports, module) => {\\n var hashClear = require_hashClear();\\n var hashDelete = require_hashDelete();\\n var hashGet = require_hashGet();\\n var hashHas = require_hashHas();\\n var hashSet = require_hashSet();\\n function Hash(entries) {\\n var index = -1, length = entries == null ? 0 : entries.length;\\n this.clear();\\n while (++index < length) {\\n var entry = entries[index];\\n this.set(entry[0], entry[1]);\\n }\\n }\\n Hash.prototype.clear = hashClear;\\n Hash.prototype[\\\\\\\"delete\\\\\\\"] = hashDelete;\\n Hash.prototype.get = hashGet;\\n Hash.prototype.has = hashHas;\\n Hash.prototype.set = hashSet;\\n module.exports = Hash;\\n});\\n\\n// ../../../node_modules/lodash/_mapCacheClear.js\\nvar require_mapCacheClear = __commonJS((exports, module) => {\\n var Hash = require_Hash();\\n var ListCache = require_ListCache();\\n var Map2 = require_Map();\\n function mapCacheClear() {\\n this.size = 0;\\n this.__data__ = {\\n hash: new Hash(),\\n map: new (Map2 || ListCache)(),\\n string: new Hash()\\n };\\n }\\n module.exports = mapCacheClear;\\n});\\n\\n// ../../../node_modules/lodash/_isKeyable.js\\nvar require_isKeyable = __commonJS((exports, module) => {\\n function isKeyable(value) {\\n var type = typeof value;\\n return type == \\\\\\\"string\\\\\\\" || type == \\\\\\\"number\\\\\\\" || type == \\\\\\\"symbol\\\\\\\" || type == \\\\\\\"boolean\\\\\\\" ? value !== \\\\\\\"__proto__\\\\\\\" : value === null;\\n }\\n module.exports = isKeyable;\\n});\\n\\n// ../../../node_modules/lodash/_getMapData.js\\nvar require_getMapData = __commonJS((exports, module) => {\\n var isKeyable = require_isKeyable();\\n function getMapData(map, key) {\\n var data = map.__data__;\\n return isKeyable(key) ? data[typeof key == \\\\\\\"string\\\\\\\" ? \\\\\\\"string\\\\\\\" : \\\\\\\"hash\\\\\\\"] : data.map;\\n }\\n module.exports = getMapData;\\n});\\n\\n// ../../../node_modules/lodash/_mapCacheDelete.js\\nvar require_mapCacheDelete = __commonJS((exports, module) => {\\n var getMapData = require_getMapData();\\n function mapCacheDelete(key) {\\n var result = getMapData(this, key)[\\\\\\\"delete\\\\\\\"](key);\\n this.size -= result ? 1 : 0;\\n return result;\\n }\\n module.exports = mapCacheDelete;\\n});\\n\\n// ../../../node_modules/lodash/_mapCacheGet.js\\nvar require_mapCacheGet = __commonJS((exports, module) => {\\n var getMapData = require_getMapData();\\n function mapCacheGet(key) {\\n return getMapData(this, key).get(key);\\n }\\n module.exports = mapCacheGet;\\n});\\n\\n// ../../../node_modules/lodash/_mapCacheHas.js\\nvar require_mapCacheHas = __commonJS((exports, module) => {\\n var getMapData = require_getMapData();\\n function mapCacheHas(key) {\\n return getMapData(this, key).has(key);\\n }\\n module.exports = mapCacheHas;\\n});\\n\\n// ../../../node_modules/lodash/_mapCacheSet.js\\nvar require_mapCacheSet = __commonJS((exports, module) => {\\n var getMapData = require_getMapData();\\n function mapCacheSet(key, value) {\\n var data = getMapData(this, key), size = data.size;\\n data.set(key, value);\\n this.size += data.size == size ? 0 : 1;\\n return this;\\n }\\n module.exports = mapCacheSet;\\n});\\n\\n// ../../../node_modules/lodash/_MapCache.js\\nvar require_MapCache = __commonJS((exports, module) => {\\n var mapCacheClear = require_mapCacheClear();\\n var mapCacheDelete = require_mapCacheDelete();\\n var mapCacheGet = require_mapCacheGet();\\n var mapCacheHas = require_mapCacheHas();\\n var mapCacheSet = require_mapCacheSet();\\n function MapCache(entries) {\\n var index = -1, length = entries == null ? 0 : entries.length;\\n this.clear();\\n while (++index < length) {\\n var entry = entries[index];\\n this.set(entry[0], entry[1]);\\n }\\n }\\n MapCache.prototype.clear = mapCacheClear;\\n MapCache.prototype[\\\\\\\"delete\\\\\\\"] = mapCacheDelete;\\n MapCache.prototype.get = mapCacheGet;\\n MapCache.prototype.has = mapCacheHas;\\n MapCache.prototype.set = mapCacheSet;\\n module.exports = MapCache;\\n});\\n\\n// ../../../node_modules/lodash/_stackSet.js\\nvar require_stackSet = __commonJS((exports, module) => {\\n var ListCache = require_ListCache();\\n var Map2 = require_Map();\\n var MapCache = require_MapCache();\\n var LARGE_ARRAY_SIZE = 200;\\n function stackSet(key, value) {\\n var data = this.__data__;\\n if (data instanceof ListCache) {\\n var pairs = data.__data__;\\n if (!Map2 || pairs.length < LARGE_ARRAY_SIZE - 1) {\\n pairs.push([key, value]);\\n this.size = ++data.size;\\n return this;\\n }\\n data = this.__data__ = new MapCache(pairs);\\n }\\n data.set(key, value);\\n this.size = data.size;\\n return this;\\n }\\n module.exports = stackSet;\\n});\\n\\n// ../../../node_modules/lodash/_Stack.js\\nvar require_Stack = __commonJS((exports, module) => {\\n var ListCache = require_ListCache();\\n var stackClear = require_stackClear();\\n var stackDelete = require_stackDelete();\\n var stackGet = require_stackGet();\\n var stackHas = require_stackHas();\\n var stackSet = require_stackSet();\\n function Stack(entries) {\\n var data = this.__data__ = new ListCache(entries);\\n this.size = data.size;\\n }\\n Stack.prototype.clear = stackClear;\\n Stack.prototype[\\\\\\\"delete\\\\\\\"] = stackDelete;\\n Stack.prototype.get = stackGet;\\n Stack.prototype.has = stackHas;\\n Stack.prototype.set = stackSet;\\n module.exports = Stack;\\n});\\n\\n// ../../../node_modules/lodash/_arrayEach.js\\nvar require_arrayEach = __commonJS((exports, module) => {\\n function arrayEach(array, iteratee) {\\n var index = -1, length = array == null ? 0 : array.length;\\n while (++index < length) {\\n if (iteratee(array[index], index, array) === false) {\\n break;\\n }\\n }\\n return array;\\n }\\n module.exports = arrayEach;\\n});\\n\\n// ../../../node_modules/lodash/_defineProperty.js\\nvar require_defineProperty = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var defineProperty = function() {\\n try {\\n var func = getNative(Object, \\\\\\\"defineProperty\\\\\\\");\\n func({}, \\\\\\\"\\\\\\\", {});\\n return func;\\n } catch (e) {\\n }\\n }();\\n module.exports = defineProperty;\\n});\\n\\n// ../../../node_modules/lodash/_baseAssignValue.js\\nvar require_baseAssignValue = __commonJS((exports, module) => {\\n var defineProperty = require_defineProperty();\\n function baseAssignValue(object, key, value) {\\n if (key == \\\\\\\"__proto__\\\\\\\" && defineProperty) {\\n defineProperty(object, key, {\\n configurable: true,\\n enumerable: true,\\n value,\\n writable: true\\n });\\n } else {\\n object[key] = value;\\n }\\n }\\n module.exports = baseAssignValue;\\n});\\n\\n// ../../../node_modules/lodash/_assignValue.js\\nvar require_assignValue = __commonJS((exports, module) => {\\n var baseAssignValue = require_baseAssignValue();\\n var eq = require_eq();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function assignValue(object, key, value) {\\n var objValue = object[key];\\n if (!(hasOwnProperty.call(object, key) && eq(objValue, value)) || value === void 0 && !(key in object)) {\\n baseAssignValue(object, key, value);\\n }\\n }\\n module.exports = assignValue;\\n});\\n\\n// ../../../node_modules/lodash/_copyObject.js\\nvar require_copyObject = __commonJS((exports, module) => {\\n var assignValue = require_assignValue();\\n var baseAssignValue = require_baseAssignValue();\\n function copyObject(source, props, object, customizer) {\\n var isNew = !object;\\n object || (object = {});\\n var index = -1, length = props.length;\\n while (++index < length) {\\n var key = props[index];\\n var newValue = customizer ? customizer(object[key], source[key], key, object, source) : void 0;\\n if (newValue === void 0) {\\n newValue = source[key];\\n }\\n if (isNew) {\\n baseAssignValue(object, key, newValue);\\n } else {\\n assignValue(object, key, newValue);\\n }\\n }\\n return object;\\n }\\n module.exports = copyObject;\\n});\\n\\n// ../../../node_modules/lodash/_baseTimes.js\\nvar require_baseTimes = __commonJS((exports, module) => {\\n function baseTimes(n, iteratee) {\\n var index = -1, result = Array(n);\\n while (++index < n) {\\n result[index] = iteratee(index);\\n }\\n return result;\\n }\\n module.exports = baseTimes;\\n});\\n\\n// ../../../node_modules/lodash/isObjectLike.js\\nvar require_isObjectLike = __commonJS((exports, module) => {\\n function isObjectLike(value) {\\n return value != null && typeof value == \\\\\\\"object\\\\\\\";\\n }\\n module.exports = isObjectLike;\\n});\\n\\n// ../../../node_modules/lodash/_baseIsArguments.js\\nvar require_baseIsArguments = __commonJS((exports, module) => {\\n var baseGetTag = require_baseGetTag();\\n var isObjectLike = require_isObjectLike();\\n var argsTag = \\\\\\\"[object Arguments]\\\\\\\";\\n function baseIsArguments(value) {\\n return isObjectLike(value) && baseGetTag(value) == argsTag;\\n }\\n module.exports = baseIsArguments;\\n});\\n\\n// ../../../node_modules/lodash/isArguments.js\\nvar require_isArguments = __commonJS((exports, module) => {\\n var baseIsArguments = require_baseIsArguments();\\n var isObjectLike = require_isObjectLike();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n var propertyIsEnumerable = objectProto.propertyIsEnumerable;\\n var isArguments = baseIsArguments(function() {\\n return arguments;\\n }()) ? baseIsArguments : function(value) {\\n return isObjectLike(value) && hasOwnProperty.call(value, \\\\\\\"callee\\\\\\\") && !propertyIsEnumerable.call(value, \\\\\\\"callee\\\\\\\");\\n };\\n module.exports = isArguments;\\n});\\n\\n// ../../../node_modules/lodash/isArray.js\\nvar require_isArray = __commonJS((exports, module) => {\\n var isArray = Array.isArray;\\n module.exports = isArray;\\n});\\n\\n// ../../../node_modules/lodash/stubFalse.js\\nvar require_stubFalse = __commonJS((exports, module) => {\\n function stubFalse() {\\n return false;\\n }\\n module.exports = stubFalse;\\n});\\n\\n// ../../../node_modules/lodash/isBuffer.js\\nvar require_isBuffer = __commonJS((exports, module) => {\\n var root = require_root();\\n var stubFalse = require_stubFalse();\\n var freeExports = typeof exports == \\\\\\\"object\\\\\\\" && exports && !exports.nodeType && exports;\\n var freeModule = freeExports && typeof module == \\\\\\\"object\\\\\\\" && module && !module.nodeType && module;\\n var moduleExports = freeModule && freeModule.exports === freeExports;\\n var Buffer2 = moduleExports ? root.Buffer : void 0;\\n var nativeIsBuffer = Buffer2 ? Buffer2.isBuffer : void 0;\\n var isBuffer = nativeIsBuffer || stubFalse;\\n module.exports = isBuffer;\\n});\\n\\n// ../../../node_modules/lodash/_isIndex.js\\nvar require_isIndex = __commonJS((exports, module) => {\\n var MAX_SAFE_INTEGER = 9007199254740991;\\n var reIsUint = /^(?:0|[1-9]\\\\d*)$/;\\n function isIndex(value, length) {\\n var type = typeof value;\\n length = length == null ? MAX_SAFE_INTEGER : length;\\n return !!length && (type == \\\\\\\"number\\\\\\\" || type != \\\\\\\"symbol\\\\\\\" && reIsUint.test(value)) && (value > -1 && value % 1 == 0 && value < length);\\n }\\n module.exports = isIndex;\\n});\\n\\n// ../../../node_modules/lodash/isLength.js\\nvar require_isLength = __commonJS((exports, module) => {\\n var MAX_SAFE_INTEGER = 9007199254740991;\\n function isLength(value) {\\n return typeof value == \\\\\\\"number\\\\\\\" && value > -1 && value % 1 == 0 && value <= MAX_SAFE_INTEGER;\\n }\\n module.exports = isLength;\\n});\\n\\n// ../../../node_modules/lodash/_baseIsTypedArray.js\\nvar require_baseIsTypedArray = __commonJS((exports, module) => {\\n var baseGetTag = require_baseGetTag();\\n var isLength = require_isLength();\\n var isObjectLike = require_isObjectLike();\\n var argsTag = \\\\\\\"[object Arguments]\\\\\\\";\\n var arrayTag = \\\\\\\"[object Array]\\\\\\\";\\n var boolTag = \\\\\\\"[object Boolean]\\\\\\\";\\n var dateTag = \\\\\\\"[object Date]\\\\\\\";\\n var errorTag = \\\\\\\"[object Error]\\\\\\\";\\n var funcTag = \\\\\\\"[object Function]\\\\\\\";\\n var mapTag = \\\\\\\"[object Map]\\\\\\\";\\n var numberTag = \\\\\\\"[object Number]\\\\\\\";\\n var objectTag = \\\\\\\"[object Object]\\\\\\\";\\n var regexpTag = \\\\\\\"[object RegExp]\\\\\\\";\\n var setTag = \\\\\\\"[object Set]\\\\\\\";\\n var stringTag = \\\\\\\"[object String]\\\\\\\";\\n var weakMapTag = \\\\\\\"[object WeakMap]\\\\\\\";\\n var arrayBufferTag = \\\\\\\"[object ArrayBuffer]\\\\\\\";\\n var dataViewTag = \\\\\\\"[object DataView]\\\\\\\";\\n var float32Tag = \\\\\\\"[object Float32Array]\\\\\\\";\\n var float64Tag = \\\\\\\"[object Float64Array]\\\\\\\";\\n var int8Tag = \\\\\\\"[object Int8Array]\\\\\\\";\\n var int16Tag = \\\\\\\"[object Int16Array]\\\\\\\";\\n var int32Tag = \\\\\\\"[object Int32Array]\\\\\\\";\\n var uint8Tag = \\\\\\\"[object Uint8Array]\\\\\\\";\\n var uint8ClampedTag = \\\\\\\"[object Uint8ClampedArray]\\\\\\\";\\n var uint16Tag = \\\\\\\"[object Uint16Array]\\\\\\\";\\n var uint32Tag = \\\\\\\"[object Uint32Array]\\\\\\\";\\n var typedArrayTags = {};\\n typedArrayTags[float32Tag] = typedArrayTags[float64Tag] = typedArrayTags[int8Tag] = typedArrayTags[int16Tag] = typedArrayTags[int32Tag] = typedArrayTags[uint8Tag] = typedArrayTags[uint8ClampedTag] = typedArrayTags[uint16Tag] = typedArrayTags[uint32Tag] = true;\\n typedArrayTags[argsTag] = typedArrayTags[arrayTag] = typedArrayTags[arrayBufferTag] = typedArrayTags[boolTag] = typedArrayTags[dataViewTag] = typedArrayTags[dateTag] = typedArrayTags[errorTag] = typedArrayTags[funcTag] = typedArrayTags[mapTag] = typedArrayTags[numberTag] = typedArrayTags[objectTag] = typedArrayTags[regexpTag] = typedArrayTags[setTag] = typedArrayTags[stringTag] = typedArrayTags[weakMapTag] = false;\\n function baseIsTypedArray(value) {\\n return isObjectLike(value) && isLength(value.length) && !!typedArrayTags[baseGetTag(value)];\\n }\\n module.exports = baseIsTypedArray;\\n});\\n\\n// ../../../node_modules/lodash/_baseUnary.js\\nvar require_baseUnary = __commonJS((exports, module) => {\\n function baseUnary(func) {\\n return function(value) {\\n return func(value);\\n };\\n }\\n module.exports = baseUnary;\\n});\\n\\n// ../../../node_modules/lodash/_nodeUtil.js\\nvar require_nodeUtil = __commonJS((exports, module) => {\\n var freeGlobal = require_freeGlobal();\\n var freeExports = typeof exports == \\\\\\\"object\\\\\\\" && exports && !exports.nodeType && exports;\\n var freeModule = freeExports && typeof module == \\\\\\\"object\\\\\\\" && module && !module.nodeType && module;\\n var moduleExports = freeModule && freeModule.exports === freeExports;\\n var freeProcess = moduleExports && freeGlobal.process;\\n var nodeUtil = function() {\\n try {\\n var types = freeModule && freeModule.require && freeModule.require(\\\\\\\"util\\\\\\\").types;\\n if (types) {\\n return types;\\n }\\n return freeProcess && freeProcess.binding && freeProcess.binding(\\\\\\\"util\\\\\\\");\\n } catch (e) {\\n }\\n }();\\n module.exports = nodeUtil;\\n});\\n\\n// ../../../node_modules/lodash/isTypedArray.js\\nvar require_isTypedArray = __commonJS((exports, module) => {\\n var baseIsTypedArray = require_baseIsTypedArray();\\n var baseUnary = require_baseUnary();\\n var nodeUtil = require_nodeUtil();\\n var nodeIsTypedArray = nodeUtil && nodeUtil.isTypedArray;\\n var isTypedArray = nodeIsTypedArray ? baseUnary(nodeIsTypedArray) : baseIsTypedArray;\\n module.exports = isTypedArray;\\n});\\n\\n// ../../../node_modules/lodash/_arrayLikeKeys.js\\nvar require_arrayLikeKeys = __commonJS((exports, module) => {\\n var baseTimes = require_baseTimes();\\n var isArguments = require_isArguments();\\n var isArray = require_isArray();\\n var isBuffer = require_isBuffer();\\n var isIndex = require_isIndex();\\n var isTypedArray = require_isTypedArray();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function arrayLikeKeys(value, inherited) {\\n var isArr = isArray(value), isArg = !isArr && isArguments(value), isBuff = !isArr && !isArg && isBuffer(value), isType = !isArr && !isArg && !isBuff && isTypedArray(value), skipIndexes = isArr || isArg || isBuff || isType, result = skipIndexes ? baseTimes(value.length, String) : [], length = result.length;\\n for (var key in value) {\\n if ((inherited || hasOwnProperty.call(value, key)) && !(skipIndexes && (key == \\\\\\\"length\\\\\\\" || isBuff && (key == \\\\\\\"offset\\\\\\\" || key == \\\\\\\"parent\\\\\\\") || isType && (key == \\\\\\\"buffer\\\\\\\" || key == \\\\\\\"byteLength\\\\\\\" || key == \\\\\\\"byteOffset\\\\\\\") || isIndex(key, length)))) {\\n result.push(key);\\n }\\n }\\n return result;\\n }\\n module.exports = arrayLikeKeys;\\n});\\n\\n// ../../../node_modules/lodash/_isPrototype.js\\nvar require_isPrototype = __commonJS((exports, module) => {\\n var objectProto = Object.prototype;\\n function isPrototype(value) {\\n var Ctor = value && value.constructor, proto = typeof Ctor == \\\\\\\"function\\\\\\\" && Ctor.prototype || objectProto;\\n return value === proto;\\n }\\n module.exports = isPrototype;\\n});\\n\\n// ../../../node_modules/lodash/_overArg.js\\nvar require_overArg = __commonJS((exports, module) => {\\n function overArg(func, transform) {\\n return function(arg) {\\n return func(transform(arg));\\n };\\n }\\n module.exports = overArg;\\n});\\n\\n// ../../../node_modules/lodash/_nativeKeys.js\\nvar require_nativeKeys = __commonJS((exports, module) => {\\n var overArg = require_overArg();\\n var nativeKeys = overArg(Object.keys, Object);\\n module.exports = nativeKeys;\\n});\\n\\n// ../../../node_modules/lodash/_baseKeys.js\\nvar require_baseKeys = __commonJS((exports, module) => {\\n var isPrototype = require_isPrototype();\\n var nativeKeys = require_nativeKeys();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function baseKeys(object) {\\n if (!isPrototype(object)) {\\n return nativeKeys(object);\\n }\\n var result = [];\\n for (var key in Object(object)) {\\n if (hasOwnProperty.call(object, key) && key != \\\\\\\"constructor\\\\\\\") {\\n result.push(key);\\n }\\n }\\n return result;\\n }\\n module.exports = baseKeys;\\n});\\n\\n// ../../../node_modules/lodash/isArrayLike.js\\nvar require_isArrayLike = __commonJS((exports, module) => {\\n var isFunction = require_isFunction();\\n var isLength = require_isLength();\\n function isArrayLike(value) {\\n return value != null && isLength(value.length) && !isFunction(value);\\n }\\n module.exports = isArrayLike;\\n});\\n\\n// ../../../node_modules/lodash/keys.js\\nvar require_keys = __commonJS((exports, module) => {\\n var arrayLikeKeys = require_arrayLikeKeys();\\n var baseKeys = require_baseKeys();\\n var isArrayLike = require_isArrayLike();\\n function keys(object) {\\n return isArrayLike(object) ? arrayLikeKeys(object) : baseKeys(object);\\n }\\n module.exports = keys;\\n});\\n\\n// ../../../node_modules/lodash/_baseAssign.js\\nvar require_baseAssign = __commonJS((exports, module) => {\\n var copyObject = require_copyObject();\\n var keys = require_keys();\\n function baseAssign(object, source) {\\n return object && copyObject(source, keys(source), object);\\n }\\n module.exports = baseAssign;\\n});\\n\\n// ../../../node_modules/lodash/_nativeKeysIn.js\\nvar require_nativeKeysIn = __commonJS((exports, module) => {\\n function nativeKeysIn(object) {\\n var result = [];\\n if (object != null) {\\n for (var key in Object(object)) {\\n result.push(key);\\n }\\n }\\n return result;\\n }\\n module.exports = nativeKeysIn;\\n});\\n\\n// ../../../node_modules/lodash/_baseKeysIn.js\\nvar require_baseKeysIn = __commonJS((exports, module) => {\\n var isObject = require_isObject();\\n var isPrototype = require_isPrototype();\\n var nativeKeysIn = require_nativeKeysIn();\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function baseKeysIn(object) {\\n if (!isObject(object)) {\\n return nativeKeysIn(object);\\n }\\n var isProto = isPrototype(object), result = [];\\n for (var key in object) {\\n if (!(key == \\\\\\\"constructor\\\\\\\" && (isProto || !hasOwnProperty.call(object, key)))) {\\n result.push(key);\\n }\\n }\\n return result;\\n }\\n module.exports = baseKeysIn;\\n});\\n\\n// ../../../node_modules/lodash/keysIn.js\\nvar require_keysIn = __commonJS((exports, module) => {\\n var arrayLikeKeys = require_arrayLikeKeys();\\n var baseKeysIn = require_baseKeysIn();\\n var isArrayLike = require_isArrayLike();\\n function keysIn(object) {\\n return isArrayLike(object) ? arrayLikeKeys(object, true) : baseKeysIn(object);\\n }\\n module.exports = keysIn;\\n});\\n\\n// ../../../node_modules/lodash/_baseAssignIn.js\\nvar require_baseAssignIn = __commonJS((exports, module) => {\\n var copyObject = require_copyObject();\\n var keysIn = require_keysIn();\\n function baseAssignIn(object, source) {\\n return object && copyObject(source, keysIn(source), object);\\n }\\n module.exports = baseAssignIn;\\n});\\n\\n// ../../../node_modules/lodash/_cloneBuffer.js\\nvar require_cloneBuffer = __commonJS((exports, module) => {\\n var root = require_root();\\n var freeExports = typeof exports == \\\\\\\"object\\\\\\\" && exports && !exports.nodeType && exports;\\n var freeModule = freeExports && typeof module == \\\\\\\"object\\\\\\\" && module && !module.nodeType && module;\\n var moduleExports = freeModule && freeModule.exports === freeExports;\\n var Buffer2 = moduleExports ? root.Buffer : void 0;\\n var allocUnsafe = Buffer2 ? Buffer2.allocUnsafe : void 0;\\n function cloneBuffer(buffer, isDeep) {\\n if (isDeep) {\\n return buffer.slice();\\n }\\n var length = buffer.length, result = allocUnsafe ? allocUnsafe(length) : new buffer.constructor(length);\\n buffer.copy(result);\\n return result;\\n }\\n module.exports = cloneBuffer;\\n});\\n\\n// ../../../node_modules/lodash/_copyArray.js\\nvar require_copyArray = __commonJS((exports, module) => {\\n function copyArray2(source, array) {\\n var index = -1, length = source.length;\\n array || (array = Array(length));\\n while (++index < length) {\\n array[index] = source[index];\\n }\\n return array;\\n }\\n module.exports = copyArray2;\\n});\\n\\n// ../../../node_modules/lodash/_arrayFilter.js\\nvar require_arrayFilter = __commonJS((exports, module) => {\\n function arrayFilter(array, predicate) {\\n var index = -1, length = array == null ? 0 : array.length, resIndex = 0, result = [];\\n while (++index < length) {\\n var value = array[index];\\n if (predicate(value, index, array)) {\\n result[resIndex++] = value;\\n }\\n }\\n return result;\\n }\\n module.exports = arrayFilter;\\n});\\n\\n// ../../../node_modules/lodash/stubArray.js\\nvar require_stubArray = __commonJS((exports, module) => {\\n function stubArray() {\\n return [];\\n }\\n module.exports = stubArray;\\n});\\n\\n// ../../../node_modules/lodash/_getSymbols.js\\nvar require_getSymbols = __commonJS((exports, module) => {\\n var arrayFilter = require_arrayFilter();\\n var stubArray = require_stubArray();\\n var objectProto = Object.prototype;\\n var propertyIsEnumerable = objectProto.propertyIsEnumerable;\\n var nativeGetSymbols = Object.getOwnPropertySymbols;\\n var getSymbols = !nativeGetSymbols ? stubArray : function(object) {\\n if (object == null) {\\n return [];\\n }\\n object = Object(object);\\n return arrayFilter(nativeGetSymbols(object), function(symbol) {\\n return propertyIsEnumerable.call(object, symbol);\\n });\\n };\\n module.exports = getSymbols;\\n});\\n\\n// ../../../node_modules/lodash/_copySymbols.js\\nvar require_copySymbols = __commonJS((exports, module) => {\\n var copyObject = require_copyObject();\\n var getSymbols = require_getSymbols();\\n function copySymbols(source, object) {\\n return copyObject(source, getSymbols(source), object);\\n }\\n module.exports = copySymbols;\\n});\\n\\n// ../../../node_modules/lodash/_arrayPush.js\\nvar require_arrayPush = __commonJS((exports, module) => {\\n function arrayPush(array, values) {\\n var index = -1, length = values.length, offset = array.length;\\n while (++index < length) {\\n array[offset + index] = values[index];\\n }\\n return array;\\n }\\n module.exports = arrayPush;\\n});\\n\\n// ../../../node_modules/lodash/_getPrototype.js\\nvar require_getPrototype = __commonJS((exports, module) => {\\n var overArg = require_overArg();\\n var getPrototype = overArg(Object.getPrototypeOf, Object);\\n module.exports = getPrototype;\\n});\\n\\n// ../../../node_modules/lodash/_getSymbolsIn.js\\nvar require_getSymbolsIn = __commonJS((exports, module) => {\\n var arrayPush = require_arrayPush();\\n var getPrototype = require_getPrototype();\\n var getSymbols = require_getSymbols();\\n var stubArray = require_stubArray();\\n var nativeGetSymbols = Object.getOwnPropertySymbols;\\n var getSymbolsIn = !nativeGetSymbols ? stubArray : function(object) {\\n var result = [];\\n while (object) {\\n arrayPush(result, getSymbols(object));\\n object = getPrototype(object);\\n }\\n return result;\\n };\\n module.exports = getSymbolsIn;\\n});\\n\\n// ../../../node_modules/lodash/_copySymbolsIn.js\\nvar require_copySymbolsIn = __commonJS((exports, module) => {\\n var copyObject = require_copyObject();\\n var getSymbolsIn = require_getSymbolsIn();\\n function copySymbolsIn(source, object) {\\n return copyObject(source, getSymbolsIn(source), object);\\n }\\n module.exports = copySymbolsIn;\\n});\\n\\n// ../../../node_modules/lodash/_baseGetAllKeys.js\\nvar require_baseGetAllKeys = __commonJS((exports, module) => {\\n var arrayPush = require_arrayPush();\\n var isArray = require_isArray();\\n function baseGetAllKeys(object, keysFunc, symbolsFunc) {\\n var result = keysFunc(object);\\n return isArray(object) ? result : arrayPush(result, symbolsFunc(object));\\n }\\n module.exports = baseGetAllKeys;\\n});\\n\\n// ../../../node_modules/lodash/_getAllKeys.js\\nvar require_getAllKeys = __commonJS((exports, module) => {\\n var baseGetAllKeys = require_baseGetAllKeys();\\n var getSymbols = require_getSymbols();\\n var keys = require_keys();\\n function getAllKeys(object) {\\n return baseGetAllKeys(object, keys, getSymbols);\\n }\\n module.exports = getAllKeys;\\n});\\n\\n// ../../../node_modules/lodash/_getAllKeysIn.js\\nvar require_getAllKeysIn = __commonJS((exports, module) => {\\n var baseGetAllKeys = require_baseGetAllKeys();\\n var getSymbolsIn = require_getSymbolsIn();\\n var keysIn = require_keysIn();\\n function getAllKeysIn(object) {\\n return baseGetAllKeys(object, keysIn, getSymbolsIn);\\n }\\n module.exports = getAllKeysIn;\\n});\\n\\n// ../../../node_modules/lodash/_DataView.js\\nvar require_DataView = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var root = require_root();\\n var DataView = getNative(root, \\\\\\\"DataView\\\\\\\");\\n module.exports = DataView;\\n});\\n\\n// ../../../node_modules/lodash/_Promise.js\\nvar require_Promise = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var root = require_root();\\n var Promise2 = getNative(root, \\\\\\\"Promise\\\\\\\");\\n module.exports = Promise2;\\n});\\n\\n// ../../../node_modules/lodash/_Set.js\\nvar require_Set = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var root = require_root();\\n var Set2 = getNative(root, \\\\\\\"Set\\\\\\\");\\n module.exports = Set2;\\n});\\n\\n// ../../../node_modules/lodash/_WeakMap.js\\nvar require_WeakMap = __commonJS((exports, module) => {\\n var getNative = require_getNative();\\n var root = require_root();\\n var WeakMap2 = getNative(root, \\\\\\\"WeakMap\\\\\\\");\\n module.exports = WeakMap2;\\n});\\n\\n// ../../../node_modules/lodash/_getTag.js\\nvar require_getTag = __commonJS((exports, module) => {\\n var DataView = require_DataView();\\n var Map2 = require_Map();\\n var Promise2 = require_Promise();\\n var Set2 = require_Set();\\n var WeakMap2 = require_WeakMap();\\n var baseGetTag = require_baseGetTag();\\n var toSource = require_toSource();\\n var mapTag = \\\\\\\"[object Map]\\\\\\\";\\n var objectTag = \\\\\\\"[object Object]\\\\\\\";\\n var promiseTag = \\\\\\\"[object Promise]\\\\\\\";\\n var setTag = \\\\\\\"[object Set]\\\\\\\";\\n var weakMapTag = \\\\\\\"[object WeakMap]\\\\\\\";\\n var dataViewTag = \\\\\\\"[object DataView]\\\\\\\";\\n var dataViewCtorString = toSource(DataView);\\n var mapCtorString = toSource(Map2);\\n var promiseCtorString = toSource(Promise2);\\n var setCtorString = toSource(Set2);\\n var weakMapCtorString = toSource(WeakMap2);\\n var getTag = baseGetTag;\\n if (DataView && getTag(new DataView(new ArrayBuffer(1))) != dataViewTag || Map2 && getTag(new Map2()) != mapTag || Promise2 && getTag(Promise2.resolve()) != promiseTag || Set2 && getTag(new Set2()) != setTag || WeakMap2 && getTag(new WeakMap2()) != weakMapTag) {\\n getTag = function(value) {\\n var result = baseGetTag(value), Ctor = result == objectTag ? value.constructor : void 0, ctorString = Ctor ? toSource(Ctor) : \\\\\\\"\\\\\\\";\\n if (ctorString) {\\n switch (ctorString) {\\n case dataViewCtorString:\\n return dataViewTag;\\n case mapCtorString:\\n return mapTag;\\n case promiseCtorString:\\n return promiseTag;\\n case setCtorString:\\n return setTag;\\n case weakMapCtorString:\\n return weakMapTag;\\n }\\n }\\n return result;\\n };\\n }\\n module.exports = getTag;\\n});\\n\\n// ../../../node_modules/lodash/_initCloneArray.js\\nvar require_initCloneArray = __commonJS((exports, module) => {\\n var objectProto = Object.prototype;\\n var hasOwnProperty = objectProto.hasOwnProperty;\\n function initCloneArray(array) {\\n var length = array.length, result = new array.constructor(length);\\n if (length && typeof array[0] == \\\\\\\"string\\\\\\\" && hasOwnProperty.call(array, \\\\\\\"index\\\\\\\")) {\\n result.index = array.index;\\n result.input = array.input;\\n }\\n return result;\\n }\\n module.exports = initCloneArray;\\n});\\n\\n// ../../../node_modules/lodash/_Uint8Array.js\\nvar require_Uint8Array = __commonJS((exports, module) => {\\n var root = require_root();\\n var Uint8Array2 = root.Uint8Array;\\n module.exports = Uint8Array2;\\n});\\n\\n// ../../../node_modules/lodash/_cloneArrayBuffer.js\\nvar require_cloneArrayBuffer = __commonJS((exports, module) => {\\n var Uint8Array2 = require_Uint8Array();\\n function cloneArrayBuffer(arrayBuffer) {\\n var result = new arrayBuffer.constructor(arrayBuffer.byteLength);\\n new Uint8Array2(result).set(new Uint8Array2(arrayBuffer));\\n return result;\\n }\\n module.exports = cloneArrayBuffer;\\n});\\n\\n// ../../../node_modules/lodash/_cloneDataView.js\\nvar require_cloneDataView = __commonJS((exports, module) => {\\n var cloneArrayBuffer = require_cloneArrayBuffer();\\n function cloneDataView(dataView, isDeep) {\\n var buffer = isDeep ? cloneArrayBuffer(dataView.buffer) : dataView.buffer;\\n return new dataView.constructor(buffer, dataView.byteOffset, dataView.byteLength);\\n }\\n module.exports = cloneDataView;\\n});\\n\\n// ../../../node_modules/lodash/_cloneRegExp.js\\nvar require_cloneRegExp = __commonJS((exports, module) => {\\n var reFlags = /\\\\w*$/;\\n function cloneRegExp(regexp) {\\n var result = new regexp.constructor(regexp.source, reFlags.exec(regexp));\\n result.lastIndex = regexp.lastIndex;\\n return result;\\n }\\n module.exports = cloneRegExp;\\n});\\n\\n// ../../../node_modules/lodash/_cloneSymbol.js\\nvar require_cloneSymbol = __commonJS((exports, module) => {\\n var Symbol2 = require_Symbol();\\n var symbolProto = Symbol2 ? Symbol2.prototype : void 0;\\n var symbolValueOf = symbolProto ? symbolProto.valueOf : void 0;\\n function cloneSymbol(symbol) {\\n return symbolValueOf ? Object(symbolValueOf.call(symbol)) : {};\\n }\\n module.exports = cloneSymbol;\\n});\\n\\n// ../../../node_modules/lodash/_cloneTypedArray.js\\nvar require_cloneTypedArray = __commonJS((exports, module) => {\\n var cloneArrayBuffer = require_cloneArrayBuffer();\\n function cloneTypedArray(typedArray, isDeep) {\\n var buffer = isDeep ? cloneArrayBuffer(typedArray.buffer) : typedArray.buffer;\\n return new typedArray.constructor(buffer, typedArray.byteOffset, typedArray.length);\\n }\\n module.exports = cloneTypedArray;\\n});\\n\\n// ../../../node_modules/lodash/_initCloneByTag.js\\nvar require_initCloneByTag = __commonJS((exports, module) => {\\n var cloneArrayBuffer = require_cloneArrayBuffer();\\n var cloneDataView = require_cloneDataView();\\n var cloneRegExp = require_cloneRegExp();\\n var cloneSymbol = require_cloneSymbol();\\n var cloneTypedArray = require_cloneTypedArray();\\n var boolTag = \\\\\\\"[object Boolean]\\\\\\\";\\n var dateTag = \\\\\\\"[object Date]\\\\\\\";\\n var mapTag = \\\\\\\"[object Map]\\\\\\\";\\n var numberTag = \\\\\\\"[object Number]\\\\\\\";\\n var regexpTag = \\\\\\\"[object RegExp]\\\\\\\";\\n var setTag = \\\\\\\"[object Set]\\\\\\\";\\n var stringTag = \\\\\\\"[object String]\\\\\\\";\\n var symbolTag = \\\\\\\"[object Symbol]\\\\\\\";\\n var arrayBufferTag = \\\\\\\"[object ArrayBuffer]\\\\\\\";\\n var dataViewTag = \\\\\\\"[object DataView]\\\\\\\";\\n var float32Tag = \\\\\\\"[object Float32Array]\\\\\\\";\\n var float64Tag = \\\\\\\"[object Float64Array]\\\\\\\";\\n var int8Tag = \\\\\\\"[object Int8Array]\\\\\\\";\\n var int16Tag = \\\\\\\"[object Int16Array]\\\\\\\";\\n var int32Tag = \\\\\\\"[object Int32Array]\\\\\\\";\\n var uint8Tag = \\\\\\\"[object Uint8Array]\\\\\\\";\\n var uint8ClampedTag = \\\\\\\"[object Uint8ClampedArray]\\\\\\\";\\n var uint16Tag = \\\\\\\"[object Uint16Array]\\\\\\\";\\n var uint32Tag = \\\\\\\"[object Uint32Array]\\\\\\\";\\n function initCloneByTag(object, tag, isDeep) {\\n var Ctor = object.constructor;\\n switch (tag) {\\n case arrayBufferTag:\\n return cloneArrayBuffer(object);\\n case boolTag:\\n case dateTag:\\n return new Ctor(+object);\\n case dataViewTag:\\n return cloneDataView(object, isDeep);\\n case float32Tag:\\n case float64Tag:\\n case int8Tag:\\n case int16Tag:\\n case int32Tag:\\n case uint8Tag:\\n case uint8ClampedTag:\\n case uint16Tag:\\n case uint32Tag:\\n return cloneTypedArray(object, isDeep);\\n case mapTag:\\n return new Ctor();\\n case numberTag:\\n case stringTag:\\n return new Ctor(object);\\n case regexpTag:\\n return cloneRegExp(object);\\n case setTag:\\n return new Ctor();\\n case symbolTag:\\n return cloneSymbol(object);\\n }\\n }\\n module.exports = initCloneByTag;\\n});\\n\\n// ../../../node_modules/lodash/_baseCreate.js\\nvar require_baseCreate = __commonJS((exports, module) => {\\n var isObject = require_isObject();\\n var objectCreate = Object.create;\\n var baseCreate = function() {\\n function object() {\\n }\\n return function(proto) {\\n if (!isObject(proto)) {\\n return {};\\n }\\n if (objectCreate) {\\n return objectCreate(proto);\\n }\\n object.prototype = proto;\\n var result = new object();\\n object.prototype = void 0;\\n return result;\\n };\\n }();\\n module.exports = baseCreate;\\n});\\n\\n// ../../../node_modules/lodash/_initCloneObject.js\\nvar require_initCloneObject = __commonJS((exports, module) => {\\n var baseCreate = require_baseCreate();\\n var getPrototype = require_getPrototype();\\n var isPrototype = require_isPrototype();\\n function initCloneObject(object) {\\n return typeof object.constructor == \\\\\\\"function\\\\\\\" && !isPrototype(object) ? baseCreate(getPrototype(object)) : {};\\n }\\n module.exports = initCloneObject;\\n});\\n\\n// ../../../node_modules/lodash/_baseIsMap.js\\nvar require_baseIsMap = __commonJS((exports, module) => {\\n var getTag = require_getTag();\\n var isObjectLike = require_isObjectLike();\\n var mapTag = \\\\\\\"[object Map]\\\\\\\";\\n function baseIsMap(value) {\\n return isObjectLike(value) && getTag(value) == mapTag;\\n }\\n module.exports = baseIsMap;\\n});\\n\\n// ../../../node_modules/lodash/isMap.js\\nvar require_isMap = __commonJS((exports, module) => {\\n var baseIsMap = require_baseIsMap();\\n var baseUnary = require_baseUnary();\\n var nodeUtil = require_nodeUtil();\\n var nodeIsMap = nodeUtil && nodeUtil.isMap;\\n var isMap = nodeIsMap ? baseUnary(nodeIsMap) : baseIsMap;\\n module.exports = isMap;\\n});\\n\\n// ../../../node_modules/lodash/_baseIsSet.js\\nvar require_baseIsSet = __commonJS((exports, module) => {\\n var getTag = require_getTag();\\n var isObjectLike = require_isObjectLike();\\n var setTag = \\\\\\\"[object Set]\\\\\\\";\\n function baseIsSet(value) {\\n return isObjectLike(value) && getTag(value) == setTag;\\n }\\n module.exports = baseIsSet;\\n});\\n\\n// ../../../node_modules/lodash/isSet.js\\nvar require_isSet = __commonJS((exports, module) => {\\n var baseIsSet = require_baseIsSet();\\n var baseUnary = require_baseUnary();\\n var nodeUtil = require_nodeUtil();\\n var nodeIsSet = nodeUtil && nodeUtil.isSet;\\n var isSet = nodeIsSet ? baseUnary(nodeIsSet) : baseIsSet;\\n module.exports = isSet;\\n});\\n\\n// ../../../node_modules/lodash/_baseClone.js\\nvar require_baseClone = __commonJS((exports, module) => {\\n var Stack = require_Stack();\\n var arrayEach = require_arrayEach();\\n var assignValue = require_assignValue();\\n var baseAssign = require_baseAssign();\\n var baseAssignIn = require_baseAssignIn();\\n var cloneBuffer = require_cloneBuffer();\\n var copyArray2 = require_copyArray();\\n var copySymbols = require_copySymbols();\\n var copySymbolsIn = require_copySymbolsIn();\\n var getAllKeys = require_getAllKeys();\\n var getAllKeysIn = require_getAllKeysIn();\\n var getTag = require_getTag();\\n var initCloneArray = require_initCloneArray();\\n var initCloneByTag = require_initCloneByTag();\\n var initCloneObject = require_initCloneObject();\\n var isArray = require_isArray();\\n var isBuffer = require_isBuffer();\\n var isMap = require_isMap();\\n var isObject = require_isObject();\\n var isSet = require_isSet();\\n var keys = require_keys();\\n var keysIn = require_keysIn();\\n var CLONE_DEEP_FLAG = 1;\\n var CLONE_FLAT_FLAG = 2;\\n var CLONE_SYMBOLS_FLAG = 4;\\n var argsTag = \\\\\\\"[object Arguments]\\\\\\\";\\n var arrayTag = \\\\\\\"[object Array]\\\\\\\";\\n var boolTag = \\\\\\\"[object Boolean]\\\\\\\";\\n var dateTag = \\\\\\\"[object Date]\\\\\\\";\\n var errorTag = \\\\\\\"[object Error]\\\\\\\";\\n var funcTag = \\\\\\\"[object Function]\\\\\\\";\\n var genTag = \\\\\\\"[object GeneratorFunction]\\\\\\\";\\n var mapTag = \\\\\\\"[object Map]\\\\\\\";\\n var numberTag = \\\\\\\"[object Number]\\\\\\\";\\n var objectTag = \\\\\\\"[object Object]\\\\\\\";\\n var regexpTag = \\\\\\\"[object RegExp]\\\\\\\";\\n var setTag = \\\\\\\"[object Set]\\\\\\\";\\n var stringTag = \\\\\\\"[object String]\\\\\\\";\\n var symbolTag = \\\\\\\"[object Symbol]\\\\\\\";\\n var weakMapTag = \\\\\\\"[object WeakMap]\\\\\\\";\\n var arrayBufferTag = \\\\\\\"[object ArrayBuffer]\\\\\\\";\\n var dataViewTag = \\\\\\\"[object DataView]\\\\\\\";\\n var float32Tag = \\\\\\\"[object Float32Array]\\\\\\\";\\n var float64Tag = \\\\\\\"[object Float64Array]\\\\\\\";\\n var int8Tag = \\\\\\\"[object Int8Array]\\\\\\\";\\n var int16Tag = \\\\\\\"[object Int16Array]\\\\\\\";\\n var int32Tag = \\\\\\\"[object Int32Array]\\\\\\\";\\n var uint8Tag = \\\\\\\"[object Uint8Array]\\\\\\\";\\n var uint8ClampedTag = \\\\\\\"[object Uint8ClampedArray]\\\\\\\";\\n var uint16Tag = \\\\\\\"[object Uint16Array]\\\\\\\";\\n var uint32Tag = \\\\\\\"[object Uint32Array]\\\\\\\";\\n var cloneableTags = {};\\n cloneableTags[argsTag] = cloneableTags[arrayTag] = cloneableTags[arrayBufferTag] = cloneableTags[dataViewTag] = cloneableTags[boolTag] = cloneableTags[dateTag] = cloneableTags[float32Tag] = cloneableTags[float64Tag] = cloneableTags[int8Tag] = cloneableTags[int16Tag] = cloneableTags[int32Tag] = cloneableTags[mapTag] = cloneableTags[numberTag] = cloneableTags[objectTag] = cloneableTags[regexpTag] = cloneableTags[setTag] = cloneableTags[stringTag] = cloneableTags[symbolTag] = cloneableTags[uint8Tag] = cloneableTags[uint8ClampedTag] = cloneableTags[uint16Tag] = cloneableTags[uint32Tag] = true;\\n cloneableTags[errorTag] = cloneableTags[funcTag] = cloneableTags[weakMapTag] = false;\\n function baseClone(value, bitmask, customizer, key, object, stack) {\\n var result, isDeep = bitmask & CLONE_DEEP_FLAG, isFlat = bitmask & CLONE_FLAT_FLAG, isFull = bitmask & CLONE_SYMBOLS_FLAG;\\n if (customizer) {\\n result = object ? customizer(value, key, object, stack) : customizer(value);\\n }\\n if (result !== void 0) {\\n return result;\\n }\\n if (!isObject(value)) {\\n return value;\\n }\\n var isArr = isArray(value);\\n if (isArr) {\\n result = initCloneArray(value);\\n if (!isDeep) {\\n return copyArray2(value, result);\\n }\\n } else {\\n var tag = getTag(value), isFunc = tag == funcTag || tag == genTag;\\n if (isBuffer(value)) {\\n return cloneBuffer(value, isDeep);\\n }\\n if (tag == objectTag || tag == argsTag || isFunc && !object) {\\n result = isFlat || isFunc ? {} : initCloneObject(value);\\n if (!isDeep) {\\n return isFlat ? copySymbolsIn(value, baseAssignIn(result, value)) : copySymbols(value, baseAssign(result, value));\\n }\\n } else {\\n if (!cloneableTags[tag]) {\\n return object ? value : {};\\n }\\n result = initCloneByTag(value, tag, isDeep);\\n }\\n }\\n stack || (stack = new Stack());\\n var stacked = stack.get(value);\\n if (stacked) {\\n return stacked;\\n }\\n stack.set(value, result);\\n if (isSet(value)) {\\n value.forEach(function(subValue) {\\n result.add(baseClone(subValue, bitmask, customizer, subValue, value, stack));\\n });\\n } else if (isMap(value)) {\\n value.forEach(function(subValue, key2) {\\n result.set(key2, baseClone(subValue, bitmask, customizer, key2, value, stack));\\n });\\n }\\n var keysFunc = isFull ? isFlat ? getAllKeysIn : getAllKeys : isFlat ? keysIn : keys;\\n var props = isArr ? void 0 : keysFunc(value);\\n arrayEach(props || value, function(subValue, key2) {\\n if (props) {\\n key2 = subValue;\\n subValue = value[key2];\\n }\\n assignValue(result, key2, baseClone(subValue, bitmask, customizer, key2, value, stack));\\n });\\n return result;\\n }\\n module.exports = baseClone;\\n});\\n\\n// ../../../node_modules/lodash/cloneDeep.js\\nvar require_cloneDeep = __commonJS((exports, module) => {\\n var baseClone = require_baseClone();\\n var CLONE_DEEP_FLAG = 1;\\n var CLONE_SYMBOLS_FLAG = 4;\\n function cloneDeep(value) {\\n return baseClone(value, CLONE_DEEP_FLAG | CLONE_SYMBOLS_FLAG);\\n }\\n module.exports = cloneDeep;\\n});\\n\\n// ../../../node_modules/lodash/clone.js\\nvar require_clone = __commonJS((exports, module) => {\\n var baseClone = require_baseClone();\\n var CLONE_SYMBOLS_FLAG = 4;\\n function clone(value) {\\n return baseClone(value, CLONE_SYMBOLS_FLAG);\\n }\\n module.exports = clone;\\n});\\n\\n// ../../../node_modules/jsep/build/jsep.js\\nvar require_jsep = __commonJS((exports, module) => {\\n (function(root) {\\n \\\\\\\"use strict\\\\\\\";\\n var COMPOUND = \\\\\\\"Compound\\\\\\\", IDENTIFIER = \\\\\\\"Identifier\\\\\\\", MEMBER_EXP = \\\\\\\"MemberExpression\\\\\\\", LITERAL = \\\\\\\"Literal\\\\\\\", THIS_EXP = \\\\\\\"ThisExpression\\\\\\\", CALL_EXP = \\\\\\\"CallExpression\\\\\\\", UNARY_EXP = \\\\\\\"UnaryExpression\\\\\\\", BINARY_EXP = \\\\\\\"BinaryExpression\\\\\\\", LOGICAL_EXP = \\\\\\\"LogicalExpression\\\\\\\", CONDITIONAL_EXP = \\\\\\\"ConditionalExpression\\\\\\\", ARRAY_EXP = \\\\\\\"ArrayExpression\\\\\\\", PERIOD_CODE = 46, COMMA_CODE = 44, SQUOTE_CODE = 39, DQUOTE_CODE = 34, OPAREN_CODE = 40, CPAREN_CODE = 41, OBRACK_CODE = 91, CBRACK_CODE = 93, QUMARK_CODE = 63, SEMCOL_CODE = 59, COLON_CODE = 58, throwError = function(message, index) {\\n var error = new Error(message + \\\\\\\" at character \\\\\\\" + index);\\n error.index = index;\\n error.description = message;\\n throw error;\\n }, t = true, unary_ops = {\\\\\\\"-\\\\\\\": t, \\\\\\\"!\\\\\\\": t, \\\\\\\"~\\\\\\\": t, \\\\\\\"+\\\\\\\": t}, binary_ops = {\\n \\\\\\\"||\\\\\\\": 1,\\n \\\\\\\"&&\\\\\\\": 2,\\n \\\\\\\"|\\\\\\\": 3,\\n \\\\\\\"^\\\\\\\": 4,\\n \\\\\\\"&\\\\\\\": 5,\\n \\\\\\\"==\\\\\\\": 6,\\n \\\\\\\"!=\\\\\\\": 6,\\n \\\\\\\"===\\\\\\\": 6,\\n \\\\\\\"!==\\\\\\\": 6,\\n \\\\\\\"<\\\\\\\": 7,\\n \\\\\\\">\\\\\\\": 7,\\n \\\\\\\"<=\\\\\\\": 7,\\n \\\\\\\">=\\\\\\\": 7,\\n \\\\\\\"<<\\\\\\\": 8,\\n \\\\\\\">>\\\\\\\": 8,\\n \\\\\\\">>>\\\\\\\": 8,\\n \\\\\\\"+\\\\\\\": 9,\\n \\\\\\\"-\\\\\\\": 9,\\n \\\\\\\"*\\\\\\\": 10,\\n \\\\\\\"/\\\\\\\": 10,\\n \\\\\\\"%\\\\\\\": 10\\n }, getMaxKeyLen = function(obj) {\\n var max_len = 0, len;\\n for (var key in obj) {\\n if ((len = key.length) > max_len && obj.hasOwnProperty(key)) {\\n max_len = len;\\n }\\n }\\n return max_len;\\n }, max_unop_len = getMaxKeyLen(unary_ops), max_binop_len = getMaxKeyLen(binary_ops), literals = {\\n true: true,\\n false: false,\\n null: null\\n }, this_str = \\\\\\\"this\\\\\\\", binaryPrecedence = function(op_val) {\\n return binary_ops[op_val] || 0;\\n }, createBinaryExpression = function(operator, left, right) {\\n var type = operator === \\\\\\\"||\\\\\\\" || operator === \\\\\\\"&&\\\\\\\" ? LOGICAL_EXP : BINARY_EXP;\\n return {\\n type,\\n operator,\\n left,\\n right\\n };\\n }, isDecimalDigit = function(ch) {\\n return ch >= 48 && ch <= 57;\\n }, isIdentifierStart = function(ch) {\\n return ch === 36 || ch === 95 || ch >= 65 && ch <= 90 || ch >= 97 && ch <= 122 || ch >= 128 && !binary_ops[String.fromCharCode(ch)];\\n }, isIdentifierPart = function(ch) {\\n return ch === 36 || ch === 95 || ch >= 65 && ch <= 90 || ch >= 97 && ch <= 122 || ch >= 48 && ch <= 57 || ch >= 128 && !binary_ops[String.fromCharCode(ch)];\\n }, jsep2 = function(expr) {\\n var index = 0, charAtFunc = expr.charAt, charCodeAtFunc = expr.charCodeAt, exprI = function(i) {\\n return charAtFunc.call(expr, i);\\n }, exprICode = function(i) {\\n return charCodeAtFunc.call(expr, i);\\n }, length = expr.length, gobbleSpaces = function() {\\n var ch = exprICode(index);\\n while (ch === 32 || ch === 9 || ch === 10 || ch === 13) {\\n ch = exprICode(++index);\\n }\\n }, gobbleExpression = function() {\\n var test = gobbleBinaryExpression(), consequent, alternate;\\n gobbleSpaces();\\n if (exprICode(index) === QUMARK_CODE) {\\n index++;\\n consequent = gobbleExpression();\\n if (!consequent) {\\n throwError(\\\\\\\"Expected expression\\\\\\\", index);\\n }\\n gobbleSpaces();\\n if (exprICode(index) === COLON_CODE) {\\n index++;\\n alternate = gobbleExpression();\\n if (!alternate) {\\n throwError(\\\\\\\"Expected expression\\\\\\\", index);\\n }\\n return {\\n type: CONDITIONAL_EXP,\\n test,\\n consequent,\\n alternate\\n };\\n } else {\\n throwError(\\\\\\\"Expected :\\\\\\\", index);\\n }\\n } else {\\n return test;\\n }\\n }, gobbleBinaryOp = function() {\\n gobbleSpaces();\\n var biop, to_check = expr.substr(index, max_binop_len), tc_len = to_check.length;\\n while (tc_len > 0) {\\n if (binary_ops.hasOwnProperty(to_check) && (!isIdentifierStart(exprICode(index)) || index + to_check.length < expr.length && !isIdentifierPart(exprICode(index + to_check.length)))) {\\n index += tc_len;\\n return to_check;\\n }\\n to_check = to_check.substr(0, --tc_len);\\n }\\n return false;\\n }, gobbleBinaryExpression = function() {\\n var ch_i2, node2, biop, prec, stack, biop_info, left, right, i, cur_biop;\\n left = gobbleToken();\\n biop = gobbleBinaryOp();\\n if (!biop) {\\n return left;\\n }\\n biop_info = {value: biop, prec: binaryPrecedence(biop)};\\n right = gobbleToken();\\n if (!right) {\\n throwError(\\\\\\\"Expected expression after \\\\\\\" + biop, index);\\n }\\n stack = [left, biop_info, right];\\n while (biop = gobbleBinaryOp()) {\\n prec = binaryPrecedence(biop);\\n if (prec === 0) {\\n break;\\n }\\n biop_info = {value: biop, prec};\\n cur_biop = biop;\\n while (stack.length > 2 && prec <= stack[stack.length - 2].prec) {\\n right = stack.pop();\\n biop = stack.pop().value;\\n left = stack.pop();\\n node2 = createBinaryExpression(biop, left, right);\\n stack.push(node2);\\n }\\n node2 = gobbleToken();\\n if (!node2) {\\n throwError(\\\\\\\"Expected expression after \\\\\\\" + cur_biop, index);\\n }\\n stack.push(biop_info, node2);\\n }\\n i = stack.length - 1;\\n node2 = stack[i];\\n while (i > 1) {\\n node2 = createBinaryExpression(stack[i - 1].value, stack[i - 2], node2);\\n i -= 2;\\n }\\n return node2;\\n }, gobbleToken = function() {\\n var ch, to_check, tc_len;\\n gobbleSpaces();\\n ch = exprICode(index);\\n if (isDecimalDigit(ch) || ch === PERIOD_CODE) {\\n return gobbleNumericLiteral();\\n } else if (ch === SQUOTE_CODE || ch === DQUOTE_CODE) {\\n return gobbleStringLiteral();\\n } else if (ch === OBRACK_CODE) {\\n return gobbleArray();\\n } else {\\n to_check = expr.substr(index, max_unop_len);\\n tc_len = to_check.length;\\n while (tc_len > 0) {\\n if (unary_ops.hasOwnProperty(to_check) && (!isIdentifierStart(exprICode(index)) || index + to_check.length < expr.length && !isIdentifierPart(exprICode(index + to_check.length)))) {\\n index += tc_len;\\n return {\\n type: UNARY_EXP,\\n operator: to_check,\\n argument: gobbleToken(),\\n prefix: true\\n };\\n }\\n to_check = to_check.substr(0, --tc_len);\\n }\\n if (isIdentifierStart(ch) || ch === OPAREN_CODE) {\\n return gobbleVariable();\\n }\\n }\\n return false;\\n }, gobbleNumericLiteral = function() {\\n var number = \\\\\\\"\\\\\\\", ch, chCode;\\n while (isDecimalDigit(exprICode(index))) {\\n number += exprI(index++);\\n }\\n if (exprICode(index) === PERIOD_CODE) {\\n number += exprI(index++);\\n while (isDecimalDigit(exprICode(index))) {\\n number += exprI(index++);\\n }\\n }\\n ch = exprI(index);\\n if (ch === \\\\\\\"e\\\\\\\" || ch === \\\\\\\"E\\\\\\\") {\\n number += exprI(index++);\\n ch = exprI(index);\\n if (ch === \\\\\\\"+\\\\\\\" || ch === \\\\\\\"-\\\\\\\") {\\n number += exprI(index++);\\n }\\n while (isDecimalDigit(exprICode(index))) {\\n number += exprI(index++);\\n }\\n if (!isDecimalDigit(exprICode(index - 1))) {\\n throwError(\\\\\\\"Expected exponent (\\\\\\\" + number + exprI(index) + \\\\\\\")\\\\\\\", index);\\n }\\n }\\n chCode = exprICode(index);\\n if (isIdentifierStart(chCode)) {\\n throwError(\\\\\\\"Variable names cannot start with a number (\\\\\\\" + number + exprI(index) + \\\\\\\")\\\\\\\", index);\\n } else if (chCode === PERIOD_CODE) {\\n throwError(\\\\\\\"Unexpected period\\\\\\\", index);\\n }\\n return {\\n type: LITERAL,\\n value: parseFloat(number),\\n raw: number\\n };\\n }, gobbleStringLiteral = function() {\\n var str = \\\\\\\"\\\\\\\", quote = exprI(index++), closed = false, ch;\\n while (index < length) {\\n ch = exprI(index++);\\n if (ch === quote) {\\n closed = true;\\n break;\\n } else if (ch === \\\\\\\"\\\\\\\\\\\\\\\") {\\n ch = exprI(index++);\\n switch (ch) {\\n case \\\\\\\"n\\\\\\\":\\n str += \\\\\\\"\\\\n\\\\\\\";\\n break;\\n case \\\\\\\"r\\\\\\\":\\n str += \\\\\\\"\\\\r\\\\\\\";\\n break;\\n case \\\\\\\"t\\\\\\\":\\n str += \\\\\\\"\\t\\\\\\\";\\n break;\\n case \\\\\\\"b\\\\\\\":\\n str += \\\\\\\"\\\\b\\\\\\\";\\n break;\\n case \\\\\\\"f\\\\\\\":\\n str += \\\\\\\"\\\\f\\\\\\\";\\n break;\\n case \\\\\\\"v\\\\\\\":\\n str += \\\\\\\"\\\\v\\\\\\\";\\n break;\\n default:\\n str += ch;\\n }\\n } else {\\n str += ch;\\n }\\n }\\n if (!closed) {\\n throwError('Unclosed quote after \\\\\\\"' + str + '\\\\\\\"', index);\\n }\\n return {\\n type: LITERAL,\\n value: str,\\n raw: quote + str + quote\\n };\\n }, gobbleIdentifier = function() {\\n var ch = exprICode(index), start = index, identifier;\\n if (isIdentifierStart(ch)) {\\n index++;\\n } else {\\n throwError(\\\\\\\"Unexpected \\\\\\\" + exprI(index), index);\\n }\\n while (index < length) {\\n ch = exprICode(index);\\n if (isIdentifierPart(ch)) {\\n index++;\\n } else {\\n break;\\n }\\n }\\n identifier = expr.slice(start, index);\\n if (literals.hasOwnProperty(identifier)) {\\n return {\\n type: LITERAL,\\n value: literals[identifier],\\n raw: identifier\\n };\\n } else if (identifier === this_str) {\\n return {type: THIS_EXP};\\n } else {\\n return {\\n type: IDENTIFIER,\\n name: identifier\\n };\\n }\\n }, gobbleArguments = function(termination) {\\n var ch_i2, args = [], node2, closed = false;\\n var separator_count = 0;\\n while (index < length) {\\n gobbleSpaces();\\n ch_i2 = exprICode(index);\\n if (ch_i2 === termination) {\\n closed = true;\\n index++;\\n if (termination === CPAREN_CODE && separator_count && separator_count >= args.length) {\\n throwError(\\\\\\\"Unexpected token \\\\\\\" + String.fromCharCode(termination), index);\\n }\\n break;\\n } else if (ch_i2 === COMMA_CODE) {\\n index++;\\n separator_count++;\\n if (separator_count !== args.length) {\\n if (termination === CPAREN_CODE) {\\n throwError(\\\\\\\"Unexpected token ,\\\\\\\", index);\\n } else if (termination === CBRACK_CODE) {\\n for (var arg = args.length; arg < separator_count; arg++) {\\n args.push(null);\\n }\\n }\\n }\\n } else {\\n node2 = gobbleExpression();\\n if (!node2 || node2.type === COMPOUND) {\\n throwError(\\\\\\\"Expected comma\\\\\\\", index);\\n }\\n args.push(node2);\\n }\\n }\\n if (!closed) {\\n throwError(\\\\\\\"Expected \\\\\\\" + String.fromCharCode(termination), index);\\n }\\n return args;\\n }, gobbleVariable = function() {\\n var ch_i2, node2;\\n ch_i2 = exprICode(index);\\n if (ch_i2 === OPAREN_CODE) {\\n node2 = gobbleGroup();\\n } else {\\n node2 = gobbleIdentifier();\\n }\\n gobbleSpaces();\\n ch_i2 = exprICode(index);\\n while (ch_i2 === PERIOD_CODE || ch_i2 === OBRACK_CODE || ch_i2 === OPAREN_CODE) {\\n index++;\\n if (ch_i2 === PERIOD_CODE) {\\n gobbleSpaces();\\n node2 = {\\n type: MEMBER_EXP,\\n computed: false,\\n object: node2,\\n property: gobbleIdentifier()\\n };\\n } else if (ch_i2 === OBRACK_CODE) {\\n node2 = {\\n type: MEMBER_EXP,\\n computed: true,\\n object: node2,\\n property: gobbleExpression()\\n };\\n gobbleSpaces();\\n ch_i2 = exprICode(index);\\n if (ch_i2 !== CBRACK_CODE) {\\n throwError(\\\\\\\"Unclosed [\\\\\\\", index);\\n }\\n index++;\\n } else if (ch_i2 === OPAREN_CODE) {\\n node2 = {\\n type: CALL_EXP,\\n arguments: gobbleArguments(CPAREN_CODE),\\n callee: node2\\n };\\n }\\n gobbleSpaces();\\n ch_i2 = exprICode(index);\\n }\\n return node2;\\n }, gobbleGroup = function() {\\n index++;\\n var node2 = gobbleExpression();\\n gobbleSpaces();\\n if (exprICode(index) === CPAREN_CODE) {\\n index++;\\n return node2;\\n } else {\\n throwError(\\\\\\\"Unclosed (\\\\\\\", index);\\n }\\n }, gobbleArray = function() {\\n index++;\\n return {\\n type: ARRAY_EXP,\\n elements: gobbleArguments(CBRACK_CODE)\\n };\\n }, nodes = [], ch_i, node;\\n while (index < length) {\\n ch_i = exprICode(index);\\n if (ch_i === SEMCOL_CODE || ch_i === COMMA_CODE) {\\n index++;\\n } else {\\n if (node = gobbleExpression()) {\\n nodes.push(node);\\n } else if (index < length) {\\n throwError('Unexpected \\\\\\\"' + exprI(index) + '\\\\\\\"', index);\\n }\\n }\\n }\\n if (nodes.length === 1) {\\n return nodes[0];\\n } else {\\n return {\\n type: COMPOUND,\\n body: nodes\\n };\\n }\\n };\\n jsep2.version = \\\\\\\"0.3.5\\\\\\\";\\n jsep2.toString = function() {\\n return \\\\\\\"JavaScript Expression Parser (JSEP) v\\\\\\\" + jsep2.version;\\n };\\n jsep2.addUnaryOp = function(op_name) {\\n max_unop_len = Math.max(op_name.length, max_unop_len);\\n unary_ops[op_name] = t;\\n return this;\\n };\\n jsep2.addBinaryOp = function(op_name, precedence2) {\\n max_binop_len = Math.max(op_name.length, max_binop_len);\\n binary_ops[op_name] = precedence2;\\n return this;\\n };\\n jsep2.addLiteral = function(literal_name, literal_value) {\\n literals[literal_name] = literal_value;\\n return this;\\n };\\n jsep2.removeUnaryOp = function(op_name) {\\n delete unary_ops[op_name];\\n if (op_name.length === max_unop_len) {\\n max_unop_len = getMaxKeyLen(unary_ops);\\n }\\n return this;\\n };\\n jsep2.removeAllUnaryOps = function() {\\n unary_ops = {};\\n max_unop_len = 0;\\n return this;\\n };\\n jsep2.removeBinaryOp = function(op_name) {\\n delete binary_ops[op_name];\\n if (op_name.length === max_binop_len) {\\n max_binop_len = getMaxKeyLen(binary_ops);\\n }\\n return this;\\n };\\n jsep2.removeAllBinaryOps = function() {\\n binary_ops = {};\\n max_binop_len = 0;\\n return this;\\n };\\n jsep2.removeLiteral = function(literal_name) {\\n delete literals[literal_name];\\n return this;\\n };\\n jsep2.removeAllLiterals = function() {\\n literals = {};\\n return this;\\n };\\n if (typeof exports === \\\\\\\"undefined\\\\\\\") {\\n var old_jsep = root.jsep;\\n root.jsep = jsep2;\\n jsep2.noConflict = function() {\\n if (root.jsep === jsep2) {\\n root.jsep = old_jsep;\\n }\\n return jsep2;\\n };\\n } else {\\n if (typeof module !== \\\\\\\"undefined\\\\\\\" && module.exports) {\\n exports = module.exports = jsep2;\\n } else {\\n exports.parse = jsep2;\\n }\\n }\\n })(exports);\\n});\\n\\n// ../../../node_modules/three/src/math/MathUtils.js\\nvar DEG2RAD = Math.PI / 180;\\nvar RAD2DEG = 180 / Math.PI;\\nvar _lut = [];\\nfor (let i = 0; i < 256; i++) {\\n _lut[i] = (i < 16 ? \\\\\\\"0\\\\\\\" : \\\\\\\"\\\\\\\") + i.toString(16);\\n}\\nvar hasRandomUUID = typeof crypto !== \\\\\\\"undefined\\\\\\\" && \\\\\\\"randomUUID\\\\\\\" in crypto;\\nfunction generateUUID() {\\n if (hasRandomUUID) {\\n return crypto.randomUUID().toUpperCase();\\n }\\n const d0 = Math.random() * 4294967295 | 0;\\n const d1 = Math.random() * 4294967295 | 0;\\n const d2 = Math.random() * 4294967295 | 0;\\n const d3 = Math.random() * 4294967295 | 0;\\n const uuid = _lut[d0 & 255] + _lut[d0 >> 8 & 255] + _lut[d0 >> 16 & 255] + _lut[d0 >> 24 & 255] + \\\\\\\"-\\\\\\\" + _lut[d1 & 255] + _lut[d1 >> 8 & 255] + \\\\\\\"-\\\\\\\" + _lut[d1 >> 16 & 15 | 64] + _lut[d1 >> 24 & 255] + \\\\\\\"-\\\\\\\" + _lut[d2 & 63 | 128] + _lut[d2 >> 8 & 255] + \\\\\\\"-\\\\\\\" + _lut[d2 >> 16 & 255] + _lut[d2 >> 24 & 255] + _lut[d3 & 255] + _lut[d3 >> 8 & 255] + _lut[d3 >> 16 & 255] + _lut[d3 >> 24 & 255];\\n return uuid.toUpperCase();\\n}\\nfunction clamp(value, min, max) {\\n return Math.max(min, Math.min(max, value));\\n}\\nfunction euclideanModulo(n, m) {\\n return (n % m + m) % m;\\n}\\nfunction lerp(x, y, t) {\\n return (1 - t) * x + t * y;\\n}\\nfunction degToRad(degrees) {\\n return degrees * DEG2RAD;\\n}\\nfunction isPowerOfTwo(value) {\\n return (value & value - 1) === 0 && value !== 0;\\n}\\nfunction floorPowerOfTwo(value) {\\n return Math.pow(2, Math.floor(Math.log(value) / Math.LN2));\\n}\\n\\n// ../../../node_modules/three/src/math/Quaternion.js\\nvar Quaternion = class {\\n constructor(x = 0, y = 0, z = 0, w = 1) {\\n this._x = x;\\n this._y = y;\\n this._z = z;\\n this._w = w;\\n }\\n static slerp(qa, qb, qm, t) {\\n console.warn(\\\\\\\"THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead.\\\\\\\");\\n return qm.slerpQuaternions(qa, qb, t);\\n }\\n static slerpFlat(dst, dstOffset, src0, srcOffset0, src1, srcOffset1, t) {\\n let x0 = src0[srcOffset0 + 0], y0 = src0[srcOffset0 + 1], z0 = src0[srcOffset0 + 2], w0 = src0[srcOffset0 + 3];\\n const x1 = src1[srcOffset1 + 0], y1 = src1[srcOffset1 + 1], z1 = src1[srcOffset1 + 2], w1 = src1[srcOffset1 + 3];\\n if (t === 0) {\\n dst[dstOffset + 0] = x0;\\n dst[dstOffset + 1] = y0;\\n dst[dstOffset + 2] = z0;\\n dst[dstOffset + 3] = w0;\\n return;\\n }\\n if (t === 1) {\\n dst[dstOffset + 0] = x1;\\n dst[dstOffset + 1] = y1;\\n dst[dstOffset + 2] = z1;\\n dst[dstOffset + 3] = w1;\\n return;\\n }\\n if (w0 !== w1 || x0 !== x1 || y0 !== y1 || z0 !== z1) {\\n let s = 1 - t;\\n const cos = x0 * x1 + y0 * y1 + z0 * z1 + w0 * w1, dir = cos >= 0 ? 1 : -1, sqrSin = 1 - cos * cos;\\n if (sqrSin > Number.EPSILON) {\\n const sin = Math.sqrt(sqrSin), len = Math.atan2(sin, cos * dir);\\n s = Math.sin(s * len) / sin;\\n t = Math.sin(t * len) / sin;\\n }\\n const tDir = t * dir;\\n x0 = x0 * s + x1 * tDir;\\n y0 = y0 * s + y1 * tDir;\\n z0 = z0 * s + z1 * tDir;\\n w0 = w0 * s + w1 * tDir;\\n if (s === 1 - t) {\\n const f = 1 / Math.sqrt(x0 * x0 + y0 * y0 + z0 * z0 + w0 * w0);\\n x0 *= f;\\n y0 *= f;\\n z0 *= f;\\n w0 *= f;\\n }\\n }\\n dst[dstOffset] = x0;\\n dst[dstOffset + 1] = y0;\\n dst[dstOffset + 2] = z0;\\n dst[dstOffset + 3] = w0;\\n }\\n static multiplyQuaternionsFlat(dst, dstOffset, src0, srcOffset0, src1, srcOffset1) {\\n const x0 = src0[srcOffset0];\\n const y0 = src0[srcOffset0 + 1];\\n const z0 = src0[srcOffset0 + 2];\\n const w0 = src0[srcOffset0 + 3];\\n const x1 = src1[srcOffset1];\\n const y1 = src1[srcOffset1 + 1];\\n const z1 = src1[srcOffset1 + 2];\\n const w1 = src1[srcOffset1 + 3];\\n dst[dstOffset] = x0 * w1 + w0 * x1 + y0 * z1 - z0 * y1;\\n dst[dstOffset + 1] = y0 * w1 + w0 * y1 + z0 * x1 - x0 * z1;\\n dst[dstOffset + 2] = z0 * w1 + w0 * z1 + x0 * y1 - y0 * x1;\\n dst[dstOffset + 3] = w0 * w1 - x0 * x1 - y0 * y1 - z0 * z1;\\n return dst;\\n }\\n get x() {\\n return this._x;\\n }\\n set x(value) {\\n this._x = value;\\n this._onChangeCallback();\\n }\\n get y() {\\n return this._y;\\n }\\n set y(value) {\\n this._y = value;\\n this._onChangeCallback();\\n }\\n get z() {\\n return this._z;\\n }\\n set z(value) {\\n this._z = value;\\n this._onChangeCallback();\\n }\\n get w() {\\n return this._w;\\n }\\n set w(value) {\\n this._w = value;\\n this._onChangeCallback();\\n }\\n set(x, y, z, w) {\\n this._x = x;\\n this._y = y;\\n this._z = z;\\n this._w = w;\\n this._onChangeCallback();\\n return this;\\n }\\n clone() {\\n return new this.constructor(this._x, this._y, this._z, this._w);\\n }\\n copy(quaternion) {\\n this._x = quaternion.x;\\n this._y = quaternion.y;\\n this._z = quaternion.z;\\n this._w = quaternion.w;\\n this._onChangeCallback();\\n return this;\\n }\\n setFromEuler(euler, update) {\\n if (!(euler && euler.isEuler)) {\\n throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");\\n }\\n const x = euler._x, y = euler._y, z = euler._z, order = euler._order;\\n const cos = Math.cos;\\n const sin = Math.sin;\\n const c1 = cos(x / 2);\\n const c2 = cos(y / 2);\\n const c3 = cos(z / 2);\\n const s1 = sin(x / 2);\\n const s2 = sin(y / 2);\\n const s3 = sin(z / 2);\\n switch (order) {\\n case \\\\\\\"XYZ\\\\\\\":\\n this._x = s1 * c2 * c3 + c1 * s2 * s3;\\n this._y = c1 * s2 * c3 - s1 * c2 * s3;\\n this._z = c1 * c2 * s3 + s1 * s2 * c3;\\n this._w = c1 * c2 * c3 - s1 * s2 * s3;\\n break;\\n case \\\\\\\"YXZ\\\\\\\":\\n this._x = s1 * c2 * c3 + c1 * s2 * s3;\\n this._y = c1 * s2 * c3 - s1 * c2 * s3;\\n this._z = c1 * c2 * s3 - s1 * s2 * c3;\\n this._w = c1 * c2 * c3 + s1 * s2 * s3;\\n break;\\n case \\\\\\\"ZXY\\\\\\\":\\n this._x = s1 * c2 * c3 - c1 * s2 * s3;\\n this._y = c1 * s2 * c3 + s1 * c2 * s3;\\n this._z = c1 * c2 * s3 + s1 * s2 * c3;\\n this._w = c1 * c2 * c3 - s1 * s2 * s3;\\n break;\\n case \\\\\\\"ZYX\\\\\\\":\\n this._x = s1 * c2 * c3 - c1 * s2 * s3;\\n this._y = c1 * s2 * c3 + s1 * c2 * s3;\\n this._z = c1 * c2 * s3 - s1 * s2 * c3;\\n this._w = c1 * c2 * c3 + s1 * s2 * s3;\\n break;\\n case \\\\\\\"YZX\\\\\\\":\\n this._x = s1 * c2 * c3 + c1 * s2 * s3;\\n this._y = c1 * s2 * c3 + s1 * c2 * s3;\\n this._z = c1 * c2 * s3 - s1 * s2 * c3;\\n this._w = c1 * c2 * c3 - s1 * s2 * s3;\\n break;\\n case \\\\\\\"XZY\\\\\\\":\\n this._x = s1 * c2 * c3 - c1 * s2 * s3;\\n this._y = c1 * s2 * c3 - s1 * c2 * s3;\\n this._z = c1 * c2 * s3 + s1 * s2 * c3;\\n this._w = c1 * c2 * c3 + s1 * s2 * s3;\\n break;\\n default:\\n console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\" + order);\\n }\\n if (update !== false)\\n this._onChangeCallback();\\n return this;\\n }\\n setFromAxisAngle(axis, angle) {\\n const halfAngle = angle / 2, s = Math.sin(halfAngle);\\n this._x = axis.x * s;\\n this._y = axis.y * s;\\n this._z = axis.z * s;\\n this._w = Math.cos(halfAngle);\\n this._onChangeCallback();\\n return this;\\n }\\n setFromRotationMatrix(m) {\\n const te = m.elements, m11 = te[0], m12 = te[4], m13 = te[8], m21 = te[1], m22 = te[5], m23 = te[9], m31 = te[2], m32 = te[6], m33 = te[10], trace = m11 + m22 + m33;\\n if (trace > 0) {\\n const s = 0.5 / Math.sqrt(trace + 1);\\n this._w = 0.25 / s;\\n this._x = (m32 - m23) * s;\\n this._y = (m13 - m31) * s;\\n this._z = (m21 - m12) * s;\\n } else if (m11 > m22 && m11 > m33) {\\n const s = 2 * Math.sqrt(1 + m11 - m22 - m33);\\n this._w = (m32 - m23) / s;\\n this._x = 0.25 * s;\\n this._y = (m12 + m21) / s;\\n this._z = (m13 + m31) / s;\\n } else if (m22 > m33) {\\n const s = 2 * Math.sqrt(1 + m22 - m11 - m33);\\n this._w = (m13 - m31) / s;\\n this._x = (m12 + m21) / s;\\n this._y = 0.25 * s;\\n this._z = (m23 + m32) / s;\\n } else {\\n const s = 2 * Math.sqrt(1 + m33 - m11 - m22);\\n this._w = (m21 - m12) / s;\\n this._x = (m13 + m31) / s;\\n this._y = (m23 + m32) / s;\\n this._z = 0.25 * s;\\n }\\n this._onChangeCallback();\\n return this;\\n }\\n setFromUnitVectors(vFrom, vTo) {\\n let r = vFrom.dot(vTo) + 1;\\n if (r < Number.EPSILON) {\\n r = 0;\\n if (Math.abs(vFrom.x) > Math.abs(vFrom.z)) {\\n this._x = -vFrom.y;\\n this._y = vFrom.x;\\n this._z = 0;\\n this._w = r;\\n } else {\\n this._x = 0;\\n this._y = -vFrom.z;\\n this._z = vFrom.y;\\n this._w = r;\\n }\\n } else {\\n this._x = vFrom.y * vTo.z - vFrom.z * vTo.y;\\n this._y = vFrom.z * vTo.x - vFrom.x * vTo.z;\\n this._z = vFrom.x * vTo.y - vFrom.y * vTo.x;\\n this._w = r;\\n }\\n return this.normalize();\\n }\\n angleTo(q) {\\n return 2 * Math.acos(Math.abs(clamp(this.dot(q), -1, 1)));\\n }\\n rotateTowards(q, step) {\\n const angle = this.angleTo(q);\\n if (angle === 0)\\n return this;\\n const t = Math.min(1, step / angle);\\n this.slerp(q, t);\\n return this;\\n }\\n identity() {\\n return this.set(0, 0, 0, 1);\\n }\\n invert() {\\n return this.conjugate();\\n }\\n conjugate() {\\n this._x *= -1;\\n this._y *= -1;\\n this._z *= -1;\\n this._onChangeCallback();\\n return this;\\n }\\n dot(v) {\\n return this._x * v._x + this._y * v._y + this._z * v._z + this._w * v._w;\\n }\\n lengthSq() {\\n return this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w;\\n }\\n length() {\\n return Math.sqrt(this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w);\\n }\\n normalize() {\\n let l = this.length();\\n if (l === 0) {\\n this._x = 0;\\n this._y = 0;\\n this._z = 0;\\n this._w = 1;\\n } else {\\n l = 1 / l;\\n this._x = this._x * l;\\n this._y = this._y * l;\\n this._z = this._z * l;\\n this._w = this._w * l;\\n }\\n this._onChangeCallback();\\n return this;\\n }\\n multiply(q, p) {\\n if (p !== void 0) {\\n console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\");\\n return this.multiplyQuaternions(q, p);\\n }\\n return this.multiplyQuaternions(this, q);\\n }\\n premultiply(q) {\\n return this.multiplyQuaternions(q, this);\\n }\\n multiplyQuaternions(a, b) {\\n const qax = a._x, qay = a._y, qaz = a._z, qaw = a._w;\\n const qbx = b._x, qby = b._y, qbz = b._z, qbw = b._w;\\n this._x = qax * qbw + qaw * qbx + qay * qbz - qaz * qby;\\n this._y = qay * qbw + qaw * qby + qaz * qbx - qax * qbz;\\n this._z = qaz * qbw + qaw * qbz + qax * qby - qay * qbx;\\n this._w = qaw * qbw - qax * qbx - qay * qby - qaz * qbz;\\n this._onChangeCallback();\\n return this;\\n }\\n slerp(qb, t) {\\n if (t === 0)\\n return this;\\n if (t === 1)\\n return this.copy(qb);\\n const x = this._x, y = this._y, z = this._z, w = this._w;\\n let cosHalfTheta = w * qb._w + x * qb._x + y * qb._y + z * qb._z;\\n if (cosHalfTheta < 0) {\\n this._w = -qb._w;\\n this._x = -qb._x;\\n this._y = -qb._y;\\n this._z = -qb._z;\\n cosHalfTheta = -cosHalfTheta;\\n } else {\\n this.copy(qb);\\n }\\n if (cosHalfTheta >= 1) {\\n this._w = w;\\n this._x = x;\\n this._y = y;\\n this._z = z;\\n return this;\\n }\\n const sqrSinHalfTheta = 1 - cosHalfTheta * cosHalfTheta;\\n if (sqrSinHalfTheta <= Number.EPSILON) {\\n const s = 1 - t;\\n this._w = s * w + t * this._w;\\n this._x = s * x + t * this._x;\\n this._y = s * y + t * this._y;\\n this._z = s * z + t * this._z;\\n this.normalize();\\n this._onChangeCallback();\\n return this;\\n }\\n const sinHalfTheta = Math.sqrt(sqrSinHalfTheta);\\n const halfTheta = Math.atan2(sinHalfTheta, cosHalfTheta);\\n const ratioA = Math.sin((1 - t) * halfTheta) / sinHalfTheta, ratioB = Math.sin(t * halfTheta) / sinHalfTheta;\\n this._w = w * ratioA + this._w * ratioB;\\n this._x = x * ratioA + this._x * ratioB;\\n this._y = y * ratioA + this._y * ratioB;\\n this._z = z * ratioA + this._z * ratioB;\\n this._onChangeCallback();\\n return this;\\n }\\n slerpQuaternions(qa, qb, t) {\\n this.copy(qa).slerp(qb, t);\\n }\\n random() {\\n const u1 = Math.random();\\n const sqrt1u1 = Math.sqrt(1 - u1);\\n const sqrtu1 = Math.sqrt(u1);\\n const u2 = 2 * Math.PI * Math.random();\\n const u3 = 2 * Math.PI * Math.random();\\n return this.set(sqrt1u1 * Math.cos(u2), sqrtu1 * Math.sin(u3), sqrtu1 * Math.cos(u3), sqrt1u1 * Math.sin(u2));\\n }\\n equals(quaternion) {\\n return quaternion._x === this._x && quaternion._y === this._y && quaternion._z === this._z && quaternion._w === this._w;\\n }\\n fromArray(array, offset = 0) {\\n this._x = array[offset];\\n this._y = array[offset + 1];\\n this._z = array[offset + 2];\\n this._w = array[offset + 3];\\n this._onChangeCallback();\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this._x;\\n array[offset + 1] = this._y;\\n array[offset + 2] = this._z;\\n array[offset + 3] = this._w;\\n return array;\\n }\\n fromBufferAttribute(attribute, index) {\\n this._x = attribute.getX(index);\\n this._y = attribute.getY(index);\\n this._z = attribute.getZ(index);\\n this._w = attribute.getW(index);\\n return this;\\n }\\n _onChange(callback) {\\n this._onChangeCallback = callback;\\n return this;\\n }\\n _onChangeCallback() {\\n }\\n};\\nQuaternion.prototype.isQuaternion = true;\\n\\n// ../../../node_modules/three/src/math/Vector3.js\\nvar Vector3 = class {\\n constructor(x = 0, y = 0, z = 0) {\\n this.x = x;\\n this.y = y;\\n this.z = z;\\n }\\n set(x, y, z) {\\n if (z === void 0)\\n z = this.z;\\n this.x = x;\\n this.y = y;\\n this.z = z;\\n return this;\\n }\\n setScalar(scalar) {\\n this.x = scalar;\\n this.y = scalar;\\n this.z = scalar;\\n return this;\\n }\\n setX(x) {\\n this.x = x;\\n return this;\\n }\\n setY(y) {\\n this.y = y;\\n return this;\\n }\\n setZ(z) {\\n this.z = z;\\n return this;\\n }\\n setComponent(index, value) {\\n switch (index) {\\n case 0:\\n this.x = value;\\n break;\\n case 1:\\n this.y = value;\\n break;\\n case 2:\\n this.z = value;\\n break;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n return this;\\n }\\n getComponent(index) {\\n switch (index) {\\n case 0:\\n return this.x;\\n case 1:\\n return this.y;\\n case 2:\\n return this.z;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n }\\n clone() {\\n return new this.constructor(this.x, this.y, this.z);\\n }\\n copy(v) {\\n this.x = v.x;\\n this.y = v.y;\\n this.z = v.z;\\n return this;\\n }\\n add(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\");\\n return this.addVectors(v, w);\\n }\\n this.x += v.x;\\n this.y += v.y;\\n this.z += v.z;\\n return this;\\n }\\n addScalar(s) {\\n this.x += s;\\n this.y += s;\\n this.z += s;\\n return this;\\n }\\n addVectors(a, b) {\\n this.x = a.x + b.x;\\n this.y = a.y + b.y;\\n this.z = a.z + b.z;\\n return this;\\n }\\n addScaledVector(v, s) {\\n this.x += v.x * s;\\n this.y += v.y * s;\\n this.z += v.z * s;\\n return this;\\n }\\n sub(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\");\\n return this.subVectors(v, w);\\n }\\n this.x -= v.x;\\n this.y -= v.y;\\n this.z -= v.z;\\n return this;\\n }\\n subScalar(s) {\\n this.x -= s;\\n this.y -= s;\\n this.z -= s;\\n return this;\\n }\\n subVectors(a, b) {\\n this.x = a.x - b.x;\\n this.y = a.y - b.y;\\n this.z = a.z - b.z;\\n return this;\\n }\\n multiply(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\");\\n return this.multiplyVectors(v, w);\\n }\\n this.x *= v.x;\\n this.y *= v.y;\\n this.z *= v.z;\\n return this;\\n }\\n multiplyScalar(scalar) {\\n this.x *= scalar;\\n this.y *= scalar;\\n this.z *= scalar;\\n return this;\\n }\\n multiplyVectors(a, b) {\\n this.x = a.x * b.x;\\n this.y = a.y * b.y;\\n this.z = a.z * b.z;\\n return this;\\n }\\n applyEuler(euler) {\\n if (!(euler && euler.isEuler)) {\\n console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");\\n }\\n return this.applyQuaternion(_quaternion.setFromEuler(euler));\\n }\\n applyAxisAngle(axis, angle) {\\n return this.applyQuaternion(_quaternion.setFromAxisAngle(axis, angle));\\n }\\n applyMatrix3(m) {\\n const x = this.x, y = this.y, z = this.z;\\n const e = m.elements;\\n this.x = e[0] * x + e[3] * y + e[6] * z;\\n this.y = e[1] * x + e[4] * y + e[7] * z;\\n this.z = e[2] * x + e[5] * y + e[8] * z;\\n return this;\\n }\\n applyNormalMatrix(m) {\\n return this.applyMatrix3(m).normalize();\\n }\\n applyMatrix4(m) {\\n const x = this.x, y = this.y, z = this.z;\\n const e = m.elements;\\n const w = 1 / (e[3] * x + e[7] * y + e[11] * z + e[15]);\\n this.x = (e[0] * x + e[4] * y + e[8] * z + e[12]) * w;\\n this.y = (e[1] * x + e[5] * y + e[9] * z + e[13]) * w;\\n this.z = (e[2] * x + e[6] * y + e[10] * z + e[14]) * w;\\n return this;\\n }\\n applyQuaternion(q) {\\n const x = this.x, y = this.y, z = this.z;\\n const qx = q.x, qy = q.y, qz = q.z, qw = q.w;\\n const ix = qw * x + qy * z - qz * y;\\n const iy = qw * y + qz * x - qx * z;\\n const iz = qw * z + qx * y - qy * x;\\n const iw = -qx * x - qy * y - qz * z;\\n this.x = ix * qw + iw * -qx + iy * -qz - iz * -qy;\\n this.y = iy * qw + iw * -qy + iz * -qx - ix * -qz;\\n this.z = iz * qw + iw * -qz + ix * -qy - iy * -qx;\\n return this;\\n }\\n project(camera) {\\n return this.applyMatrix4(camera.matrixWorldInverse).applyMatrix4(camera.projectionMatrix);\\n }\\n unproject(camera) {\\n return this.applyMatrix4(camera.projectionMatrixInverse).applyMatrix4(camera.matrixWorld);\\n }\\n transformDirection(m) {\\n const x = this.x, y = this.y, z = this.z;\\n const e = m.elements;\\n this.x = e[0] * x + e[4] * y + e[8] * z;\\n this.y = e[1] * x + e[5] * y + e[9] * z;\\n this.z = e[2] * x + e[6] * y + e[10] * z;\\n return this.normalize();\\n }\\n divide(v) {\\n this.x /= v.x;\\n this.y /= v.y;\\n this.z /= v.z;\\n return this;\\n }\\n divideScalar(scalar) {\\n return this.multiplyScalar(1 / scalar);\\n }\\n min(v) {\\n this.x = Math.min(this.x, v.x);\\n this.y = Math.min(this.y, v.y);\\n this.z = Math.min(this.z, v.z);\\n return this;\\n }\\n max(v) {\\n this.x = Math.max(this.x, v.x);\\n this.y = Math.max(this.y, v.y);\\n this.z = Math.max(this.z, v.z);\\n return this;\\n }\\n clamp(min, max) {\\n this.x = Math.max(min.x, Math.min(max.x, this.x));\\n this.y = Math.max(min.y, Math.min(max.y, this.y));\\n this.z = Math.max(min.z, Math.min(max.z, this.z));\\n return this;\\n }\\n clampScalar(minVal, maxVal) {\\n this.x = Math.max(minVal, Math.min(maxVal, this.x));\\n this.y = Math.max(minVal, Math.min(maxVal, this.y));\\n this.z = Math.max(minVal, Math.min(maxVal, this.z));\\n return this;\\n }\\n clampLength(min, max) {\\n const length = this.length();\\n return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max, length)));\\n }\\n floor() {\\n this.x = Math.floor(this.x);\\n this.y = Math.floor(this.y);\\n this.z = Math.floor(this.z);\\n return this;\\n }\\n ceil() {\\n this.x = Math.ceil(this.x);\\n this.y = Math.ceil(this.y);\\n this.z = Math.ceil(this.z);\\n return this;\\n }\\n round() {\\n this.x = Math.round(this.x);\\n this.y = Math.round(this.y);\\n this.z = Math.round(this.z);\\n return this;\\n }\\n roundToZero() {\\n this.x = this.x < 0 ? Math.ceil(this.x) : Math.floor(this.x);\\n this.y = this.y < 0 ? Math.ceil(this.y) : Math.floor(this.y);\\n this.z = this.z < 0 ? Math.ceil(this.z) : Math.floor(this.z);\\n return this;\\n }\\n negate() {\\n this.x = -this.x;\\n this.y = -this.y;\\n this.z = -this.z;\\n return this;\\n }\\n dot(v) {\\n return this.x * v.x + this.y * v.y + this.z * v.z;\\n }\\n lengthSq() {\\n return this.x * this.x + this.y * this.y + this.z * this.z;\\n }\\n length() {\\n return Math.sqrt(this.x * this.x + this.y * this.y + this.z * this.z);\\n }\\n manhattanLength() {\\n return Math.abs(this.x) + Math.abs(this.y) + Math.abs(this.z);\\n }\\n normalize() {\\n return this.divideScalar(this.length() || 1);\\n }\\n setLength(length) {\\n return this.normalize().multiplyScalar(length);\\n }\\n lerp(v, alpha) {\\n this.x += (v.x - this.x) * alpha;\\n this.y += (v.y - this.y) * alpha;\\n this.z += (v.z - this.z) * alpha;\\n return this;\\n }\\n lerpVectors(v1, v2, alpha) {\\n this.x = v1.x + (v2.x - v1.x) * alpha;\\n this.y = v1.y + (v2.y - v1.y) * alpha;\\n this.z = v1.z + (v2.z - v1.z) * alpha;\\n return this;\\n }\\n cross(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\");\\n return this.crossVectors(v, w);\\n }\\n return this.crossVectors(this, v);\\n }\\n crossVectors(a, b) {\\n const ax = a.x, ay = a.y, az = a.z;\\n const bx = b.x, by = b.y, bz = b.z;\\n this.x = ay * bz - az * by;\\n this.y = az * bx - ax * bz;\\n this.z = ax * by - ay * bx;\\n return this;\\n }\\n projectOnVector(v) {\\n const denominator = v.lengthSq();\\n if (denominator === 0)\\n return this.set(0, 0, 0);\\n const scalar = v.dot(this) / denominator;\\n return this.copy(v).multiplyScalar(scalar);\\n }\\n projectOnPlane(planeNormal) {\\n _vector.copy(this).projectOnVector(planeNormal);\\n return this.sub(_vector);\\n }\\n reflect(normal) {\\n return this.sub(_vector.copy(normal).multiplyScalar(2 * this.dot(normal)));\\n }\\n angleTo(v) {\\n const denominator = Math.sqrt(this.lengthSq() * v.lengthSq());\\n if (denominator === 0)\\n return Math.PI / 2;\\n const theta = this.dot(v) / denominator;\\n return Math.acos(clamp(theta, -1, 1));\\n }\\n distanceTo(v) {\\n return Math.sqrt(this.distanceToSquared(v));\\n }\\n distanceToSquared(v) {\\n const dx = this.x - v.x, dy = this.y - v.y, dz = this.z - v.z;\\n return dx * dx + dy * dy + dz * dz;\\n }\\n manhattanDistanceTo(v) {\\n return Math.abs(this.x - v.x) + Math.abs(this.y - v.y) + Math.abs(this.z - v.z);\\n }\\n setFromSpherical(s) {\\n return this.setFromSphericalCoords(s.radius, s.phi, s.theta);\\n }\\n setFromSphericalCoords(radius, phi, theta) {\\n const sinPhiRadius = Math.sin(phi) * radius;\\n this.x = sinPhiRadius * Math.sin(theta);\\n this.y = Math.cos(phi) * radius;\\n this.z = sinPhiRadius * Math.cos(theta);\\n return this;\\n }\\n setFromCylindrical(c) {\\n return this.setFromCylindricalCoords(c.radius, c.theta, c.y);\\n }\\n setFromCylindricalCoords(radius, theta, y) {\\n this.x = radius * Math.sin(theta);\\n this.y = y;\\n this.z = radius * Math.cos(theta);\\n return this;\\n }\\n setFromMatrixPosition(m) {\\n const e = m.elements;\\n this.x = e[12];\\n this.y = e[13];\\n this.z = e[14];\\n return this;\\n }\\n setFromMatrixScale(m) {\\n const sx = this.setFromMatrixColumn(m, 0).length();\\n const sy = this.setFromMatrixColumn(m, 1).length();\\n const sz = this.setFromMatrixColumn(m, 2).length();\\n this.x = sx;\\n this.y = sy;\\n this.z = sz;\\n return this;\\n }\\n setFromMatrixColumn(m, index) {\\n return this.fromArray(m.elements, index * 4);\\n }\\n setFromMatrix3Column(m, index) {\\n return this.fromArray(m.elements, index * 3);\\n }\\n equals(v) {\\n return v.x === this.x && v.y === this.y && v.z === this.z;\\n }\\n fromArray(array, offset = 0) {\\n this.x = array[offset];\\n this.y = array[offset + 1];\\n this.z = array[offset + 2];\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this.x;\\n array[offset + 1] = this.y;\\n array[offset + 2] = this.z;\\n return array;\\n }\\n fromBufferAttribute(attribute, index, offset) {\\n if (offset !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\");\\n }\\n this.x = attribute.getX(index);\\n this.y = attribute.getY(index);\\n this.z = attribute.getZ(index);\\n return this;\\n }\\n random() {\\n this.x = Math.random();\\n this.y = Math.random();\\n this.z = Math.random();\\n return this;\\n }\\n randomDirection() {\\n const u = (Math.random() - 0.5) * 2;\\n const t = Math.random() * Math.PI * 2;\\n const f = Math.sqrt(1 - u ** 2);\\n this.x = f * Math.cos(t);\\n this.y = f * Math.sin(t);\\n this.z = u;\\n return this;\\n }\\n *[Symbol.iterator]() {\\n yield this.x;\\n yield this.y;\\n yield this.z;\\n }\\n};\\nVector3.prototype.isVector3 = true;\\nvar _vector = /* @__PURE__ */ new Vector3();\\nvar _quaternion = /* @__PURE__ */ new Quaternion();\\n\\n// ../../../node_modules/three/src/math/Matrix4.js\\nvar Matrix4 = class {\\n constructor() {\\n this.elements = [\\n 1,\\n 0,\\n 0,\\n 0,\\n 0,\\n 1,\\n 0,\\n 0,\\n 0,\\n 0,\\n 1,\\n 0,\\n 0,\\n 0,\\n 0,\\n 1\\n ];\\n if (arguments.length > 0) {\\n console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\");\\n }\\n }\\n set(n11, n12, n13, n14, n21, n22, n23, n24, n31, n32, n33, n34, n41, n42, n43, n44) {\\n const te = this.elements;\\n te[0] = n11;\\n te[4] = n12;\\n te[8] = n13;\\n te[12] = n14;\\n te[1] = n21;\\n te[5] = n22;\\n te[9] = n23;\\n te[13] = n24;\\n te[2] = n31;\\n te[6] = n32;\\n te[10] = n33;\\n te[14] = n34;\\n te[3] = n41;\\n te[7] = n42;\\n te[11] = n43;\\n te[15] = n44;\\n return this;\\n }\\n identity() {\\n this.set(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1);\\n return this;\\n }\\n clone() {\\n return new Matrix4().fromArray(this.elements);\\n }\\n copy(m) {\\n const te = this.elements;\\n const me = m.elements;\\n te[0] = me[0];\\n te[1] = me[1];\\n te[2] = me[2];\\n te[3] = me[3];\\n te[4] = me[4];\\n te[5] = me[5];\\n te[6] = me[6];\\n te[7] = me[7];\\n te[8] = me[8];\\n te[9] = me[9];\\n te[10] = me[10];\\n te[11] = me[11];\\n te[12] = me[12];\\n te[13] = me[13];\\n te[14] = me[14];\\n te[15] = me[15];\\n return this;\\n }\\n copyPosition(m) {\\n const te = this.elements, me = m.elements;\\n te[12] = me[12];\\n te[13] = me[13];\\n te[14] = me[14];\\n return this;\\n }\\n setFromMatrix3(m) {\\n const me = m.elements;\\n this.set(me[0], me[3], me[6], 0, me[1], me[4], me[7], 0, me[2], me[5], me[8], 0, 0, 0, 0, 1);\\n return this;\\n }\\n extractBasis(xAxis, yAxis, zAxis) {\\n xAxis.setFromMatrixColumn(this, 0);\\n yAxis.setFromMatrixColumn(this, 1);\\n zAxis.setFromMatrixColumn(this, 2);\\n return this;\\n }\\n makeBasis(xAxis, yAxis, zAxis) {\\n this.set(xAxis.x, yAxis.x, zAxis.x, 0, xAxis.y, yAxis.y, zAxis.y, 0, xAxis.z, yAxis.z, zAxis.z, 0, 0, 0, 0, 1);\\n return this;\\n }\\n extractRotation(m) {\\n const te = this.elements;\\n const me = m.elements;\\n const scaleX = 1 / _v1.setFromMatrixColumn(m, 0).length();\\n const scaleY = 1 / _v1.setFromMatrixColumn(m, 1).length();\\n const scaleZ = 1 / _v1.setFromMatrixColumn(m, 2).length();\\n te[0] = me[0] * scaleX;\\n te[1] = me[1] * scaleX;\\n te[2] = me[2] * scaleX;\\n te[3] = 0;\\n te[4] = me[4] * scaleY;\\n te[5] = me[5] * scaleY;\\n te[6] = me[6] * scaleY;\\n te[7] = 0;\\n te[8] = me[8] * scaleZ;\\n te[9] = me[9] * scaleZ;\\n te[10] = me[10] * scaleZ;\\n te[11] = 0;\\n te[12] = 0;\\n te[13] = 0;\\n te[14] = 0;\\n te[15] = 1;\\n return this;\\n }\\n makeRotationFromEuler(euler) {\\n if (!(euler && euler.isEuler)) {\\n console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");\\n }\\n const te = this.elements;\\n const x = euler.x, y = euler.y, z = euler.z;\\n const a = Math.cos(x), b = Math.sin(x);\\n const c = Math.cos(y), d = Math.sin(y);\\n const e = Math.cos(z), f = Math.sin(z);\\n if (euler.order === \\\\\\\"XYZ\\\\\\\") {\\n const ae = a * e, af = a * f, be = b * e, bf = b * f;\\n te[0] = c * e;\\n te[4] = -c * f;\\n te[8] = d;\\n te[1] = af + be * d;\\n te[5] = ae - bf * d;\\n te[9] = -b * c;\\n te[2] = bf - ae * d;\\n te[6] = be + af * d;\\n te[10] = a * c;\\n } else if (euler.order === \\\\\\\"YXZ\\\\\\\") {\\n const ce = c * e, cf = c * f, de = d * e, df = d * f;\\n te[0] = ce + df * b;\\n te[4] = de * b - cf;\\n te[8] = a * d;\\n te[1] = a * f;\\n te[5] = a * e;\\n te[9] = -b;\\n te[2] = cf * b - de;\\n te[6] = df + ce * b;\\n te[10] = a * c;\\n } else if (euler.order === \\\\\\\"ZXY\\\\\\\") {\\n const ce = c * e, cf = c * f, de = d * e, df = d * f;\\n te[0] = ce - df * b;\\n te[4] = -a * f;\\n te[8] = de + cf * b;\\n te[1] = cf + de * b;\\n te[5] = a * e;\\n te[9] = df - ce * b;\\n te[2] = -a * d;\\n te[6] = b;\\n te[10] = a * c;\\n } else if (euler.order === \\\\\\\"ZYX\\\\\\\") {\\n const ae = a * e, af = a * f, be = b * e, bf = b * f;\\n te[0] = c * e;\\n te[4] = be * d - af;\\n te[8] = ae * d + bf;\\n te[1] = c * f;\\n te[5] = bf * d + ae;\\n te[9] = af * d - be;\\n te[2] = -d;\\n te[6] = b * c;\\n te[10] = a * c;\\n } else if (euler.order === \\\\\\\"YZX\\\\\\\") {\\n const ac = a * c, ad = a * d, bc = b * c, bd = b * d;\\n te[0] = c * e;\\n te[4] = bd - ac * f;\\n te[8] = bc * f + ad;\\n te[1] = f;\\n te[5] = a * e;\\n te[9] = -b * e;\\n te[2] = -d * e;\\n te[6] = ad * f + bc;\\n te[10] = ac - bd * f;\\n } else if (euler.order === \\\\\\\"XZY\\\\\\\") {\\n const ac = a * c, ad = a * d, bc = b * c, bd = b * d;\\n te[0] = c * e;\\n te[4] = -f;\\n te[8] = d * e;\\n te[1] = ac * f + bd;\\n te[5] = a * e;\\n te[9] = ad * f - bc;\\n te[2] = bc * f - ad;\\n te[6] = b * e;\\n te[10] = bd * f + ac;\\n }\\n te[3] = 0;\\n te[7] = 0;\\n te[11] = 0;\\n te[12] = 0;\\n te[13] = 0;\\n te[14] = 0;\\n te[15] = 1;\\n return this;\\n }\\n makeRotationFromQuaternion(q) {\\n return this.compose(_zero, q, _one);\\n }\\n lookAt(eye, target, up) {\\n const te = this.elements;\\n _z.subVectors(eye, target);\\n if (_z.lengthSq() === 0) {\\n _z.z = 1;\\n }\\n _z.normalize();\\n _x.crossVectors(up, _z);\\n if (_x.lengthSq() === 0) {\\n if (Math.abs(up.z) === 1) {\\n _z.x += 1e-4;\\n } else {\\n _z.z += 1e-4;\\n }\\n _z.normalize();\\n _x.crossVectors(up, _z);\\n }\\n _x.normalize();\\n _y.crossVectors(_z, _x);\\n te[0] = _x.x;\\n te[4] = _y.x;\\n te[8] = _z.x;\\n te[1] = _x.y;\\n te[5] = _y.y;\\n te[9] = _z.y;\\n te[2] = _x.z;\\n te[6] = _y.z;\\n te[10] = _z.z;\\n return this;\\n }\\n multiply(m, n) {\\n if (n !== void 0) {\\n console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\");\\n return this.multiplyMatrices(m, n);\\n }\\n return this.multiplyMatrices(this, m);\\n }\\n premultiply(m) {\\n return this.multiplyMatrices(m, this);\\n }\\n multiplyMatrices(a, b) {\\n const ae = a.elements;\\n const be = b.elements;\\n const te = this.elements;\\n const a11 = ae[0], a12 = ae[4], a13 = ae[8], a14 = ae[12];\\n const a21 = ae[1], a22 = ae[5], a23 = ae[9], a24 = ae[13];\\n const a31 = ae[2], a32 = ae[6], a33 = ae[10], a34 = ae[14];\\n const a41 = ae[3], a42 = ae[7], a43 = ae[11], a44 = ae[15];\\n const b11 = be[0], b12 = be[4], b13 = be[8], b14 = be[12];\\n const b21 = be[1], b22 = be[5], b23 = be[9], b24 = be[13];\\n const b31 = be[2], b32 = be[6], b33 = be[10], b34 = be[14];\\n const b41 = be[3], b42 = be[7], b43 = be[11], b44 = be[15];\\n te[0] = a11 * b11 + a12 * b21 + a13 * b31 + a14 * b41;\\n te[4] = a11 * b12 + a12 * b22 + a13 * b32 + a14 * b42;\\n te[8] = a11 * b13 + a12 * b23 + a13 * b33 + a14 * b43;\\n te[12] = a11 * b14 + a12 * b24 + a13 * b34 + a14 * b44;\\n te[1] = a21 * b11 + a22 * b21 + a23 * b31 + a24 * b41;\\n te[5] = a21 * b12 + a22 * b22 + a23 * b32 + a24 * b42;\\n te[9] = a21 * b13 + a22 * b23 + a23 * b33 + a24 * b43;\\n te[13] = a21 * b14 + a22 * b24 + a23 * b34 + a24 * b44;\\n te[2] = a31 * b11 + a32 * b21 + a33 * b31 + a34 * b41;\\n te[6] = a31 * b12 + a32 * b22 + a33 * b32 + a34 * b42;\\n te[10] = a31 * b13 + a32 * b23 + a33 * b33 + a34 * b43;\\n te[14] = a31 * b14 + a32 * b24 + a33 * b34 + a34 * b44;\\n te[3] = a41 * b11 + a42 * b21 + a43 * b31 + a44 * b41;\\n te[7] = a41 * b12 + a42 * b22 + a43 * b32 + a44 * b42;\\n te[11] = a41 * b13 + a42 * b23 + a43 * b33 + a44 * b43;\\n te[15] = a41 * b14 + a42 * b24 + a43 * b34 + a44 * b44;\\n return this;\\n }\\n multiplyScalar(s) {\\n const te = this.elements;\\n te[0] *= s;\\n te[4] *= s;\\n te[8] *= s;\\n te[12] *= s;\\n te[1] *= s;\\n te[5] *= s;\\n te[9] *= s;\\n te[13] *= s;\\n te[2] *= s;\\n te[6] *= s;\\n te[10] *= s;\\n te[14] *= s;\\n te[3] *= s;\\n te[7] *= s;\\n te[11] *= s;\\n te[15] *= s;\\n return this;\\n }\\n determinant() {\\n const te = this.elements;\\n const n11 = te[0], n12 = te[4], n13 = te[8], n14 = te[12];\\n const n21 = te[1], n22 = te[5], n23 = te[9], n24 = te[13];\\n const n31 = te[2], n32 = te[6], n33 = te[10], n34 = te[14];\\n const n41 = te[3], n42 = te[7], n43 = te[11], n44 = te[15];\\n return n41 * (+n14 * n23 * n32 - n13 * n24 * n32 - n14 * n22 * n33 + n12 * n24 * n33 + n13 * n22 * n34 - n12 * n23 * n34) + n42 * (+n11 * n23 * n34 - n11 * n24 * n33 + n14 * n21 * n33 - n13 * n21 * n34 + n13 * n24 * n31 - n14 * n23 * n31) + n43 * (+n11 * n24 * n32 - n11 * n22 * n34 - n14 * n21 * n32 + n12 * n21 * n34 + n14 * n22 * n31 - n12 * n24 * n31) + n44 * (-n13 * n22 * n31 - n11 * n23 * n32 + n11 * n22 * n33 + n13 * n21 * n32 - n12 * n21 * n33 + n12 * n23 * n31);\\n }\\n transpose() {\\n const te = this.elements;\\n let tmp;\\n tmp = te[1];\\n te[1] = te[4];\\n te[4] = tmp;\\n tmp = te[2];\\n te[2] = te[8];\\n te[8] = tmp;\\n tmp = te[6];\\n te[6] = te[9];\\n te[9] = tmp;\\n tmp = te[3];\\n te[3] = te[12];\\n te[12] = tmp;\\n tmp = te[7];\\n te[7] = te[13];\\n te[13] = tmp;\\n tmp = te[11];\\n te[11] = te[14];\\n te[14] = tmp;\\n return this;\\n }\\n setPosition(x, y, z) {\\n const te = this.elements;\\n if (x.isVector3) {\\n te[12] = x.x;\\n te[13] = x.y;\\n te[14] = x.z;\\n } else {\\n te[12] = x;\\n te[13] = y;\\n te[14] = z;\\n }\\n return this;\\n }\\n invert() {\\n const te = this.elements, n11 = te[0], n21 = te[1], n31 = te[2], n41 = te[3], n12 = te[4], n22 = te[5], n32 = te[6], n42 = te[7], n13 = te[8], n23 = te[9], n33 = te[10], n43 = te[11], n14 = te[12], n24 = te[13], n34 = te[14], n44 = te[15], t11 = n23 * n34 * n42 - n24 * n33 * n42 + n24 * n32 * n43 - n22 * n34 * n43 - n23 * n32 * n44 + n22 * n33 * n44, t12 = n14 * n33 * n42 - n13 * n34 * n42 - n14 * n32 * n43 + n12 * n34 * n43 + n13 * n32 * n44 - n12 * n33 * n44, t13 = n13 * n24 * n42 - n14 * n23 * n42 + n14 * n22 * n43 - n12 * n24 * n43 - n13 * n22 * n44 + n12 * n23 * n44, t14 = n14 * n23 * n32 - n13 * n24 * n32 - n14 * n22 * n33 + n12 * n24 * n33 + n13 * n22 * n34 - n12 * n23 * n34;\\n const det = n11 * t11 + n21 * t12 + n31 * t13 + n41 * t14;\\n if (det === 0)\\n return this.set(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0);\\n const detInv = 1 / det;\\n te[0] = t11 * detInv;\\n te[1] = (n24 * n33 * n41 - n23 * n34 * n41 - n24 * n31 * n43 + n21 * n34 * n43 + n23 * n31 * n44 - n21 * n33 * n44) * detInv;\\n te[2] = (n22 * n34 * n41 - n24 * n32 * n41 + n24 * n31 * n42 - n21 * n34 * n42 - n22 * n31 * n44 + n21 * n32 * n44) * detInv;\\n te[3] = (n23 * n32 * n41 - n22 * n33 * n41 - n23 * n31 * n42 + n21 * n33 * n42 + n22 * n31 * n43 - n21 * n32 * n43) * detInv;\\n te[4] = t12 * detInv;\\n te[5] = (n13 * n34 * n41 - n14 * n33 * n41 + n14 * n31 * n43 - n11 * n34 * n43 - n13 * n31 * n44 + n11 * n33 * n44) * detInv;\\n te[6] = (n14 * n32 * n41 - n12 * n34 * n41 - n14 * n31 * n42 + n11 * n34 * n42 + n12 * n31 * n44 - n11 * n32 * n44) * detInv;\\n te[7] = (n12 * n33 * n41 - n13 * n32 * n41 + n13 * n31 * n42 - n11 * n33 * n42 - n12 * n31 * n43 + n11 * n32 * n43) * detInv;\\n te[8] = t13 * detInv;\\n te[9] = (n14 * n23 * n41 - n13 * n24 * n41 - n14 * n21 * n43 + n11 * n24 * n43 + n13 * n21 * n44 - n11 * n23 * n44) * detInv;\\n te[10] = (n12 * n24 * n41 - n14 * n22 * n41 + n14 * n21 * n42 - n11 * n24 * n42 - n12 * n21 * n44 + n11 * n22 * n44) * detInv;\\n te[11] = (n13 * n22 * n41 - n12 * n23 * n41 - n13 * n21 * n42 + n11 * n23 * n42 + n12 * n21 * n43 - n11 * n22 * n43) * detInv;\\n te[12] = t14 * detInv;\\n te[13] = (n13 * n24 * n31 - n14 * n23 * n31 + n14 * n21 * n33 - n11 * n24 * n33 - n13 * n21 * n34 + n11 * n23 * n34) * detInv;\\n te[14] = (n14 * n22 * n31 - n12 * n24 * n31 - n14 * n21 * n32 + n11 * n24 * n32 + n12 * n21 * n34 - n11 * n22 * n34) * detInv;\\n te[15] = (n12 * n23 * n31 - n13 * n22 * n31 + n13 * n21 * n32 - n11 * n23 * n32 - n12 * n21 * n33 + n11 * n22 * n33) * detInv;\\n return this;\\n }\\n scale(v) {\\n const te = this.elements;\\n const x = v.x, y = v.y, z = v.z;\\n te[0] *= x;\\n te[4] *= y;\\n te[8] *= z;\\n te[1] *= x;\\n te[5] *= y;\\n te[9] *= z;\\n te[2] *= x;\\n te[6] *= y;\\n te[10] *= z;\\n te[3] *= x;\\n te[7] *= y;\\n te[11] *= z;\\n return this;\\n }\\n getMaxScaleOnAxis() {\\n const te = this.elements;\\n const scaleXSq = te[0] * te[0] + te[1] * te[1] + te[2] * te[2];\\n const scaleYSq = te[4] * te[4] + te[5] * te[5] + te[6] * te[6];\\n const scaleZSq = te[8] * te[8] + te[9] * te[9] + te[10] * te[10];\\n return Math.sqrt(Math.max(scaleXSq, scaleYSq, scaleZSq));\\n }\\n makeTranslation(x, y, z) {\\n this.set(1, 0, 0, x, 0, 1, 0, y, 0, 0, 1, z, 0, 0, 0, 1);\\n return this;\\n }\\n makeRotationX(theta) {\\n const c = Math.cos(theta), s = Math.sin(theta);\\n this.set(1, 0, 0, 0, 0, c, -s, 0, 0, s, c, 0, 0, 0, 0, 1);\\n return this;\\n }\\n makeRotationY(theta) {\\n const c = Math.cos(theta), s = Math.sin(theta);\\n this.set(c, 0, s, 0, 0, 1, 0, 0, -s, 0, c, 0, 0, 0, 0, 1);\\n return this;\\n }\\n makeRotationZ(theta) {\\n const c = Math.cos(theta), s = Math.sin(theta);\\n this.set(c, -s, 0, 0, s, c, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1);\\n return this;\\n }\\n makeRotationAxis(axis, angle) {\\n const c = Math.cos(angle);\\n const s = Math.sin(angle);\\n const t = 1 - c;\\n const x = axis.x, y = axis.y, z = axis.z;\\n const tx = t * x, ty = t * y;\\n this.set(tx * x + c, tx * y - s * z, tx * z + s * y, 0, tx * y + s * z, ty * y + c, ty * z - s * x, 0, tx * z - s * y, ty * z + s * x, t * z * z + c, 0, 0, 0, 0, 1);\\n return this;\\n }\\n makeScale(x, y, z) {\\n this.set(x, 0, 0, 0, 0, y, 0, 0, 0, 0, z, 0, 0, 0, 0, 1);\\n return this;\\n }\\n makeShear(xy, xz, yx, yz, zx, zy) {\\n this.set(1, yx, zx, 0, xy, 1, zy, 0, xz, yz, 1, 0, 0, 0, 0, 1);\\n return this;\\n }\\n compose(position, quaternion, scale) {\\n const te = this.elements;\\n const x = quaternion._x, y = quaternion._y, z = quaternion._z, w = quaternion._w;\\n const x2 = x + x, y2 = y + y, z2 = z + z;\\n const xx = x * x2, xy = x * y2, xz = x * z2;\\n const yy = y * y2, yz = y * z2, zz = z * z2;\\n const wx = w * x2, wy = w * y2, wz = w * z2;\\n const sx = scale.x, sy = scale.y, sz = scale.z;\\n te[0] = (1 - (yy + zz)) * sx;\\n te[1] = (xy + wz) * sx;\\n te[2] = (xz - wy) * sx;\\n te[3] = 0;\\n te[4] = (xy - wz) * sy;\\n te[5] = (1 - (xx + zz)) * sy;\\n te[6] = (yz + wx) * sy;\\n te[7] = 0;\\n te[8] = (xz + wy) * sz;\\n te[9] = (yz - wx) * sz;\\n te[10] = (1 - (xx + yy)) * sz;\\n te[11] = 0;\\n te[12] = position.x;\\n te[13] = position.y;\\n te[14] = position.z;\\n te[15] = 1;\\n return this;\\n }\\n decompose(position, quaternion, scale) {\\n const te = this.elements;\\n let sx = _v1.set(te[0], te[1], te[2]).length();\\n const sy = _v1.set(te[4], te[5], te[6]).length();\\n const sz = _v1.set(te[8], te[9], te[10]).length();\\n const det = this.determinant();\\n if (det < 0)\\n sx = -sx;\\n position.x = te[12];\\n position.y = te[13];\\n position.z = te[14];\\n _m1.copy(this);\\n const invSX = 1 / sx;\\n const invSY = 1 / sy;\\n const invSZ = 1 / sz;\\n _m1.elements[0] *= invSX;\\n _m1.elements[1] *= invSX;\\n _m1.elements[2] *= invSX;\\n _m1.elements[4] *= invSY;\\n _m1.elements[5] *= invSY;\\n _m1.elements[6] *= invSY;\\n _m1.elements[8] *= invSZ;\\n _m1.elements[9] *= invSZ;\\n _m1.elements[10] *= invSZ;\\n quaternion.setFromRotationMatrix(_m1);\\n scale.x = sx;\\n scale.y = sy;\\n scale.z = sz;\\n return this;\\n }\\n makePerspective(left, right, top, bottom, near, far) {\\n if (far === void 0) {\\n console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");\\n }\\n const te = this.elements;\\n const x = 2 * near / (right - left);\\n const y = 2 * near / (top - bottom);\\n const a = (right + left) / (right - left);\\n const b = (top + bottom) / (top - bottom);\\n const c = -(far + near) / (far - near);\\n const d = -2 * far * near / (far - near);\\n te[0] = x;\\n te[4] = 0;\\n te[8] = a;\\n te[12] = 0;\\n te[1] = 0;\\n te[5] = y;\\n te[9] = b;\\n te[13] = 0;\\n te[2] = 0;\\n te[6] = 0;\\n te[10] = c;\\n te[14] = d;\\n te[3] = 0;\\n te[7] = 0;\\n te[11] = -1;\\n te[15] = 0;\\n return this;\\n }\\n makeOrthographic(left, right, top, bottom, near, far) {\\n const te = this.elements;\\n const w = 1 / (right - left);\\n const h = 1 / (top - bottom);\\n const p = 1 / (far - near);\\n const x = (right + left) * w;\\n const y = (top + bottom) * h;\\n const z = (far + near) * p;\\n te[0] = 2 * w;\\n te[4] = 0;\\n te[8] = 0;\\n te[12] = -x;\\n te[1] = 0;\\n te[5] = 2 * h;\\n te[9] = 0;\\n te[13] = -y;\\n te[2] = 0;\\n te[6] = 0;\\n te[10] = -2 * p;\\n te[14] = -z;\\n te[3] = 0;\\n te[7] = 0;\\n te[11] = 0;\\n te[15] = 1;\\n return this;\\n }\\n equals(matrix) {\\n const te = this.elements;\\n const me = matrix.elements;\\n for (let i = 0; i < 16; i++) {\\n if (te[i] !== me[i])\\n return false;\\n }\\n return true;\\n }\\n fromArray(array, offset = 0) {\\n for (let i = 0; i < 16; i++) {\\n this.elements[i] = array[i + offset];\\n }\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n const te = this.elements;\\n array[offset] = te[0];\\n array[offset + 1] = te[1];\\n array[offset + 2] = te[2];\\n array[offset + 3] = te[3];\\n array[offset + 4] = te[4];\\n array[offset + 5] = te[5];\\n array[offset + 6] = te[6];\\n array[offset + 7] = te[7];\\n array[offset + 8] = te[8];\\n array[offset + 9] = te[9];\\n array[offset + 10] = te[10];\\n array[offset + 11] = te[11];\\n array[offset + 12] = te[12];\\n array[offset + 13] = te[13];\\n array[offset + 14] = te[14];\\n array[offset + 15] = te[15];\\n return array;\\n }\\n};\\nMatrix4.prototype.isMatrix4 = true;\\nvar _v1 = /* @__PURE__ */ new Vector3();\\nvar _m1 = /* @__PURE__ */ new Matrix4();\\nvar _zero = /* @__PURE__ */ new Vector3(0, 0, 0);\\nvar _one = /* @__PURE__ */ new Vector3(1, 1, 1);\\nvar _x = /* @__PURE__ */ new Vector3();\\nvar _y = /* @__PURE__ */ new Vector3();\\nvar _z = /* @__PURE__ */ new Vector3();\\n\\n// ../../../node_modules/three/src/core/EventDispatcher.js\\nvar EventDispatcher = class {\\n addEventListener(type, listener) {\\n if (this._listeners === void 0)\\n this._listeners = {};\\n const listeners = this._listeners;\\n if (listeners[type] === void 0) {\\n listeners[type] = [];\\n }\\n if (listeners[type].indexOf(listener) === -1) {\\n listeners[type].push(listener);\\n }\\n }\\n hasEventListener(type, listener) {\\n if (this._listeners === void 0)\\n return false;\\n const listeners = this._listeners;\\n return listeners[type] !== void 0 && listeners[type].indexOf(listener) !== -1;\\n }\\n removeEventListener(type, listener) {\\n if (this._listeners === void 0)\\n return;\\n const listeners = this._listeners;\\n const listenerArray = listeners[type];\\n if (listenerArray !== void 0) {\\n const index = listenerArray.indexOf(listener);\\n if (index !== -1) {\\n listenerArray.splice(index, 1);\\n }\\n }\\n }\\n dispatchEvent(event) {\\n if (this._listeners === void 0)\\n return;\\n const listeners = this._listeners;\\n const listenerArray = listeners[event.type];\\n if (listenerArray !== void 0) {\\n event.target = this;\\n const array = listenerArray.slice(0);\\n for (let i = 0, l = array.length; i < l; i++) {\\n array[i].call(this, event);\\n }\\n event.target = null;\\n }\\n }\\n};\\n\\n// ../../../node_modules/three/src/math/Euler.js\\nvar _matrix = /* @__PURE__ */ new Matrix4();\\nvar _quaternion2 = /* @__PURE__ */ new Quaternion();\\nvar Euler = class {\\n constructor(x = 0, y = 0, z = 0, order = Euler.DefaultOrder) {\\n this._x = x;\\n this._y = y;\\n this._z = z;\\n this._order = order;\\n }\\n get x() {\\n return this._x;\\n }\\n set x(value) {\\n this._x = value;\\n this._onChangeCallback();\\n }\\n get y() {\\n return this._y;\\n }\\n set y(value) {\\n this._y = value;\\n this._onChangeCallback();\\n }\\n get z() {\\n return this._z;\\n }\\n set z(value) {\\n this._z = value;\\n this._onChangeCallback();\\n }\\n get order() {\\n return this._order;\\n }\\n set order(value) {\\n this._order = value;\\n this._onChangeCallback();\\n }\\n set(x, y, z, order = this._order) {\\n this._x = x;\\n this._y = y;\\n this._z = z;\\n this._order = order;\\n this._onChangeCallback();\\n return this;\\n }\\n clone() {\\n return new this.constructor(this._x, this._y, this._z, this._order);\\n }\\n copy(euler) {\\n this._x = euler._x;\\n this._y = euler._y;\\n this._z = euler._z;\\n this._order = euler._order;\\n this._onChangeCallback();\\n return this;\\n }\\n setFromRotationMatrix(m, order = this._order, update = true) {\\n const te = m.elements;\\n const m11 = te[0], m12 = te[4], m13 = te[8];\\n const m21 = te[1], m22 = te[5], m23 = te[9];\\n const m31 = te[2], m32 = te[6], m33 = te[10];\\n switch (order) {\\n case \\\\\\\"XYZ\\\\\\\":\\n this._y = Math.asin(clamp(m13, -1, 1));\\n if (Math.abs(m13) < 0.9999999) {\\n this._x = Math.atan2(-m23, m33);\\n this._z = Math.atan2(-m12, m11);\\n } else {\\n this._x = Math.atan2(m32, m22);\\n this._z = 0;\\n }\\n break;\\n case \\\\\\\"YXZ\\\\\\\":\\n this._x = Math.asin(-clamp(m23, -1, 1));\\n if (Math.abs(m23) < 0.9999999) {\\n this._y = Math.atan2(m13, m33);\\n this._z = Math.atan2(m21, m22);\\n } else {\\n this._y = Math.atan2(-m31, m11);\\n this._z = 0;\\n }\\n break;\\n case \\\\\\\"ZXY\\\\\\\":\\n this._x = Math.asin(clamp(m32, -1, 1));\\n if (Math.abs(m32) < 0.9999999) {\\n this._y = Math.atan2(-m31, m33);\\n this._z = Math.atan2(-m12, m22);\\n } else {\\n this._y = 0;\\n this._z = Math.atan2(m21, m11);\\n }\\n break;\\n case \\\\\\\"ZYX\\\\\\\":\\n this._y = Math.asin(-clamp(m31, -1, 1));\\n if (Math.abs(m31) < 0.9999999) {\\n this._x = Math.atan2(m32, m33);\\n this._z = Math.atan2(m21, m11);\\n } else {\\n this._x = 0;\\n this._z = Math.atan2(-m12, m22);\\n }\\n break;\\n case \\\\\\\"YZX\\\\\\\":\\n this._z = Math.asin(clamp(m21, -1, 1));\\n if (Math.abs(m21) < 0.9999999) {\\n this._x = Math.atan2(-m23, m22);\\n this._y = Math.atan2(-m31, m11);\\n } else {\\n this._x = 0;\\n this._y = Math.atan2(m13, m33);\\n }\\n break;\\n case \\\\\\\"XZY\\\\\\\":\\n this._z = Math.asin(-clamp(m12, -1, 1));\\n if (Math.abs(m12) < 0.9999999) {\\n this._x = Math.atan2(m32, m22);\\n this._y = Math.atan2(m13, m11);\\n } else {\\n this._x = Math.atan2(-m23, m33);\\n this._y = 0;\\n }\\n break;\\n default:\\n console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\" + order);\\n }\\n this._order = order;\\n if (update === true)\\n this._onChangeCallback();\\n return this;\\n }\\n setFromQuaternion(q, order, update) {\\n _matrix.makeRotationFromQuaternion(q);\\n return this.setFromRotationMatrix(_matrix, order, update);\\n }\\n setFromVector3(v, order = this._order) {\\n return this.set(v.x, v.y, v.z, order);\\n }\\n reorder(newOrder) {\\n _quaternion2.setFromEuler(this);\\n return this.setFromQuaternion(_quaternion2, newOrder);\\n }\\n equals(euler) {\\n return euler._x === this._x && euler._y === this._y && euler._z === this._z && euler._order === this._order;\\n }\\n fromArray(array) {\\n this._x = array[0];\\n this._y = array[1];\\n this._z = array[2];\\n if (array[3] !== void 0)\\n this._order = array[3];\\n this._onChangeCallback();\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this._x;\\n array[offset + 1] = this._y;\\n array[offset + 2] = this._z;\\n array[offset + 3] = this._order;\\n return array;\\n }\\n toVector3(optionalResult) {\\n if (optionalResult) {\\n return optionalResult.set(this._x, this._y, this._z);\\n } else {\\n return new Vector3(this._x, this._y, this._z);\\n }\\n }\\n _onChange(callback) {\\n this._onChangeCallback = callback;\\n return this;\\n }\\n _onChangeCallback() {\\n }\\n};\\nEuler.prototype.isEuler = true;\\nEuler.DefaultOrder = \\\\\\\"XYZ\\\\\\\";\\nEuler.RotationOrders = [\\\\\\\"XYZ\\\\\\\", \\\\\\\"YZX\\\\\\\", \\\\\\\"ZXY\\\\\\\", \\\\\\\"XZY\\\\\\\", \\\\\\\"YXZ\\\\\\\", \\\\\\\"ZYX\\\\\\\"];\\n\\n// ../../../node_modules/three/src/core/Layers.js\\nvar Layers = class {\\n constructor() {\\n this.mask = 1 | 0;\\n }\\n set(channel) {\\n this.mask = 1 << channel | 0;\\n }\\n enable(channel) {\\n this.mask |= 1 << channel | 0;\\n }\\n enableAll() {\\n this.mask = 4294967295 | 0;\\n }\\n toggle(channel) {\\n this.mask ^= 1 << channel | 0;\\n }\\n disable(channel) {\\n this.mask &= ~(1 << channel | 0);\\n }\\n disableAll() {\\n this.mask = 0;\\n }\\n test(layers) {\\n return (this.mask & layers.mask) !== 0;\\n }\\n};\\n\\n// ../../../node_modules/three/src/math/Matrix3.js\\nvar Matrix3 = class {\\n constructor() {\\n this.elements = [\\n 1,\\n 0,\\n 0,\\n 0,\\n 1,\\n 0,\\n 0,\\n 0,\\n 1\\n ];\\n if (arguments.length > 0) {\\n console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\");\\n }\\n }\\n set(n11, n12, n13, n21, n22, n23, n31, n32, n33) {\\n const te = this.elements;\\n te[0] = n11;\\n te[1] = n21;\\n te[2] = n31;\\n te[3] = n12;\\n te[4] = n22;\\n te[5] = n32;\\n te[6] = n13;\\n te[7] = n23;\\n te[8] = n33;\\n return this;\\n }\\n identity() {\\n this.set(1, 0, 0, 0, 1, 0, 0, 0, 1);\\n return this;\\n }\\n copy(m) {\\n const te = this.elements;\\n const me = m.elements;\\n te[0] = me[0];\\n te[1] = me[1];\\n te[2] = me[2];\\n te[3] = me[3];\\n te[4] = me[4];\\n te[5] = me[5];\\n te[6] = me[6];\\n te[7] = me[7];\\n te[8] = me[8];\\n return this;\\n }\\n extractBasis(xAxis, yAxis, zAxis) {\\n xAxis.setFromMatrix3Column(this, 0);\\n yAxis.setFromMatrix3Column(this, 1);\\n zAxis.setFromMatrix3Column(this, 2);\\n return this;\\n }\\n setFromMatrix4(m) {\\n const me = m.elements;\\n this.set(me[0], me[4], me[8], me[1], me[5], me[9], me[2], me[6], me[10]);\\n return this;\\n }\\n multiply(m) {\\n return this.multiplyMatrices(this, m);\\n }\\n premultiply(m) {\\n return this.multiplyMatrices(m, this);\\n }\\n multiplyMatrices(a, b) {\\n const ae = a.elements;\\n const be = b.elements;\\n const te = this.elements;\\n const a11 = ae[0], a12 = ae[3], a13 = ae[6];\\n const a21 = ae[1], a22 = ae[4], a23 = ae[7];\\n const a31 = ae[2], a32 = ae[5], a33 = ae[8];\\n const b11 = be[0], b12 = be[3], b13 = be[6];\\n const b21 = be[1], b22 = be[4], b23 = be[7];\\n const b31 = be[2], b32 = be[5], b33 = be[8];\\n te[0] = a11 * b11 + a12 * b21 + a13 * b31;\\n te[3] = a11 * b12 + a12 * b22 + a13 * b32;\\n te[6] = a11 * b13 + a12 * b23 + a13 * b33;\\n te[1] = a21 * b11 + a22 * b21 + a23 * b31;\\n te[4] = a21 * b12 + a22 * b22 + a23 * b32;\\n te[7] = a21 * b13 + a22 * b23 + a23 * b33;\\n te[2] = a31 * b11 + a32 * b21 + a33 * b31;\\n te[5] = a31 * b12 + a32 * b22 + a33 * b32;\\n te[8] = a31 * b13 + a32 * b23 + a33 * b33;\\n return this;\\n }\\n multiplyScalar(s) {\\n const te = this.elements;\\n te[0] *= s;\\n te[3] *= s;\\n te[6] *= s;\\n te[1] *= s;\\n te[4] *= s;\\n te[7] *= s;\\n te[2] *= s;\\n te[5] *= s;\\n te[8] *= s;\\n return this;\\n }\\n determinant() {\\n const te = this.elements;\\n const a = te[0], b = te[1], c = te[2], d = te[3], e = te[4], f = te[5], g = te[6], h = te[7], i = te[8];\\n return a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g;\\n }\\n invert() {\\n const te = this.elements, n11 = te[0], n21 = te[1], n31 = te[2], n12 = te[3], n22 = te[4], n32 = te[5], n13 = te[6], n23 = te[7], n33 = te[8], t11 = n33 * n22 - n32 * n23, t12 = n32 * n13 - n33 * n12, t13 = n23 * n12 - n22 * n13, det = n11 * t11 + n21 * t12 + n31 * t13;\\n if (det === 0)\\n return this.set(0, 0, 0, 0, 0, 0, 0, 0, 0);\\n const detInv = 1 / det;\\n te[0] = t11 * detInv;\\n te[1] = (n31 * n23 - n33 * n21) * detInv;\\n te[2] = (n32 * n21 - n31 * n22) * detInv;\\n te[3] = t12 * detInv;\\n te[4] = (n33 * n11 - n31 * n13) * detInv;\\n te[5] = (n31 * n12 - n32 * n11) * detInv;\\n te[6] = t13 * detInv;\\n te[7] = (n21 * n13 - n23 * n11) * detInv;\\n te[8] = (n22 * n11 - n21 * n12) * detInv;\\n return this;\\n }\\n transpose() {\\n let tmp;\\n const m = this.elements;\\n tmp = m[1];\\n m[1] = m[3];\\n m[3] = tmp;\\n tmp = m[2];\\n m[2] = m[6];\\n m[6] = tmp;\\n tmp = m[5];\\n m[5] = m[7];\\n m[7] = tmp;\\n return this;\\n }\\n getNormalMatrix(matrix4) {\\n return this.setFromMatrix4(matrix4).invert().transpose();\\n }\\n transposeIntoArray(r) {\\n const m = this.elements;\\n r[0] = m[0];\\n r[1] = m[3];\\n r[2] = m[6];\\n r[3] = m[1];\\n r[4] = m[4];\\n r[5] = m[7];\\n r[6] = m[2];\\n r[7] = m[5];\\n r[8] = m[8];\\n return this;\\n }\\n setUvTransform(tx, ty, sx, sy, rotation, cx, cy) {\\n const c = Math.cos(rotation);\\n const s = Math.sin(rotation);\\n this.set(sx * c, sx * s, -sx * (c * cx + s * cy) + cx + tx, -sy * s, sy * c, -sy * (-s * cx + c * cy) + cy + ty, 0, 0, 1);\\n return this;\\n }\\n scale(sx, sy) {\\n const te = this.elements;\\n te[0] *= sx;\\n te[3] *= sx;\\n te[6] *= sx;\\n te[1] *= sy;\\n te[4] *= sy;\\n te[7] *= sy;\\n return this;\\n }\\n rotate(theta) {\\n const c = Math.cos(theta);\\n const s = Math.sin(theta);\\n const te = this.elements;\\n const a11 = te[0], a12 = te[3], a13 = te[6];\\n const a21 = te[1], a22 = te[4], a23 = te[7];\\n te[0] = c * a11 + s * a21;\\n te[3] = c * a12 + s * a22;\\n te[6] = c * a13 + s * a23;\\n te[1] = -s * a11 + c * a21;\\n te[4] = -s * a12 + c * a22;\\n te[7] = -s * a13 + c * a23;\\n return this;\\n }\\n translate(tx, ty) {\\n const te = this.elements;\\n te[0] += tx * te[2];\\n te[3] += tx * te[5];\\n te[6] += tx * te[8];\\n te[1] += ty * te[2];\\n te[4] += ty * te[5];\\n te[7] += ty * te[8];\\n return this;\\n }\\n equals(matrix) {\\n const te = this.elements;\\n const me = matrix.elements;\\n for (let i = 0; i < 9; i++) {\\n if (te[i] !== me[i])\\n return false;\\n }\\n return true;\\n }\\n fromArray(array, offset = 0) {\\n for (let i = 0; i < 9; i++) {\\n this.elements[i] = array[i + offset];\\n }\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n const te = this.elements;\\n array[offset] = te[0];\\n array[offset + 1] = te[1];\\n array[offset + 2] = te[2];\\n array[offset + 3] = te[3];\\n array[offset + 4] = te[4];\\n array[offset + 5] = te[5];\\n array[offset + 6] = te[6];\\n array[offset + 7] = te[7];\\n array[offset + 8] = te[8];\\n return array;\\n }\\n clone() {\\n return new this.constructor().fromArray(this.elements);\\n }\\n};\\nMatrix3.prototype.isMatrix3 = true;\\n\\n// ../../../node_modules/three/src/core/Object3D.js\\nvar _object3DId = 0;\\nvar _v12 = /* @__PURE__ */ new Vector3();\\nvar _q1 = /* @__PURE__ */ new Quaternion();\\nvar _m12 = /* @__PURE__ */ new Matrix4();\\nvar _target = /* @__PURE__ */ new Vector3();\\nvar _position = /* @__PURE__ */ new Vector3();\\nvar _scale = /* @__PURE__ */ new Vector3();\\nvar _quaternion3 = /* @__PURE__ */ new Quaternion();\\nvar _xAxis = /* @__PURE__ */ new Vector3(1, 0, 0);\\nvar _yAxis = /* @__PURE__ */ new Vector3(0, 1, 0);\\nvar _zAxis = /* @__PURE__ */ new Vector3(0, 0, 1);\\nvar _addedEvent = {type: \\\\\\\"added\\\\\\\"};\\nvar _removedEvent = {type: \\\\\\\"removed\\\\\\\"};\\nvar Object3D = class extends EventDispatcher {\\n constructor() {\\n super();\\n Object.defineProperty(this, \\\\\\\"id\\\\\\\", {value: _object3DId++});\\n this.uuid = generateUUID();\\n this.name = \\\\\\\"\\\\\\\";\\n this.type = \\\\\\\"Object3D\\\\\\\";\\n this.parent = null;\\n this.children = [];\\n this.up = Object3D.DefaultUp.clone();\\n const position = new Vector3();\\n const rotation = new Euler();\\n const quaternion = new Quaternion();\\n const scale = new Vector3(1, 1, 1);\\n function onRotationChange() {\\n quaternion.setFromEuler(rotation, false);\\n }\\n function onQuaternionChange() {\\n rotation.setFromQuaternion(quaternion, void 0, false);\\n }\\n rotation._onChange(onRotationChange);\\n quaternion._onChange(onQuaternionChange);\\n Object.defineProperties(this, {\\n position: {\\n configurable: true,\\n enumerable: true,\\n value: position\\n },\\n rotation: {\\n configurable: true,\\n enumerable: true,\\n value: rotation\\n },\\n quaternion: {\\n configurable: true,\\n enumerable: true,\\n value: quaternion\\n },\\n scale: {\\n configurable: true,\\n enumerable: true,\\n value: scale\\n },\\n modelViewMatrix: {\\n value: new Matrix4()\\n },\\n normalMatrix: {\\n value: new Matrix3()\\n }\\n });\\n this.matrix = new Matrix4();\\n this.matrixWorld = new Matrix4();\\n this.matrixAutoUpdate = Object3D.DefaultMatrixAutoUpdate;\\n this.matrixWorldNeedsUpdate = false;\\n this.layers = new Layers();\\n this.visible = true;\\n this.castShadow = false;\\n this.receiveShadow = false;\\n this.frustumCulled = true;\\n this.renderOrder = 0;\\n this.animations = [];\\n this.userData = {};\\n }\\n onBeforeRender() {\\n }\\n onAfterRender() {\\n }\\n applyMatrix4(matrix) {\\n if (this.matrixAutoUpdate)\\n this.updateMatrix();\\n this.matrix.premultiply(matrix);\\n this.matrix.decompose(this.position, this.quaternion, this.scale);\\n }\\n applyQuaternion(q) {\\n this.quaternion.premultiply(q);\\n return this;\\n }\\n setRotationFromAxisAngle(axis, angle) {\\n this.quaternion.setFromAxisAngle(axis, angle);\\n }\\n setRotationFromEuler(euler) {\\n this.quaternion.setFromEuler(euler, true);\\n }\\n setRotationFromMatrix(m) {\\n this.quaternion.setFromRotationMatrix(m);\\n }\\n setRotationFromQuaternion(q) {\\n this.quaternion.copy(q);\\n }\\n rotateOnAxis(axis, angle) {\\n _q1.setFromAxisAngle(axis, angle);\\n this.quaternion.multiply(_q1);\\n return this;\\n }\\n rotateOnWorldAxis(axis, angle) {\\n _q1.setFromAxisAngle(axis, angle);\\n this.quaternion.premultiply(_q1);\\n return this;\\n }\\n rotateX(angle) {\\n return this.rotateOnAxis(_xAxis, angle);\\n }\\n rotateY(angle) {\\n return this.rotateOnAxis(_yAxis, angle);\\n }\\n rotateZ(angle) {\\n return this.rotateOnAxis(_zAxis, angle);\\n }\\n translateOnAxis(axis, distance) {\\n _v12.copy(axis).applyQuaternion(this.quaternion);\\n this.position.add(_v12.multiplyScalar(distance));\\n return this;\\n }\\n translateX(distance) {\\n return this.translateOnAxis(_xAxis, distance);\\n }\\n translateY(distance) {\\n return this.translateOnAxis(_yAxis, distance);\\n }\\n translateZ(distance) {\\n return this.translateOnAxis(_zAxis, distance);\\n }\\n localToWorld(vector) {\\n return vector.applyMatrix4(this.matrixWorld);\\n }\\n worldToLocal(vector) {\\n return vector.applyMatrix4(_m12.copy(this.matrixWorld).invert());\\n }\\n lookAt(x, y, z) {\\n if (x.isVector3) {\\n _target.copy(x);\\n } else {\\n _target.set(x, y, z);\\n }\\n const parent = this.parent;\\n this.updateWorldMatrix(true, false);\\n _position.setFromMatrixPosition(this.matrixWorld);\\n if (this.isCamera || this.isLight) {\\n _m12.lookAt(_position, _target, this.up);\\n } else {\\n _m12.lookAt(_target, _position, this.up);\\n }\\n this.quaternion.setFromRotationMatrix(_m12);\\n if (parent) {\\n _m12.extractRotation(parent.matrixWorld);\\n _q1.setFromRotationMatrix(_m12);\\n this.quaternion.premultiply(_q1.invert());\\n }\\n }\\n add(object) {\\n if (arguments.length > 1) {\\n for (let i = 0; i < arguments.length; i++) {\\n this.add(arguments[i]);\\n }\\n return this;\\n }\\n if (object === this) {\\n console.error(\\\\\\\"THREE.Object3D.add: object can't be added as a child of itself.\\\\\\\", object);\\n return this;\\n }\\n if (object && object.isObject3D) {\\n if (object.parent !== null) {\\n object.parent.remove(object);\\n }\\n object.parent = this;\\n this.children.push(object);\\n object.dispatchEvent(_addedEvent);\\n } else {\\n console.error(\\\\\\\"THREE.Object3D.add: object not an instance of THREE.Object3D.\\\\\\\", object);\\n }\\n return this;\\n }\\n remove(object) {\\n if (arguments.length > 1) {\\n for (let i = 0; i < arguments.length; i++) {\\n this.remove(arguments[i]);\\n }\\n return this;\\n }\\n const index = this.children.indexOf(object);\\n if (index !== -1) {\\n object.parent = null;\\n this.children.splice(index, 1);\\n object.dispatchEvent(_removedEvent);\\n }\\n return this;\\n }\\n removeFromParent() {\\n const parent = this.parent;\\n if (parent !== null) {\\n parent.remove(this);\\n }\\n return this;\\n }\\n clear() {\\n for (let i = 0; i < this.children.length; i++) {\\n const object = this.children[i];\\n object.parent = null;\\n object.dispatchEvent(_removedEvent);\\n }\\n this.children.length = 0;\\n return this;\\n }\\n attach(object) {\\n this.updateWorldMatrix(true, false);\\n _m12.copy(this.matrixWorld).invert();\\n if (object.parent !== null) {\\n object.parent.updateWorldMatrix(true, false);\\n _m12.multiply(object.parent.matrixWorld);\\n }\\n object.applyMatrix4(_m12);\\n this.add(object);\\n object.updateWorldMatrix(false, true);\\n return this;\\n }\\n getObjectById(id) {\\n return this.getObjectByProperty(\\\\\\\"id\\\\\\\", id);\\n }\\n getObjectByName(name) {\\n return this.getObjectByProperty(\\\\\\\"name\\\\\\\", name);\\n }\\n getObjectByProperty(name, value) {\\n if (this[name] === value)\\n return this;\\n for (let i = 0, l = this.children.length; i < l; i++) {\\n const child = this.children[i];\\n const object = child.getObjectByProperty(name, value);\\n if (object !== void 0) {\\n return object;\\n }\\n }\\n return void 0;\\n }\\n getWorldPosition(target) {\\n this.updateWorldMatrix(true, false);\\n return target.setFromMatrixPosition(this.matrixWorld);\\n }\\n getWorldQuaternion(target) {\\n this.updateWorldMatrix(true, false);\\n this.matrixWorld.decompose(_position, target, _scale);\\n return target;\\n }\\n getWorldScale(target) {\\n this.updateWorldMatrix(true, false);\\n this.matrixWorld.decompose(_position, _quaternion3, target);\\n return target;\\n }\\n getWorldDirection(target) {\\n this.updateWorldMatrix(true, false);\\n const e = this.matrixWorld.elements;\\n return target.set(e[8], e[9], e[10]).normalize();\\n }\\n raycast() {\\n }\\n traverse(callback) {\\n callback(this);\\n const children = this.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n children[i].traverse(callback);\\n }\\n }\\n traverseVisible(callback) {\\n if (this.visible === false)\\n return;\\n callback(this);\\n const children = this.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n children[i].traverseVisible(callback);\\n }\\n }\\n traverseAncestors(callback) {\\n const parent = this.parent;\\n if (parent !== null) {\\n callback(parent);\\n parent.traverseAncestors(callback);\\n }\\n }\\n updateMatrix() {\\n this.matrix.compose(this.position, this.quaternion, this.scale);\\n this.matrixWorldNeedsUpdate = true;\\n }\\n updateMatrixWorld(force) {\\n if (this.matrixAutoUpdate)\\n this.updateMatrix();\\n if (this.matrixWorldNeedsUpdate || force) {\\n if (this.parent === null) {\\n this.matrixWorld.copy(this.matrix);\\n } else {\\n this.matrixWorld.multiplyMatrices(this.parent.matrixWorld, this.matrix);\\n }\\n this.matrixWorldNeedsUpdate = false;\\n force = true;\\n }\\n const children = this.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n children[i].updateMatrixWorld(force);\\n }\\n }\\n updateWorldMatrix(updateParents, updateChildren) {\\n const parent = this.parent;\\n if (updateParents === true && parent !== null) {\\n parent.updateWorldMatrix(true, false);\\n }\\n if (this.matrixAutoUpdate)\\n this.updateMatrix();\\n if (this.parent === null) {\\n this.matrixWorld.copy(this.matrix);\\n } else {\\n this.matrixWorld.multiplyMatrices(this.parent.matrixWorld, this.matrix);\\n }\\n if (updateChildren === true) {\\n const children = this.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n children[i].updateWorldMatrix(false, true);\\n }\\n }\\n }\\n toJSON(meta) {\\n const isRootObject = meta === void 0 || typeof meta === \\\\\\\"string\\\\\\\";\\n const output = {};\\n if (isRootObject) {\\n meta = {\\n geometries: {},\\n materials: {},\\n textures: {},\\n images: {},\\n shapes: {},\\n skeletons: {},\\n animations: {}\\n };\\n output.metadata = {\\n version: 4.5,\\n type: \\\\\\\"Object\\\\\\\",\\n generator: \\\\\\\"Object3D.toJSON\\\\\\\"\\n };\\n }\\n const object = {};\\n object.uuid = this.uuid;\\n object.type = this.type;\\n if (this.name !== \\\\\\\"\\\\\\\")\\n object.name = this.name;\\n if (this.castShadow === true)\\n object.castShadow = true;\\n if (this.receiveShadow === true)\\n object.receiveShadow = true;\\n if (this.visible === false)\\n object.visible = false;\\n if (this.frustumCulled === false)\\n object.frustumCulled = false;\\n if (this.renderOrder !== 0)\\n object.renderOrder = this.renderOrder;\\n if (JSON.stringify(this.userData) !== \\\\\\\"{}\\\\\\\")\\n object.userData = this.userData;\\n object.layers = this.layers.mask;\\n object.matrix = this.matrix.toArray();\\n if (this.matrixAutoUpdate === false)\\n object.matrixAutoUpdate = false;\\n if (this.isInstancedMesh) {\\n object.type = \\\\\\\"InstancedMesh\\\\\\\";\\n object.count = this.count;\\n object.instanceMatrix = this.instanceMatrix.toJSON();\\n if (this.instanceColor !== null)\\n object.instanceColor = this.instanceColor.toJSON();\\n }\\n function serialize(library, element) {\\n if (library[element.uuid] === void 0) {\\n library[element.uuid] = element.toJSON(meta);\\n }\\n return element.uuid;\\n }\\n if (this.isScene) {\\n if (this.background) {\\n if (this.background.isColor) {\\n object.background = this.background.toJSON();\\n } else if (this.background.isTexture) {\\n object.background = this.background.toJSON(meta).uuid;\\n }\\n }\\n if (this.environment && this.environment.isTexture) {\\n object.environment = this.environment.toJSON(meta).uuid;\\n }\\n } else if (this.isMesh || this.isLine || this.isPoints) {\\n object.geometry = serialize(meta.geometries, this.geometry);\\n const parameters = this.geometry.parameters;\\n if (parameters !== void 0 && parameters.shapes !== void 0) {\\n const shapes = parameters.shapes;\\n if (Array.isArray(shapes)) {\\n for (let i = 0, l = shapes.length; i < l; i++) {\\n const shape = shapes[i];\\n serialize(meta.shapes, shape);\\n }\\n } else {\\n serialize(meta.shapes, shapes);\\n }\\n }\\n }\\n if (this.isSkinnedMesh) {\\n object.bindMode = this.bindMode;\\n object.bindMatrix = this.bindMatrix.toArray();\\n if (this.skeleton !== void 0) {\\n serialize(meta.skeletons, this.skeleton);\\n object.skeleton = this.skeleton.uuid;\\n }\\n }\\n if (this.material !== void 0) {\\n if (Array.isArray(this.material)) {\\n const uuids = [];\\n for (let i = 0, l = this.material.length; i < l; i++) {\\n uuids.push(serialize(meta.materials, this.material[i]));\\n }\\n object.material = uuids;\\n } else {\\n object.material = serialize(meta.materials, this.material);\\n }\\n }\\n if (this.children.length > 0) {\\n object.children = [];\\n for (let i = 0; i < this.children.length; i++) {\\n object.children.push(this.children[i].toJSON(meta).object);\\n }\\n }\\n if (this.animations.length > 0) {\\n object.animations = [];\\n for (let i = 0; i < this.animations.length; i++) {\\n const animation = this.animations[i];\\n object.animations.push(serialize(meta.animations, animation));\\n }\\n }\\n if (isRootObject) {\\n const geometries = extractFromCache(meta.geometries);\\n const materials2 = extractFromCache(meta.materials);\\n const textures = extractFromCache(meta.textures);\\n const images = extractFromCache(meta.images);\\n const shapes = extractFromCache(meta.shapes);\\n const skeletons = extractFromCache(meta.skeletons);\\n const animations = extractFromCache(meta.animations);\\n if (geometries.length > 0)\\n output.geometries = geometries;\\n if (materials2.length > 0)\\n output.materials = materials2;\\n if (textures.length > 0)\\n output.textures = textures;\\n if (images.length > 0)\\n output.images = images;\\n if (shapes.length > 0)\\n output.shapes = shapes;\\n if (skeletons.length > 0)\\n output.skeletons = skeletons;\\n if (animations.length > 0)\\n output.animations = animations;\\n }\\n output.object = object;\\n return output;\\n function extractFromCache(cache) {\\n const values = [];\\n for (const key in cache) {\\n const data = cache[key];\\n delete data.metadata;\\n values.push(data);\\n }\\n return values;\\n }\\n }\\n clone(recursive) {\\n return new this.constructor().copy(this, recursive);\\n }\\n copy(source, recursive = true) {\\n this.name = source.name;\\n this.up.copy(source.up);\\n this.position.copy(source.position);\\n this.rotation.order = source.rotation.order;\\n this.quaternion.copy(source.quaternion);\\n this.scale.copy(source.scale);\\n this.matrix.copy(source.matrix);\\n this.matrixWorld.copy(source.matrixWorld);\\n this.matrixAutoUpdate = source.matrixAutoUpdate;\\n this.matrixWorldNeedsUpdate = source.matrixWorldNeedsUpdate;\\n this.layers.mask = source.layers.mask;\\n this.visible = source.visible;\\n this.castShadow = source.castShadow;\\n this.receiveShadow = source.receiveShadow;\\n this.frustumCulled = source.frustumCulled;\\n this.renderOrder = source.renderOrder;\\n this.userData = JSON.parse(JSON.stringify(source.userData));\\n if (recursive === true) {\\n for (let i = 0; i < source.children.length; i++) {\\n const child = source.children[i];\\n this.add(child.clone());\\n }\\n }\\n return this;\\n }\\n};\\nObject3D.DefaultUp = new Vector3(0, 1, 0);\\nObject3D.DefaultMatrixAutoUpdate = true;\\nObject3D.prototype.isObject3D = true;\\n\\n// ../../../node_modules/three/src/constants.js\\nvar CullFaceNone = 0;\\nvar CullFaceBack = 1;\\nvar CullFaceFront = 2;\\nvar PCFShadowMap = 1;\\nvar PCFSoftShadowMap = 2;\\nvar VSMShadowMap = 3;\\nvar FrontSide = 0;\\nvar BackSide = 1;\\nvar DoubleSide = 2;\\nvar FlatShading = 1;\\nvar NoBlending = 0;\\nvar NormalBlending = 1;\\nvar AdditiveBlending = 2;\\nvar SubtractiveBlending = 3;\\nvar MultiplyBlending = 4;\\nvar CustomBlending = 5;\\nvar AddEquation = 100;\\nvar SubtractEquation = 101;\\nvar ReverseSubtractEquation = 102;\\nvar MinEquation = 103;\\nvar MaxEquation = 104;\\nvar ZeroFactor = 200;\\nvar OneFactor = 201;\\nvar SrcColorFactor = 202;\\nvar OneMinusSrcColorFactor = 203;\\nvar SrcAlphaFactor = 204;\\nvar OneMinusSrcAlphaFactor = 205;\\nvar DstAlphaFactor = 206;\\nvar OneMinusDstAlphaFactor = 207;\\nvar DstColorFactor = 208;\\nvar OneMinusDstColorFactor = 209;\\nvar SrcAlphaSaturateFactor = 210;\\nvar NeverDepth = 0;\\nvar AlwaysDepth = 1;\\nvar LessDepth = 2;\\nvar LessEqualDepth = 3;\\nvar EqualDepth = 4;\\nvar GreaterEqualDepth = 5;\\nvar GreaterDepth = 6;\\nvar NotEqualDepth = 7;\\nvar MultiplyOperation = 0;\\nvar MixOperation = 1;\\nvar AddOperation = 2;\\nvar NoToneMapping = 0;\\nvar LinearToneMapping = 1;\\nvar ReinhardToneMapping = 2;\\nvar CineonToneMapping = 3;\\nvar ACESFilmicToneMapping = 4;\\nvar CustomToneMapping = 5;\\nvar UVMapping = 300;\\nvar CubeReflectionMapping = 301;\\nvar CubeRefractionMapping = 302;\\nvar EquirectangularReflectionMapping = 303;\\nvar EquirectangularRefractionMapping = 304;\\nvar CubeUVReflectionMapping = 306;\\nvar CubeUVRefractionMapping = 307;\\nvar RepeatWrapping = 1e3;\\nvar ClampToEdgeWrapping = 1001;\\nvar MirroredRepeatWrapping = 1002;\\nvar NearestFilter = 1003;\\nvar NearestMipmapNearestFilter = 1004;\\nvar NearestMipmapLinearFilter = 1005;\\nvar LinearFilter = 1006;\\nvar LinearMipmapNearestFilter = 1007;\\nvar LinearMipmapLinearFilter = 1008;\\nvar UnsignedByteType = 1009;\\nvar ByteType = 1010;\\nvar ShortType = 1011;\\nvar UnsignedShortType = 1012;\\nvar IntType = 1013;\\nvar UnsignedIntType = 1014;\\nvar FloatType = 1015;\\nvar HalfFloatType = 1016;\\nvar UnsignedShort4444Type = 1017;\\nvar UnsignedShort5551Type = 1018;\\nvar UnsignedShort565Type = 1019;\\nvar UnsignedInt248Type = 1020;\\nvar AlphaFormat = 1021;\\nvar RGBFormat = 1022;\\nvar RGBAFormat = 1023;\\nvar LuminanceFormat = 1024;\\nvar LuminanceAlphaFormat = 1025;\\nvar RGBEFormat = RGBAFormat;\\nvar DepthFormat = 1026;\\nvar DepthStencilFormat = 1027;\\nvar RedFormat = 1028;\\nvar RedIntegerFormat = 1029;\\nvar RGFormat = 1030;\\nvar RGIntegerFormat = 1031;\\nvar RGBIntegerFormat = 1032;\\nvar RGBAIntegerFormat = 1033;\\nvar RGB_S3TC_DXT1_Format = 33776;\\nvar RGBA_S3TC_DXT1_Format = 33777;\\nvar RGBA_S3TC_DXT3_Format = 33778;\\nvar RGBA_S3TC_DXT5_Format = 33779;\\nvar RGB_PVRTC_4BPPV1_Format = 35840;\\nvar RGB_PVRTC_2BPPV1_Format = 35841;\\nvar RGBA_PVRTC_4BPPV1_Format = 35842;\\nvar RGBA_PVRTC_2BPPV1_Format = 35843;\\nvar RGB_ETC1_Format = 36196;\\nvar RGB_ETC2_Format = 37492;\\nvar RGBA_ETC2_EAC_Format = 37496;\\nvar RGBA_ASTC_4x4_Format = 37808;\\nvar RGBA_ASTC_5x4_Format = 37809;\\nvar RGBA_ASTC_5x5_Format = 37810;\\nvar RGBA_ASTC_6x5_Format = 37811;\\nvar RGBA_ASTC_6x6_Format = 37812;\\nvar RGBA_ASTC_8x5_Format = 37813;\\nvar RGBA_ASTC_8x6_Format = 37814;\\nvar RGBA_ASTC_8x8_Format = 37815;\\nvar RGBA_ASTC_10x5_Format = 37816;\\nvar RGBA_ASTC_10x6_Format = 37817;\\nvar RGBA_ASTC_10x8_Format = 37818;\\nvar RGBA_ASTC_10x10_Format = 37819;\\nvar RGBA_ASTC_12x10_Format = 37820;\\nvar RGBA_ASTC_12x12_Format = 37821;\\nvar RGBA_BPTC_Format = 36492;\\nvar SRGB8_ALPHA8_ASTC_4x4_Format = 37840;\\nvar SRGB8_ALPHA8_ASTC_5x4_Format = 37841;\\nvar SRGB8_ALPHA8_ASTC_5x5_Format = 37842;\\nvar SRGB8_ALPHA8_ASTC_6x5_Format = 37843;\\nvar SRGB8_ALPHA8_ASTC_6x6_Format = 37844;\\nvar SRGB8_ALPHA8_ASTC_8x5_Format = 37845;\\nvar SRGB8_ALPHA8_ASTC_8x6_Format = 37846;\\nvar SRGB8_ALPHA8_ASTC_8x8_Format = 37847;\\nvar SRGB8_ALPHA8_ASTC_10x5_Format = 37848;\\nvar SRGB8_ALPHA8_ASTC_10x6_Format = 37849;\\nvar SRGB8_ALPHA8_ASTC_10x8_Format = 37850;\\nvar SRGB8_ALPHA8_ASTC_10x10_Format = 37851;\\nvar SRGB8_ALPHA8_ASTC_12x10_Format = 37852;\\nvar SRGB8_ALPHA8_ASTC_12x12_Format = 37853;\\nvar ZeroCurvatureEnding = 2400;\\nvar ZeroSlopeEnding = 2401;\\nvar WrapAroundEnding = 2402;\\nvar LinearEncoding = 3e3;\\nvar sRGBEncoding = 3001;\\nvar GammaEncoding = 3007;\\nvar RGBEEncoding = 3002;\\nvar LogLuvEncoding = 3003;\\nvar RGBM7Encoding = 3004;\\nvar RGBM16Encoding = 3005;\\nvar RGBDEncoding = 3006;\\nvar BasicDepthPacking = 3200;\\nvar RGBADepthPacking = 3201;\\nvar TangentSpaceNormalMap = 0;\\nvar ObjectSpaceNormalMap = 1;\\nvar KeepStencilOp = 7680;\\nvar AlwaysStencilFunc = 519;\\nvar StaticDrawUsage = 35044;\\nvar GLSL3 = \\\\\\\"300 es\\\\\\\";\\n\\n// ../../../node_modules/three/src/math/Box3.js\\nvar Box3 = class {\\n constructor(min = new Vector3(Infinity, Infinity, Infinity), max = new Vector3(-Infinity, -Infinity, -Infinity)) {\\n this.min = min;\\n this.max = max;\\n }\\n set(min, max) {\\n this.min.copy(min);\\n this.max.copy(max);\\n return this;\\n }\\n setFromArray(array) {\\n let minX = Infinity;\\n let minY = Infinity;\\n let minZ = Infinity;\\n let maxX = -Infinity;\\n let maxY = -Infinity;\\n let maxZ = -Infinity;\\n for (let i = 0, l = array.length; i < l; i += 3) {\\n const x = array[i];\\n const y = array[i + 1];\\n const z = array[i + 2];\\n if (x < minX)\\n minX = x;\\n if (y < minY)\\n minY = y;\\n if (z < minZ)\\n minZ = z;\\n if (x > maxX)\\n maxX = x;\\n if (y > maxY)\\n maxY = y;\\n if (z > maxZ)\\n maxZ = z;\\n }\\n this.min.set(minX, minY, minZ);\\n this.max.set(maxX, maxY, maxZ);\\n return this;\\n }\\n setFromBufferAttribute(attribute) {\\n let minX = Infinity;\\n let minY = Infinity;\\n let minZ = Infinity;\\n let maxX = -Infinity;\\n let maxY = -Infinity;\\n let maxZ = -Infinity;\\n for (let i = 0, l = attribute.count; i < l; i++) {\\n const x = attribute.getX(i);\\n const y = attribute.getY(i);\\n const z = attribute.getZ(i);\\n if (x < minX)\\n minX = x;\\n if (y < minY)\\n minY = y;\\n if (z < minZ)\\n minZ = z;\\n if (x > maxX)\\n maxX = x;\\n if (y > maxY)\\n maxY = y;\\n if (z > maxZ)\\n maxZ = z;\\n }\\n this.min.set(minX, minY, minZ);\\n this.max.set(maxX, maxY, maxZ);\\n return this;\\n }\\n setFromPoints(points2) {\\n this.makeEmpty();\\n for (let i = 0, il = points2.length; i < il; i++) {\\n this.expandByPoint(points2[i]);\\n }\\n return this;\\n }\\n setFromCenterAndSize(center, size) {\\n const halfSize = _vector2.copy(size).multiplyScalar(0.5);\\n this.min.copy(center).sub(halfSize);\\n this.max.copy(center).add(halfSize);\\n return this;\\n }\\n setFromObject(object) {\\n this.makeEmpty();\\n return this.expandByObject(object);\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(box) {\\n this.min.copy(box.min);\\n this.max.copy(box.max);\\n return this;\\n }\\n makeEmpty() {\\n this.min.x = this.min.y = this.min.z = Infinity;\\n this.max.x = this.max.y = this.max.z = -Infinity;\\n return this;\\n }\\n isEmpty() {\\n return this.max.x < this.min.x || this.max.y < this.min.y || this.max.z < this.min.z;\\n }\\n getCenter(target) {\\n return this.isEmpty() ? target.set(0, 0, 0) : target.addVectors(this.min, this.max).multiplyScalar(0.5);\\n }\\n getSize(target) {\\n return this.isEmpty() ? target.set(0, 0, 0) : target.subVectors(this.max, this.min);\\n }\\n expandByPoint(point) {\\n this.min.min(point);\\n this.max.max(point);\\n return this;\\n }\\n expandByVector(vector) {\\n this.min.sub(vector);\\n this.max.add(vector);\\n return this;\\n }\\n expandByScalar(scalar) {\\n this.min.addScalar(-scalar);\\n this.max.addScalar(scalar);\\n return this;\\n }\\n expandByObject(object) {\\n object.updateWorldMatrix(false, false);\\n const geometry = object.geometry;\\n if (geometry !== void 0) {\\n if (geometry.boundingBox === null) {\\n geometry.computeBoundingBox();\\n }\\n _box.copy(geometry.boundingBox);\\n _box.applyMatrix4(object.matrixWorld);\\n this.union(_box);\\n }\\n const children = object.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n this.expandByObject(children[i]);\\n }\\n return this;\\n }\\n containsPoint(point) {\\n return point.x < this.min.x || point.x > this.max.x || point.y < this.min.y || point.y > this.max.y || point.z < this.min.z || point.z > this.max.z ? false : true;\\n }\\n containsBox(box) {\\n return this.min.x <= box.min.x && box.max.x <= this.max.x && this.min.y <= box.min.y && box.max.y <= this.max.y && this.min.z <= box.min.z && box.max.z <= this.max.z;\\n }\\n getParameter(point, target) {\\n return target.set((point.x - this.min.x) / (this.max.x - this.min.x), (point.y - this.min.y) / (this.max.y - this.min.y), (point.z - this.min.z) / (this.max.z - this.min.z));\\n }\\n intersectsBox(box) {\\n return box.max.x < this.min.x || box.min.x > this.max.x || box.max.y < this.min.y || box.min.y > this.max.y || box.max.z < this.min.z || box.min.z > this.max.z ? false : true;\\n }\\n intersectsSphere(sphere) {\\n this.clampPoint(sphere.center, _vector2);\\n return _vector2.distanceToSquared(sphere.center) <= sphere.radius * sphere.radius;\\n }\\n intersectsPlane(plane) {\\n let min, max;\\n if (plane.normal.x > 0) {\\n min = plane.normal.x * this.min.x;\\n max = plane.normal.x * this.max.x;\\n } else {\\n min = plane.normal.x * this.max.x;\\n max = plane.normal.x * this.min.x;\\n }\\n if (plane.normal.y > 0) {\\n min += plane.normal.y * this.min.y;\\n max += plane.normal.y * this.max.y;\\n } else {\\n min += plane.normal.y * this.max.y;\\n max += plane.normal.y * this.min.y;\\n }\\n if (plane.normal.z > 0) {\\n min += plane.normal.z * this.min.z;\\n max += plane.normal.z * this.max.z;\\n } else {\\n min += plane.normal.z * this.max.z;\\n max += plane.normal.z * this.min.z;\\n }\\n return min <= -plane.constant && max >= -plane.constant;\\n }\\n intersectsTriangle(triangle) {\\n if (this.isEmpty()) {\\n return false;\\n }\\n this.getCenter(_center);\\n _extents.subVectors(this.max, _center);\\n _v0.subVectors(triangle.a, _center);\\n _v13.subVectors(triangle.b, _center);\\n _v2.subVectors(triangle.c, _center);\\n _f0.subVectors(_v13, _v0);\\n _f1.subVectors(_v2, _v13);\\n _f2.subVectors(_v0, _v2);\\n let axes = [\\n 0,\\n -_f0.z,\\n _f0.y,\\n 0,\\n -_f1.z,\\n _f1.y,\\n 0,\\n -_f2.z,\\n _f2.y,\\n _f0.z,\\n 0,\\n -_f0.x,\\n _f1.z,\\n 0,\\n -_f1.x,\\n _f2.z,\\n 0,\\n -_f2.x,\\n -_f0.y,\\n _f0.x,\\n 0,\\n -_f1.y,\\n _f1.x,\\n 0,\\n -_f2.y,\\n _f2.x,\\n 0\\n ];\\n if (!satForAxes(axes, _v0, _v13, _v2, _extents)) {\\n return false;\\n }\\n axes = [1, 0, 0, 0, 1, 0, 0, 0, 1];\\n if (!satForAxes(axes, _v0, _v13, _v2, _extents)) {\\n return false;\\n }\\n _triangleNormal.crossVectors(_f0, _f1);\\n axes = [_triangleNormal.x, _triangleNormal.y, _triangleNormal.z];\\n return satForAxes(axes, _v0, _v13, _v2, _extents);\\n }\\n clampPoint(point, target) {\\n return target.copy(point).clamp(this.min, this.max);\\n }\\n distanceToPoint(point) {\\n const clampedPoint = _vector2.copy(point).clamp(this.min, this.max);\\n return clampedPoint.sub(point).length();\\n }\\n getBoundingSphere(target) {\\n this.getCenter(target.center);\\n target.radius = this.getSize(_vector2).length() * 0.5;\\n return target;\\n }\\n intersect(box) {\\n this.min.max(box.min);\\n this.max.min(box.max);\\n if (this.isEmpty())\\n this.makeEmpty();\\n return this;\\n }\\n union(box) {\\n this.min.min(box.min);\\n this.max.max(box.max);\\n return this;\\n }\\n applyMatrix4(matrix) {\\n if (this.isEmpty())\\n return this;\\n _points[0].set(this.min.x, this.min.y, this.min.z).applyMatrix4(matrix);\\n _points[1].set(this.min.x, this.min.y, this.max.z).applyMatrix4(matrix);\\n _points[2].set(this.min.x, this.max.y, this.min.z).applyMatrix4(matrix);\\n _points[3].set(this.min.x, this.max.y, this.max.z).applyMatrix4(matrix);\\n _points[4].set(this.max.x, this.min.y, this.min.z).applyMatrix4(matrix);\\n _points[5].set(this.max.x, this.min.y, this.max.z).applyMatrix4(matrix);\\n _points[6].set(this.max.x, this.max.y, this.min.z).applyMatrix4(matrix);\\n _points[7].set(this.max.x, this.max.y, this.max.z).applyMatrix4(matrix);\\n this.setFromPoints(_points);\\n return this;\\n }\\n translate(offset) {\\n this.min.add(offset);\\n this.max.add(offset);\\n return this;\\n }\\n equals(box) {\\n return box.min.equals(this.min) && box.max.equals(this.max);\\n }\\n};\\nBox3.prototype.isBox3 = true;\\nvar _points = [\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3(),\\n /* @__PURE__ */ new Vector3()\\n];\\nvar _vector2 = /* @__PURE__ */ new Vector3();\\nvar _box = /* @__PURE__ */ new Box3();\\nvar _v0 = /* @__PURE__ */ new Vector3();\\nvar _v13 = /* @__PURE__ */ new Vector3();\\nvar _v2 = /* @__PURE__ */ new Vector3();\\nvar _f0 = /* @__PURE__ */ new Vector3();\\nvar _f1 = /* @__PURE__ */ new Vector3();\\nvar _f2 = /* @__PURE__ */ new Vector3();\\nvar _center = /* @__PURE__ */ new Vector3();\\nvar _extents = /* @__PURE__ */ new Vector3();\\nvar _triangleNormal = /* @__PURE__ */ new Vector3();\\nvar _testAxis = /* @__PURE__ */ new Vector3();\\nfunction satForAxes(axes, v0, v1, v2, extents) {\\n for (let i = 0, j = axes.length - 3; i <= j; i += 3) {\\n _testAxis.fromArray(axes, i);\\n const r = extents.x * Math.abs(_testAxis.x) + extents.y * Math.abs(_testAxis.y) + extents.z * Math.abs(_testAxis.z);\\n const p0 = v0.dot(_testAxis);\\n const p1 = v1.dot(_testAxis);\\n const p2 = v2.dot(_testAxis);\\n if (Math.max(-Math.max(p0, p1, p2), Math.min(p0, p1, p2)) > r) {\\n return false;\\n }\\n }\\n return true;\\n}\\n\\n// ../../../node_modules/three/src/math/Sphere.js\\nvar _box2 = /* @__PURE__ */ new Box3();\\nvar _v14 = /* @__PURE__ */ new Vector3();\\nvar _toFarthestPoint = /* @__PURE__ */ new Vector3();\\nvar _toPoint = /* @__PURE__ */ new Vector3();\\nvar Sphere = class {\\n constructor(center = new Vector3(), radius = -1) {\\n this.center = center;\\n this.radius = radius;\\n }\\n set(center, radius) {\\n this.center.copy(center);\\n this.radius = radius;\\n return this;\\n }\\n setFromPoints(points2, optionalCenter) {\\n const center = this.center;\\n if (optionalCenter !== void 0) {\\n center.copy(optionalCenter);\\n } else {\\n _box2.setFromPoints(points2).getCenter(center);\\n }\\n let maxRadiusSq = 0;\\n for (let i = 0, il = points2.length; i < il; i++) {\\n maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(points2[i]));\\n }\\n this.radius = Math.sqrt(maxRadiusSq);\\n return this;\\n }\\n copy(sphere) {\\n this.center.copy(sphere.center);\\n this.radius = sphere.radius;\\n return this;\\n }\\n isEmpty() {\\n return this.radius < 0;\\n }\\n makeEmpty() {\\n this.center.set(0, 0, 0);\\n this.radius = -1;\\n return this;\\n }\\n containsPoint(point) {\\n return point.distanceToSquared(this.center) <= this.radius * this.radius;\\n }\\n distanceToPoint(point) {\\n return point.distanceTo(this.center) - this.radius;\\n }\\n intersectsSphere(sphere) {\\n const radiusSum = this.radius + sphere.radius;\\n return sphere.center.distanceToSquared(this.center) <= radiusSum * radiusSum;\\n }\\n intersectsBox(box) {\\n return box.intersectsSphere(this);\\n }\\n intersectsPlane(plane) {\\n return Math.abs(plane.distanceToPoint(this.center)) <= this.radius;\\n }\\n clampPoint(point, target) {\\n const deltaLengthSq = this.center.distanceToSquared(point);\\n target.copy(point);\\n if (deltaLengthSq > this.radius * this.radius) {\\n target.sub(this.center).normalize();\\n target.multiplyScalar(this.radius).add(this.center);\\n }\\n return target;\\n }\\n getBoundingBox(target) {\\n if (this.isEmpty()) {\\n target.makeEmpty();\\n return target;\\n }\\n target.set(this.center, this.center);\\n target.expandByScalar(this.radius);\\n return target;\\n }\\n applyMatrix4(matrix) {\\n this.center.applyMatrix4(matrix);\\n this.radius = this.radius * matrix.getMaxScaleOnAxis();\\n return this;\\n }\\n translate(offset) {\\n this.center.add(offset);\\n return this;\\n }\\n expandByPoint(point) {\\n _toPoint.subVectors(point, this.center);\\n const lengthSq = _toPoint.lengthSq();\\n if (lengthSq > this.radius * this.radius) {\\n const length = Math.sqrt(lengthSq);\\n const missingRadiusHalf = (length - this.radius) * 0.5;\\n this.center.add(_toPoint.multiplyScalar(missingRadiusHalf / length));\\n this.radius += missingRadiusHalf;\\n }\\n return this;\\n }\\n union(sphere) {\\n _toFarthestPoint.subVectors(sphere.center, this.center).normalize().multiplyScalar(sphere.radius);\\n this.expandByPoint(_v14.copy(sphere.center).add(_toFarthestPoint));\\n this.expandByPoint(_v14.copy(sphere.center).sub(_toFarthestPoint));\\n return this;\\n }\\n equals(sphere) {\\n return sphere.center.equals(this.center) && sphere.radius === this.radius;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n};\\n\\n// ../../../node_modules/three/src/math/Plane.js\\nvar _vector1 = /* @__PURE__ */ new Vector3();\\nvar _vector22 = /* @__PURE__ */ new Vector3();\\nvar _normalMatrix = /* @__PURE__ */ new Matrix3();\\nvar Plane = class {\\n constructor(normal = new Vector3(1, 0, 0), constant = 0) {\\n this.normal = normal;\\n this.constant = constant;\\n }\\n set(normal, constant) {\\n this.normal.copy(normal);\\n this.constant = constant;\\n return this;\\n }\\n setComponents(x, y, z, w) {\\n this.normal.set(x, y, z);\\n this.constant = w;\\n return this;\\n }\\n setFromNormalAndCoplanarPoint(normal, point) {\\n this.normal.copy(normal);\\n this.constant = -point.dot(this.normal);\\n return this;\\n }\\n setFromCoplanarPoints(a, b, c) {\\n const normal = _vector1.subVectors(c, b).cross(_vector22.subVectors(a, b)).normalize();\\n this.setFromNormalAndCoplanarPoint(normal, a);\\n return this;\\n }\\n copy(plane) {\\n this.normal.copy(plane.normal);\\n this.constant = plane.constant;\\n return this;\\n }\\n normalize() {\\n const inverseNormalLength = 1 / this.normal.length();\\n this.normal.multiplyScalar(inverseNormalLength);\\n this.constant *= inverseNormalLength;\\n return this;\\n }\\n negate() {\\n this.constant *= -1;\\n this.normal.negate();\\n return this;\\n }\\n distanceToPoint(point) {\\n return this.normal.dot(point) + this.constant;\\n }\\n distanceToSphere(sphere) {\\n return this.distanceToPoint(sphere.center) - sphere.radius;\\n }\\n projectPoint(point, target) {\\n return target.copy(this.normal).multiplyScalar(-this.distanceToPoint(point)).add(point);\\n }\\n intersectLine(line, target) {\\n const direction = line.delta(_vector1);\\n const denominator = this.normal.dot(direction);\\n if (denominator === 0) {\\n if (this.distanceToPoint(line.start) === 0) {\\n return target.copy(line.start);\\n }\\n return null;\\n }\\n const t = -(line.start.dot(this.normal) + this.constant) / denominator;\\n if (t < 0 || t > 1) {\\n return null;\\n }\\n return target.copy(direction).multiplyScalar(t).add(line.start);\\n }\\n intersectsLine(line) {\\n const startSign = this.distanceToPoint(line.start);\\n const endSign = this.distanceToPoint(line.end);\\n return startSign < 0 && endSign > 0 || endSign < 0 && startSign > 0;\\n }\\n intersectsBox(box) {\\n return box.intersectsPlane(this);\\n }\\n intersectsSphere(sphere) {\\n return sphere.intersectsPlane(this);\\n }\\n coplanarPoint(target) {\\n return target.copy(this.normal).multiplyScalar(-this.constant);\\n }\\n applyMatrix4(matrix, optionalNormalMatrix) {\\n const normalMatrix = optionalNormalMatrix || _normalMatrix.getNormalMatrix(matrix);\\n const referencePoint = this.coplanarPoint(_vector1).applyMatrix4(matrix);\\n const normal = this.normal.applyMatrix3(normalMatrix).normalize();\\n this.constant = -referencePoint.dot(normal);\\n return this;\\n }\\n translate(offset) {\\n this.constant -= offset.dot(this.normal);\\n return this;\\n }\\n equals(plane) {\\n return plane.normal.equals(this.normal) && plane.constant === this.constant;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n};\\nPlane.prototype.isPlane = true;\\n\\n// ../../../node_modules/three/src/math/Frustum.js\\nvar _sphere = /* @__PURE__ */ new Sphere();\\nvar _vector3 = /* @__PURE__ */ new Vector3();\\nvar Frustum = class {\\n constructor(p0 = new Plane(), p1 = new Plane(), p2 = new Plane(), p3 = new Plane(), p4 = new Plane(), p5 = new Plane()) {\\n this.planes = [p0, p1, p2, p3, p4, p5];\\n }\\n set(p0, p1, p2, p3, p4, p5) {\\n const planes = this.planes;\\n planes[0].copy(p0);\\n planes[1].copy(p1);\\n planes[2].copy(p2);\\n planes[3].copy(p3);\\n planes[4].copy(p4);\\n planes[5].copy(p5);\\n return this;\\n }\\n copy(frustum) {\\n const planes = this.planes;\\n for (let i = 0; i < 6; i++) {\\n planes[i].copy(frustum.planes[i]);\\n }\\n return this;\\n }\\n setFromProjectionMatrix(m) {\\n const planes = this.planes;\\n const me = m.elements;\\n const me0 = me[0], me1 = me[1], me2 = me[2], me3 = me[3];\\n const me4 = me[4], me5 = me[5], me6 = me[6], me7 = me[7];\\n const me8 = me[8], me9 = me[9], me10 = me[10], me11 = me[11];\\n const me12 = me[12], me13 = me[13], me14 = me[14], me15 = me[15];\\n planes[0].setComponents(me3 - me0, me7 - me4, me11 - me8, me15 - me12).normalize();\\n planes[1].setComponents(me3 + me0, me7 + me4, me11 + me8, me15 + me12).normalize();\\n planes[2].setComponents(me3 + me1, me7 + me5, me11 + me9, me15 + me13).normalize();\\n planes[3].setComponents(me3 - me1, me7 - me5, me11 - me9, me15 - me13).normalize();\\n planes[4].setComponents(me3 - me2, me7 - me6, me11 - me10, me15 - me14).normalize();\\n planes[5].setComponents(me3 + me2, me7 + me6, me11 + me10, me15 + me14).normalize();\\n return this;\\n }\\n intersectsObject(object) {\\n const geometry = object.geometry;\\n if (geometry.boundingSphere === null)\\n geometry.computeBoundingSphere();\\n _sphere.copy(geometry.boundingSphere).applyMatrix4(object.matrixWorld);\\n return this.intersectsSphere(_sphere);\\n }\\n intersectsSprite(sprite2) {\\n _sphere.center.set(0, 0, 0);\\n _sphere.radius = 0.7071067811865476;\\n _sphere.applyMatrix4(sprite2.matrixWorld);\\n return this.intersectsSphere(_sphere);\\n }\\n intersectsSphere(sphere) {\\n const planes = this.planes;\\n const center = sphere.center;\\n const negRadius = -sphere.radius;\\n for (let i = 0; i < 6; i++) {\\n const distance = planes[i].distanceToPoint(center);\\n if (distance < negRadius) {\\n return false;\\n }\\n }\\n return true;\\n }\\n intersectsBox(box) {\\n const planes = this.planes;\\n for (let i = 0; i < 6; i++) {\\n const plane = planes[i];\\n _vector3.x = plane.normal.x > 0 ? box.max.x : box.min.x;\\n _vector3.y = plane.normal.y > 0 ? box.max.y : box.min.y;\\n _vector3.z = plane.normal.z > 0 ? box.max.z : box.min.z;\\n if (plane.distanceToPoint(_vector3) < 0) {\\n return false;\\n }\\n }\\n return true;\\n }\\n containsPoint(point) {\\n const planes = this.planes;\\n for (let i = 0; i < 6; i++) {\\n if (planes[i].distanceToPoint(point) < 0) {\\n return false;\\n }\\n }\\n return true;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n};\\n\\n// ../../../node_modules/three/src/math/Vector4.js\\nvar Vector4 = class {\\n constructor(x = 0, y = 0, z = 0, w = 1) {\\n this.x = x;\\n this.y = y;\\n this.z = z;\\n this.w = w;\\n }\\n get width() {\\n return this.z;\\n }\\n set width(value) {\\n this.z = value;\\n }\\n get height() {\\n return this.w;\\n }\\n set height(value) {\\n this.w = value;\\n }\\n set(x, y, z, w) {\\n this.x = x;\\n this.y = y;\\n this.z = z;\\n this.w = w;\\n return this;\\n }\\n setScalar(scalar) {\\n this.x = scalar;\\n this.y = scalar;\\n this.z = scalar;\\n this.w = scalar;\\n return this;\\n }\\n setX(x) {\\n this.x = x;\\n return this;\\n }\\n setY(y) {\\n this.y = y;\\n return this;\\n }\\n setZ(z) {\\n this.z = z;\\n return this;\\n }\\n setW(w) {\\n this.w = w;\\n return this;\\n }\\n setComponent(index, value) {\\n switch (index) {\\n case 0:\\n this.x = value;\\n break;\\n case 1:\\n this.y = value;\\n break;\\n case 2:\\n this.z = value;\\n break;\\n case 3:\\n this.w = value;\\n break;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n return this;\\n }\\n getComponent(index) {\\n switch (index) {\\n case 0:\\n return this.x;\\n case 1:\\n return this.y;\\n case 2:\\n return this.z;\\n case 3:\\n return this.w;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n }\\n clone() {\\n return new this.constructor(this.x, this.y, this.z, this.w);\\n }\\n copy(v) {\\n this.x = v.x;\\n this.y = v.y;\\n this.z = v.z;\\n this.w = v.w !== void 0 ? v.w : 1;\\n return this;\\n }\\n add(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\");\\n return this.addVectors(v, w);\\n }\\n this.x += v.x;\\n this.y += v.y;\\n this.z += v.z;\\n this.w += v.w;\\n return this;\\n }\\n addScalar(s) {\\n this.x += s;\\n this.y += s;\\n this.z += s;\\n this.w += s;\\n return this;\\n }\\n addVectors(a, b) {\\n this.x = a.x + b.x;\\n this.y = a.y + b.y;\\n this.z = a.z + b.z;\\n this.w = a.w + b.w;\\n return this;\\n }\\n addScaledVector(v, s) {\\n this.x += v.x * s;\\n this.y += v.y * s;\\n this.z += v.z * s;\\n this.w += v.w * s;\\n return this;\\n }\\n sub(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\");\\n return this.subVectors(v, w);\\n }\\n this.x -= v.x;\\n this.y -= v.y;\\n this.z -= v.z;\\n this.w -= v.w;\\n return this;\\n }\\n subScalar(s) {\\n this.x -= s;\\n this.y -= s;\\n this.z -= s;\\n this.w -= s;\\n return this;\\n }\\n subVectors(a, b) {\\n this.x = a.x - b.x;\\n this.y = a.y - b.y;\\n this.z = a.z - b.z;\\n this.w = a.w - b.w;\\n return this;\\n }\\n multiply(v) {\\n this.x *= v.x;\\n this.y *= v.y;\\n this.z *= v.z;\\n this.w *= v.w;\\n return this;\\n }\\n multiplyScalar(scalar) {\\n this.x *= scalar;\\n this.y *= scalar;\\n this.z *= scalar;\\n this.w *= scalar;\\n return this;\\n }\\n applyMatrix4(m) {\\n const x = this.x, y = this.y, z = this.z, w = this.w;\\n const e = m.elements;\\n this.x = e[0] * x + e[4] * y + e[8] * z + e[12] * w;\\n this.y = e[1] * x + e[5] * y + e[9] * z + e[13] * w;\\n this.z = e[2] * x + e[6] * y + e[10] * z + e[14] * w;\\n this.w = e[3] * x + e[7] * y + e[11] * z + e[15] * w;\\n return this;\\n }\\n divideScalar(scalar) {\\n return this.multiplyScalar(1 / scalar);\\n }\\n setAxisAngleFromQuaternion(q) {\\n this.w = 2 * Math.acos(q.w);\\n const s = Math.sqrt(1 - q.w * q.w);\\n if (s < 1e-4) {\\n this.x = 1;\\n this.y = 0;\\n this.z = 0;\\n } else {\\n this.x = q.x / s;\\n this.y = q.y / s;\\n this.z = q.z / s;\\n }\\n return this;\\n }\\n setAxisAngleFromRotationMatrix(m) {\\n let angle, x, y, z;\\n const epsilon = 0.01, epsilon2 = 0.1, te = m.elements, m11 = te[0], m12 = te[4], m13 = te[8], m21 = te[1], m22 = te[5], m23 = te[9], m31 = te[2], m32 = te[6], m33 = te[10];\\n if (Math.abs(m12 - m21) < epsilon && Math.abs(m13 - m31) < epsilon && Math.abs(m23 - m32) < epsilon) {\\n if (Math.abs(m12 + m21) < epsilon2 && Math.abs(m13 + m31) < epsilon2 && Math.abs(m23 + m32) < epsilon2 && Math.abs(m11 + m22 + m33 - 3) < epsilon2) {\\n this.set(1, 0, 0, 0);\\n return this;\\n }\\n angle = Math.PI;\\n const xx = (m11 + 1) / 2;\\n const yy = (m22 + 1) / 2;\\n const zz = (m33 + 1) / 2;\\n const xy = (m12 + m21) / 4;\\n const xz = (m13 + m31) / 4;\\n const yz = (m23 + m32) / 4;\\n if (xx > yy && xx > zz) {\\n if (xx < epsilon) {\\n x = 0;\\n y = 0.707106781;\\n z = 0.707106781;\\n } else {\\n x = Math.sqrt(xx);\\n y = xy / x;\\n z = xz / x;\\n }\\n } else if (yy > zz) {\\n if (yy < epsilon) {\\n x = 0.707106781;\\n y = 0;\\n z = 0.707106781;\\n } else {\\n y = Math.sqrt(yy);\\n x = xy / y;\\n z = yz / y;\\n }\\n } else {\\n if (zz < epsilon) {\\n x = 0.707106781;\\n y = 0.707106781;\\n z = 0;\\n } else {\\n z = Math.sqrt(zz);\\n x = xz / z;\\n y = yz / z;\\n }\\n }\\n this.set(x, y, z, angle);\\n return this;\\n }\\n let s = Math.sqrt((m32 - m23) * (m32 - m23) + (m13 - m31) * (m13 - m31) + (m21 - m12) * (m21 - m12));\\n if (Math.abs(s) < 1e-3)\\n s = 1;\\n this.x = (m32 - m23) / s;\\n this.y = (m13 - m31) / s;\\n this.z = (m21 - m12) / s;\\n this.w = Math.acos((m11 + m22 + m33 - 1) / 2);\\n return this;\\n }\\n min(v) {\\n this.x = Math.min(this.x, v.x);\\n this.y = Math.min(this.y, v.y);\\n this.z = Math.min(this.z, v.z);\\n this.w = Math.min(this.w, v.w);\\n return this;\\n }\\n max(v) {\\n this.x = Math.max(this.x, v.x);\\n this.y = Math.max(this.y, v.y);\\n this.z = Math.max(this.z, v.z);\\n this.w = Math.max(this.w, v.w);\\n return this;\\n }\\n clamp(min, max) {\\n this.x = Math.max(min.x, Math.min(max.x, this.x));\\n this.y = Math.max(min.y, Math.min(max.y, this.y));\\n this.z = Math.max(min.z, Math.min(max.z, this.z));\\n this.w = Math.max(min.w, Math.min(max.w, this.w));\\n return this;\\n }\\n clampScalar(minVal, maxVal) {\\n this.x = Math.max(minVal, Math.min(maxVal, this.x));\\n this.y = Math.max(minVal, Math.min(maxVal, this.y));\\n this.z = Math.max(minVal, Math.min(maxVal, this.z));\\n this.w = Math.max(minVal, Math.min(maxVal, this.w));\\n return this;\\n }\\n clampLength(min, max) {\\n const length = this.length();\\n return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max, length)));\\n }\\n floor() {\\n this.x = Math.floor(this.x);\\n this.y = Math.floor(this.y);\\n this.z = Math.floor(this.z);\\n this.w = Math.floor(this.w);\\n return this;\\n }\\n ceil() {\\n this.x = Math.ceil(this.x);\\n this.y = Math.ceil(this.y);\\n this.z = Math.ceil(this.z);\\n this.w = Math.ceil(this.w);\\n return this;\\n }\\n round() {\\n this.x = Math.round(this.x);\\n this.y = Math.round(this.y);\\n this.z = Math.round(this.z);\\n this.w = Math.round(this.w);\\n return this;\\n }\\n roundToZero() {\\n this.x = this.x < 0 ? Math.ceil(this.x) : Math.floor(this.x);\\n this.y = this.y < 0 ? Math.ceil(this.y) : Math.floor(this.y);\\n this.z = this.z < 0 ? Math.ceil(this.z) : Math.floor(this.z);\\n this.w = this.w < 0 ? Math.ceil(this.w) : Math.floor(this.w);\\n return this;\\n }\\n negate() {\\n this.x = -this.x;\\n this.y = -this.y;\\n this.z = -this.z;\\n this.w = -this.w;\\n return this;\\n }\\n dot(v) {\\n return this.x * v.x + this.y * v.y + this.z * v.z + this.w * v.w;\\n }\\n lengthSq() {\\n return this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w;\\n }\\n length() {\\n return Math.sqrt(this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w);\\n }\\n manhattanLength() {\\n return Math.abs(this.x) + Math.abs(this.y) + Math.abs(this.z) + Math.abs(this.w);\\n }\\n normalize() {\\n return this.divideScalar(this.length() || 1);\\n }\\n setLength(length) {\\n return this.normalize().multiplyScalar(length);\\n }\\n lerp(v, alpha) {\\n this.x += (v.x - this.x) * alpha;\\n this.y += (v.y - this.y) * alpha;\\n this.z += (v.z - this.z) * alpha;\\n this.w += (v.w - this.w) * alpha;\\n return this;\\n }\\n lerpVectors(v1, v2, alpha) {\\n this.x = v1.x + (v2.x - v1.x) * alpha;\\n this.y = v1.y + (v2.y - v1.y) * alpha;\\n this.z = v1.z + (v2.z - v1.z) * alpha;\\n this.w = v1.w + (v2.w - v1.w) * alpha;\\n return this;\\n }\\n equals(v) {\\n return v.x === this.x && v.y === this.y && v.z === this.z && v.w === this.w;\\n }\\n fromArray(array, offset = 0) {\\n this.x = array[offset];\\n this.y = array[offset + 1];\\n this.z = array[offset + 2];\\n this.w = array[offset + 3];\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this.x;\\n array[offset + 1] = this.y;\\n array[offset + 2] = this.z;\\n array[offset + 3] = this.w;\\n return array;\\n }\\n fromBufferAttribute(attribute, index, offset) {\\n if (offset !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector4: offset has been removed from .fromBufferAttribute().\\\\\\\");\\n }\\n this.x = attribute.getX(index);\\n this.y = attribute.getY(index);\\n this.z = attribute.getZ(index);\\n this.w = attribute.getW(index);\\n return this;\\n }\\n random() {\\n this.x = Math.random();\\n this.y = Math.random();\\n this.z = Math.random();\\n this.w = Math.random();\\n return this;\\n }\\n *[Symbol.iterator]() {\\n yield this.x;\\n yield this.y;\\n yield this.z;\\n yield this.w;\\n }\\n};\\nVector4.prototype.isVector4 = true;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLAnimation.js\\nfunction WebGLAnimation() {\\n let context = null;\\n let isAnimating = false;\\n let animationLoop = null;\\n let requestId = null;\\n function onAnimationFrame(time, frame) {\\n animationLoop(time, frame);\\n requestId = context.requestAnimationFrame(onAnimationFrame);\\n }\\n return {\\n start: function() {\\n if (isAnimating === true)\\n return;\\n if (animationLoop === null)\\n return;\\n requestId = context.requestAnimationFrame(onAnimationFrame);\\n isAnimating = true;\\n },\\n stop: function() {\\n context.cancelAnimationFrame(requestId);\\n isAnimating = false;\\n },\\n setAnimationLoop: function(callback) {\\n animationLoop = callback;\\n },\\n setContext: function(value) {\\n context = value;\\n }\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLAttributes.js\\nfunction WebGLAttributes(gl, capabilities) {\\n const isWebGL2 = capabilities.isWebGL2;\\n const buffers = new WeakMap();\\n function createBuffer(attribute, bufferType) {\\n const array = attribute.array;\\n const usage = attribute.usage;\\n const buffer = gl.createBuffer();\\n gl.bindBuffer(bufferType, buffer);\\n gl.bufferData(bufferType, array, usage);\\n attribute.onUploadCallback();\\n let type = gl.FLOAT;\\n if (array instanceof Float32Array) {\\n type = gl.FLOAT;\\n } else if (array instanceof Float64Array) {\\n console.warn(\\\\\\\"THREE.WebGLAttributes: Unsupported data buffer format: Float64Array.\\\\\\\");\\n } else if (array instanceof Uint16Array) {\\n if (attribute.isFloat16BufferAttribute) {\\n if (isWebGL2) {\\n type = gl.HALF_FLOAT;\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.\\\\\\\");\\n }\\n } else {\\n type = gl.UNSIGNED_SHORT;\\n }\\n } else if (array instanceof Int16Array) {\\n type = gl.SHORT;\\n } else if (array instanceof Uint32Array) {\\n type = gl.UNSIGNED_INT;\\n } else if (array instanceof Int32Array) {\\n type = gl.INT;\\n } else if (array instanceof Int8Array) {\\n type = gl.BYTE;\\n } else if (array instanceof Uint8Array) {\\n type = gl.UNSIGNED_BYTE;\\n } else if (array instanceof Uint8ClampedArray) {\\n type = gl.UNSIGNED_BYTE;\\n }\\n return {\\n buffer,\\n type,\\n bytesPerElement: array.BYTES_PER_ELEMENT,\\n version: attribute.version\\n };\\n }\\n function updateBuffer(buffer, attribute, bufferType) {\\n const array = attribute.array;\\n const updateRange = attribute.updateRange;\\n gl.bindBuffer(bufferType, buffer);\\n if (updateRange.count === -1) {\\n gl.bufferSubData(bufferType, 0, array);\\n } else {\\n if (isWebGL2) {\\n gl.bufferSubData(bufferType, updateRange.offset * array.BYTES_PER_ELEMENT, array, updateRange.offset, updateRange.count);\\n } else {\\n gl.bufferSubData(bufferType, updateRange.offset * array.BYTES_PER_ELEMENT, array.subarray(updateRange.offset, updateRange.offset + updateRange.count));\\n }\\n updateRange.count = -1;\\n }\\n }\\n function get(attribute) {\\n if (attribute.isInterleavedBufferAttribute)\\n attribute = attribute.data;\\n return buffers.get(attribute);\\n }\\n function remove(attribute) {\\n if (attribute.isInterleavedBufferAttribute)\\n attribute = attribute.data;\\n const data = buffers.get(attribute);\\n if (data) {\\n gl.deleteBuffer(data.buffer);\\n buffers.delete(attribute);\\n }\\n }\\n function update(attribute, bufferType) {\\n if (attribute.isGLBufferAttribute) {\\n const cached = buffers.get(attribute);\\n if (!cached || cached.version < attribute.version) {\\n buffers.set(attribute, {\\n buffer: attribute.buffer,\\n type: attribute.type,\\n bytesPerElement: attribute.elementSize,\\n version: attribute.version\\n });\\n }\\n return;\\n }\\n if (attribute.isInterleavedBufferAttribute)\\n attribute = attribute.data;\\n const data = buffers.get(attribute);\\n if (data === void 0) {\\n buffers.set(attribute, createBuffer(attribute, bufferType));\\n } else if (data.version < attribute.version) {\\n updateBuffer(data.buffer, attribute, bufferType);\\n data.version = attribute.version;\\n }\\n }\\n return {\\n get,\\n remove,\\n update\\n };\\n}\\n\\n// ../../../node_modules/three/src/math/Vector2.js\\nvar Vector2 = class {\\n constructor(x = 0, y = 0) {\\n this.x = x;\\n this.y = y;\\n }\\n get width() {\\n return this.x;\\n }\\n set width(value) {\\n this.x = value;\\n }\\n get height() {\\n return this.y;\\n }\\n set height(value) {\\n this.y = value;\\n }\\n set(x, y) {\\n this.x = x;\\n this.y = y;\\n return this;\\n }\\n setScalar(scalar) {\\n this.x = scalar;\\n this.y = scalar;\\n return this;\\n }\\n setX(x) {\\n this.x = x;\\n return this;\\n }\\n setY(y) {\\n this.y = y;\\n return this;\\n }\\n setComponent(index, value) {\\n switch (index) {\\n case 0:\\n this.x = value;\\n break;\\n case 1:\\n this.y = value;\\n break;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n return this;\\n }\\n getComponent(index) {\\n switch (index) {\\n case 0:\\n return this.x;\\n case 1:\\n return this.y;\\n default:\\n throw new Error(\\\\\\\"index is out of range: \\\\\\\" + index);\\n }\\n }\\n clone() {\\n return new this.constructor(this.x, this.y);\\n }\\n copy(v) {\\n this.x = v.x;\\n this.y = v.y;\\n return this;\\n }\\n add(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\");\\n return this.addVectors(v, w);\\n }\\n this.x += v.x;\\n this.y += v.y;\\n return this;\\n }\\n addScalar(s) {\\n this.x += s;\\n this.y += s;\\n return this;\\n }\\n addVectors(a, b) {\\n this.x = a.x + b.x;\\n this.y = a.y + b.y;\\n return this;\\n }\\n addScaledVector(v, s) {\\n this.x += v.x * s;\\n this.y += v.y * s;\\n return this;\\n }\\n sub(v, w) {\\n if (w !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\");\\n return this.subVectors(v, w);\\n }\\n this.x -= v.x;\\n this.y -= v.y;\\n return this;\\n }\\n subScalar(s) {\\n this.x -= s;\\n this.y -= s;\\n return this;\\n }\\n subVectors(a, b) {\\n this.x = a.x - b.x;\\n this.y = a.y - b.y;\\n return this;\\n }\\n multiply(v) {\\n this.x *= v.x;\\n this.y *= v.y;\\n return this;\\n }\\n multiplyScalar(scalar) {\\n this.x *= scalar;\\n this.y *= scalar;\\n return this;\\n }\\n divide(v) {\\n this.x /= v.x;\\n this.y /= v.y;\\n return this;\\n }\\n divideScalar(scalar) {\\n return this.multiplyScalar(1 / scalar);\\n }\\n applyMatrix3(m) {\\n const x = this.x, y = this.y;\\n const e = m.elements;\\n this.x = e[0] * x + e[3] * y + e[6];\\n this.y = e[1] * x + e[4] * y + e[7];\\n return this;\\n }\\n min(v) {\\n this.x = Math.min(this.x, v.x);\\n this.y = Math.min(this.y, v.y);\\n return this;\\n }\\n max(v) {\\n this.x = Math.max(this.x, v.x);\\n this.y = Math.max(this.y, v.y);\\n return this;\\n }\\n clamp(min, max) {\\n this.x = Math.max(min.x, Math.min(max.x, this.x));\\n this.y = Math.max(min.y, Math.min(max.y, this.y));\\n return this;\\n }\\n clampScalar(minVal, maxVal) {\\n this.x = Math.max(minVal, Math.min(maxVal, this.x));\\n this.y = Math.max(minVal, Math.min(maxVal, this.y));\\n return this;\\n }\\n clampLength(min, max) {\\n const length = this.length();\\n return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max, length)));\\n }\\n floor() {\\n this.x = Math.floor(this.x);\\n this.y = Math.floor(this.y);\\n return this;\\n }\\n ceil() {\\n this.x = Math.ceil(this.x);\\n this.y = Math.ceil(this.y);\\n return this;\\n }\\n round() {\\n this.x = Math.round(this.x);\\n this.y = Math.round(this.y);\\n return this;\\n }\\n roundToZero() {\\n this.x = this.x < 0 ? Math.ceil(this.x) : Math.floor(this.x);\\n this.y = this.y < 0 ? Math.ceil(this.y) : Math.floor(this.y);\\n return this;\\n }\\n negate() {\\n this.x = -this.x;\\n this.y = -this.y;\\n return this;\\n }\\n dot(v) {\\n return this.x * v.x + this.y * v.y;\\n }\\n cross(v) {\\n return this.x * v.y - this.y * v.x;\\n }\\n lengthSq() {\\n return this.x * this.x + this.y * this.y;\\n }\\n length() {\\n return Math.sqrt(this.x * this.x + this.y * this.y);\\n }\\n manhattanLength() {\\n return Math.abs(this.x) + Math.abs(this.y);\\n }\\n normalize() {\\n return this.divideScalar(this.length() || 1);\\n }\\n angle() {\\n const angle = Math.atan2(-this.y, -this.x) + Math.PI;\\n return angle;\\n }\\n distanceTo(v) {\\n return Math.sqrt(this.distanceToSquared(v));\\n }\\n distanceToSquared(v) {\\n const dx = this.x - v.x, dy = this.y - v.y;\\n return dx * dx + dy * dy;\\n }\\n manhattanDistanceTo(v) {\\n return Math.abs(this.x - v.x) + Math.abs(this.y - v.y);\\n }\\n setLength(length) {\\n return this.normalize().multiplyScalar(length);\\n }\\n lerp(v, alpha) {\\n this.x += (v.x - this.x) * alpha;\\n this.y += (v.y - this.y) * alpha;\\n return this;\\n }\\n lerpVectors(v1, v2, alpha) {\\n this.x = v1.x + (v2.x - v1.x) * alpha;\\n this.y = v1.y + (v2.y - v1.y) * alpha;\\n return this;\\n }\\n equals(v) {\\n return v.x === this.x && v.y === this.y;\\n }\\n fromArray(array, offset = 0) {\\n this.x = array[offset];\\n this.y = array[offset + 1];\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this.x;\\n array[offset + 1] = this.y;\\n return array;\\n }\\n fromBufferAttribute(attribute, index, offset) {\\n if (offset !== void 0) {\\n console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\");\\n }\\n this.x = attribute.getX(index);\\n this.y = attribute.getY(index);\\n return this;\\n }\\n rotateAround(center, angle) {\\n const c = Math.cos(angle), s = Math.sin(angle);\\n const x = this.x - center.x;\\n const y = this.y - center.y;\\n this.x = x * c - y * s + center.x;\\n this.y = x * s + y * c + center.y;\\n return this;\\n }\\n random() {\\n this.x = Math.random();\\n this.y = Math.random();\\n return this;\\n }\\n *[Symbol.iterator]() {\\n yield this.x;\\n yield this.y;\\n }\\n};\\nVector2.prototype.isVector2 = true;\\n\\n// ../../../node_modules/three/src/math/Color.js\\nvar _colorKeywords = {\\n aliceblue: 15792383,\\n antiquewhite: 16444375,\\n aqua: 65535,\\n aquamarine: 8388564,\\n azure: 15794175,\\n beige: 16119260,\\n bisque: 16770244,\\n black: 0,\\n blanchedalmond: 16772045,\\n blue: 255,\\n blueviolet: 9055202,\\n brown: 10824234,\\n burlywood: 14596231,\\n cadetblue: 6266528,\\n chartreuse: 8388352,\\n chocolate: 13789470,\\n coral: 16744272,\\n cornflowerblue: 6591981,\\n cornsilk: 16775388,\\n crimson: 14423100,\\n cyan: 65535,\\n darkblue: 139,\\n darkcyan: 35723,\\n darkgoldenrod: 12092939,\\n darkgray: 11119017,\\n darkgreen: 25600,\\n darkgrey: 11119017,\\n darkkhaki: 12433259,\\n darkmagenta: 9109643,\\n darkolivegreen: 5597999,\\n darkorange: 16747520,\\n darkorchid: 10040012,\\n darkred: 9109504,\\n darksalmon: 15308410,\\n darkseagreen: 9419919,\\n darkslateblue: 4734347,\\n darkslategray: 3100495,\\n darkslategrey: 3100495,\\n darkturquoise: 52945,\\n darkviolet: 9699539,\\n deeppink: 16716947,\\n deepskyblue: 49151,\\n dimgray: 6908265,\\n dimgrey: 6908265,\\n dodgerblue: 2003199,\\n firebrick: 11674146,\\n floralwhite: 16775920,\\n forestgreen: 2263842,\\n fuchsia: 16711935,\\n gainsboro: 14474460,\\n ghostwhite: 16316671,\\n gold: 16766720,\\n goldenrod: 14329120,\\n gray: 8421504,\\n green: 32768,\\n greenyellow: 11403055,\\n grey: 8421504,\\n honeydew: 15794160,\\n hotpink: 16738740,\\n indianred: 13458524,\\n indigo: 4915330,\\n ivory: 16777200,\\n khaki: 15787660,\\n lavender: 15132410,\\n lavenderblush: 16773365,\\n lawngreen: 8190976,\\n lemonchiffon: 16775885,\\n lightblue: 11393254,\\n lightcoral: 15761536,\\n lightcyan: 14745599,\\n lightgoldenrodyellow: 16448210,\\n lightgray: 13882323,\\n lightgreen: 9498256,\\n lightgrey: 13882323,\\n lightpink: 16758465,\\n lightsalmon: 16752762,\\n lightseagreen: 2142890,\\n lightskyblue: 8900346,\\n lightslategray: 7833753,\\n lightslategrey: 7833753,\\n lightsteelblue: 11584734,\\n lightyellow: 16777184,\\n lime: 65280,\\n limegreen: 3329330,\\n linen: 16445670,\\n magenta: 16711935,\\n maroon: 8388608,\\n mediumaquamarine: 6737322,\\n mediumblue: 205,\\n mediumorchid: 12211667,\\n mediumpurple: 9662683,\\n mediumseagreen: 3978097,\\n mediumslateblue: 8087790,\\n mediumspringgreen: 64154,\\n mediumturquoise: 4772300,\\n mediumvioletred: 13047173,\\n midnightblue: 1644912,\\n mintcream: 16121850,\\n mistyrose: 16770273,\\n moccasin: 16770229,\\n navajowhite: 16768685,\\n navy: 128,\\n oldlace: 16643558,\\n olive: 8421376,\\n olivedrab: 7048739,\\n orange: 16753920,\\n orangered: 16729344,\\n orchid: 14315734,\\n palegoldenrod: 15657130,\\n palegreen: 10025880,\\n paleturquoise: 11529966,\\n palevioletred: 14381203,\\n papayawhip: 16773077,\\n peachpuff: 16767673,\\n peru: 13468991,\\n pink: 16761035,\\n plum: 14524637,\\n powderblue: 11591910,\\n purple: 8388736,\\n rebeccapurple: 6697881,\\n red: 16711680,\\n rosybrown: 12357519,\\n royalblue: 4286945,\\n saddlebrown: 9127187,\\n salmon: 16416882,\\n sandybrown: 16032864,\\n seagreen: 3050327,\\n seashell: 16774638,\\n sienna: 10506797,\\n silver: 12632256,\\n skyblue: 8900331,\\n slateblue: 6970061,\\n slategray: 7372944,\\n slategrey: 7372944,\\n snow: 16775930,\\n springgreen: 65407,\\n steelblue: 4620980,\\n tan: 13808780,\\n teal: 32896,\\n thistle: 14204888,\\n tomato: 16737095,\\n turquoise: 4251856,\\n violet: 15631086,\\n wheat: 16113331,\\n white: 16777215,\\n whitesmoke: 16119285,\\n yellow: 16776960,\\n yellowgreen: 10145074\\n};\\nvar _hslA = {h: 0, s: 0, l: 0};\\nvar _hslB = {h: 0, s: 0, l: 0};\\nfunction hue2rgb(p, q, t) {\\n if (t < 0)\\n t += 1;\\n if (t > 1)\\n t -= 1;\\n if (t < 1 / 6)\\n return p + (q - p) * 6 * t;\\n if (t < 1 / 2)\\n return q;\\n if (t < 2 / 3)\\n return p + (q - p) * 6 * (2 / 3 - t);\\n return p;\\n}\\nfunction SRGBToLinear(c) {\\n return c < 0.04045 ? c * 0.0773993808 : Math.pow(c * 0.9478672986 + 0.0521327014, 2.4);\\n}\\nfunction LinearToSRGB(c) {\\n return c < 31308e-7 ? c * 12.92 : 1.055 * Math.pow(c, 0.41666) - 0.055;\\n}\\nvar Color = class {\\n constructor(r, g, b) {\\n if (g === void 0 && b === void 0) {\\n return this.set(r);\\n }\\n return this.setRGB(r, g, b);\\n }\\n set(value) {\\n if (value && value.isColor) {\\n this.copy(value);\\n } else if (typeof value === \\\\\\\"number\\\\\\\") {\\n this.setHex(value);\\n } else if (typeof value === \\\\\\\"string\\\\\\\") {\\n this.setStyle(value);\\n }\\n return this;\\n }\\n setScalar(scalar) {\\n this.r = scalar;\\n this.g = scalar;\\n this.b = scalar;\\n return this;\\n }\\n setHex(hex) {\\n hex = Math.floor(hex);\\n this.r = (hex >> 16 & 255) / 255;\\n this.g = (hex >> 8 & 255) / 255;\\n this.b = (hex & 255) / 255;\\n return this;\\n }\\n setRGB(r, g, b) {\\n this.r = r;\\n this.g = g;\\n this.b = b;\\n return this;\\n }\\n setHSL(h, s, l) {\\n h = euclideanModulo(h, 1);\\n s = clamp(s, 0, 1);\\n l = clamp(l, 0, 1);\\n if (s === 0) {\\n this.r = this.g = this.b = l;\\n } else {\\n const p = l <= 0.5 ? l * (1 + s) : l + s - l * s;\\n const q = 2 * l - p;\\n this.r = hue2rgb(q, p, h + 1 / 3);\\n this.g = hue2rgb(q, p, h);\\n this.b = hue2rgb(q, p, h - 1 / 3);\\n }\\n return this;\\n }\\n setStyle(style) {\\n function handleAlpha(string) {\\n if (string === void 0)\\n return;\\n if (parseFloat(string) < 1) {\\n console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\" + style + \\\\\\\" will be ignored.\\\\\\\");\\n }\\n }\\n let m;\\n if (m = /^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(style)) {\\n let color;\\n const name = m[1];\\n const components = m[2];\\n switch (name) {\\n case \\\\\\\"rgb\\\\\\\":\\n case \\\\\\\"rgba\\\\\\\":\\n if (color = /^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(components)) {\\n this.r = Math.min(255, parseInt(color[1], 10)) / 255;\\n this.g = Math.min(255, parseInt(color[2], 10)) / 255;\\n this.b = Math.min(255, parseInt(color[3], 10)) / 255;\\n handleAlpha(color[4]);\\n return this;\\n }\\n if (color = /^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(components)) {\\n this.r = Math.min(100, parseInt(color[1], 10)) / 100;\\n this.g = Math.min(100, parseInt(color[2], 10)) / 100;\\n this.b = Math.min(100, parseInt(color[3], 10)) / 100;\\n handleAlpha(color[4]);\\n return this;\\n }\\n break;\\n case \\\\\\\"hsl\\\\\\\":\\n case \\\\\\\"hsla\\\\\\\":\\n if (color = /^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(components)) {\\n const h = parseFloat(color[1]) / 360;\\n const s = parseInt(color[2], 10) / 100;\\n const l = parseInt(color[3], 10) / 100;\\n handleAlpha(color[4]);\\n return this.setHSL(h, s, l);\\n }\\n break;\\n }\\n } else if (m = /^\\\\#([A-Fa-f\\\\d]+)$/.exec(style)) {\\n const hex = m[1];\\n const size = hex.length;\\n if (size === 3) {\\n this.r = parseInt(hex.charAt(0) + hex.charAt(0), 16) / 255;\\n this.g = parseInt(hex.charAt(1) + hex.charAt(1), 16) / 255;\\n this.b = parseInt(hex.charAt(2) + hex.charAt(2), 16) / 255;\\n return this;\\n } else if (size === 6) {\\n this.r = parseInt(hex.charAt(0) + hex.charAt(1), 16) / 255;\\n this.g = parseInt(hex.charAt(2) + hex.charAt(3), 16) / 255;\\n this.b = parseInt(hex.charAt(4) + hex.charAt(5), 16) / 255;\\n return this;\\n }\\n }\\n if (style && style.length > 0) {\\n return this.setColorName(style);\\n }\\n return this;\\n }\\n setColorName(style) {\\n const hex = _colorKeywords[style.toLowerCase()];\\n if (hex !== void 0) {\\n this.setHex(hex);\\n } else {\\n console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\" + style);\\n }\\n return this;\\n }\\n clone() {\\n return new this.constructor(this.r, this.g, this.b);\\n }\\n copy(color) {\\n this.r = color.r;\\n this.g = color.g;\\n this.b = color.b;\\n return this;\\n }\\n copyGammaToLinear(color, gammaFactor = 2) {\\n this.r = Math.pow(color.r, gammaFactor);\\n this.g = Math.pow(color.g, gammaFactor);\\n this.b = Math.pow(color.b, gammaFactor);\\n return this;\\n }\\n copyLinearToGamma(color, gammaFactor = 2) {\\n const safeInverse = gammaFactor > 0 ? 1 / gammaFactor : 1;\\n this.r = Math.pow(color.r, safeInverse);\\n this.g = Math.pow(color.g, safeInverse);\\n this.b = Math.pow(color.b, safeInverse);\\n return this;\\n }\\n convertGammaToLinear(gammaFactor) {\\n this.copyGammaToLinear(this, gammaFactor);\\n return this;\\n }\\n convertLinearToGamma(gammaFactor) {\\n this.copyLinearToGamma(this, gammaFactor);\\n return this;\\n }\\n copySRGBToLinear(color) {\\n this.r = SRGBToLinear(color.r);\\n this.g = SRGBToLinear(color.g);\\n this.b = SRGBToLinear(color.b);\\n return this;\\n }\\n copyLinearToSRGB(color) {\\n this.r = LinearToSRGB(color.r);\\n this.g = LinearToSRGB(color.g);\\n this.b = LinearToSRGB(color.b);\\n return this;\\n }\\n convertSRGBToLinear() {\\n this.copySRGBToLinear(this);\\n return this;\\n }\\n convertLinearToSRGB() {\\n this.copyLinearToSRGB(this);\\n return this;\\n }\\n getHex() {\\n return this.r * 255 << 16 ^ this.g * 255 << 8 ^ this.b * 255 << 0;\\n }\\n getHexString() {\\n return (\\\\\\\"000000\\\\\\\" + this.getHex().toString(16)).slice(-6);\\n }\\n getHSL(target) {\\n const r = this.r, g = this.g, b = this.b;\\n const max = Math.max(r, g, b);\\n const min = Math.min(r, g, b);\\n let hue, saturation;\\n const lightness = (min + max) / 2;\\n if (min === max) {\\n hue = 0;\\n saturation = 0;\\n } else {\\n const delta = max - min;\\n saturation = lightness <= 0.5 ? delta / (max + min) : delta / (2 - max - min);\\n switch (max) {\\n case r:\\n hue = (g - b) / delta + (g < b ? 6 : 0);\\n break;\\n case g:\\n hue = (b - r) / delta + 2;\\n break;\\n case b:\\n hue = (r - g) / delta + 4;\\n break;\\n }\\n hue /= 6;\\n }\\n target.h = hue;\\n target.s = saturation;\\n target.l = lightness;\\n return target;\\n }\\n getStyle() {\\n return \\\\\\\"rgb(\\\\\\\" + (this.r * 255 | 0) + \\\\\\\",\\\\\\\" + (this.g * 255 | 0) + \\\\\\\",\\\\\\\" + (this.b * 255 | 0) + \\\\\\\")\\\\\\\";\\n }\\n offsetHSL(h, s, l) {\\n this.getHSL(_hslA);\\n _hslA.h += h;\\n _hslA.s += s;\\n _hslA.l += l;\\n this.setHSL(_hslA.h, _hslA.s, _hslA.l);\\n return this;\\n }\\n add(color) {\\n this.r += color.r;\\n this.g += color.g;\\n this.b += color.b;\\n return this;\\n }\\n addColors(color1, color2) {\\n this.r = color1.r + color2.r;\\n this.g = color1.g + color2.g;\\n this.b = color1.b + color2.b;\\n return this;\\n }\\n addScalar(s) {\\n this.r += s;\\n this.g += s;\\n this.b += s;\\n return this;\\n }\\n sub(color) {\\n this.r = Math.max(0, this.r - color.r);\\n this.g = Math.max(0, this.g - color.g);\\n this.b = Math.max(0, this.b - color.b);\\n return this;\\n }\\n multiply(color) {\\n this.r *= color.r;\\n this.g *= color.g;\\n this.b *= color.b;\\n return this;\\n }\\n multiplyScalar(s) {\\n this.r *= s;\\n this.g *= s;\\n this.b *= s;\\n return this;\\n }\\n lerp(color, alpha) {\\n this.r += (color.r - this.r) * alpha;\\n this.g += (color.g - this.g) * alpha;\\n this.b += (color.b - this.b) * alpha;\\n return this;\\n }\\n lerpColors(color1, color2, alpha) {\\n this.r = color1.r + (color2.r - color1.r) * alpha;\\n this.g = color1.g + (color2.g - color1.g) * alpha;\\n this.b = color1.b + (color2.b - color1.b) * alpha;\\n return this;\\n }\\n lerpHSL(color, alpha) {\\n this.getHSL(_hslA);\\n color.getHSL(_hslB);\\n const h = lerp(_hslA.h, _hslB.h, alpha);\\n const s = lerp(_hslA.s, _hslB.s, alpha);\\n const l = lerp(_hslA.l, _hslB.l, alpha);\\n this.setHSL(h, s, l);\\n return this;\\n }\\n equals(c) {\\n return c.r === this.r && c.g === this.g && c.b === this.b;\\n }\\n fromArray(array, offset = 0) {\\n this.r = array[offset];\\n this.g = array[offset + 1];\\n this.b = array[offset + 2];\\n return this;\\n }\\n toArray(array = [], offset = 0) {\\n array[offset] = this.r;\\n array[offset + 1] = this.g;\\n array[offset + 2] = this.b;\\n return array;\\n }\\n fromBufferAttribute(attribute, index) {\\n this.r = attribute.getX(index);\\n this.g = attribute.getY(index);\\n this.b = attribute.getZ(index);\\n if (attribute.normalized === true) {\\n this.r /= 255;\\n this.g /= 255;\\n this.b /= 255;\\n }\\n return this;\\n }\\n toJSON() {\\n return this.getHex();\\n }\\n};\\nColor.NAMES = _colorKeywords;\\nColor.prototype.isColor = true;\\nColor.prototype.r = 1;\\nColor.prototype.g = 1;\\nColor.prototype.b = 1;\\n\\n// ../../../node_modules/three/src/core/BufferAttribute.js\\nvar _vector4 = /* @__PURE__ */ new Vector3();\\nvar _vector23 = /* @__PURE__ */ new Vector2();\\nvar BufferAttribute = class {\\n constructor(array, itemSize, normalized) {\\n if (Array.isArray(array)) {\\n throw new TypeError(\\\\\\\"THREE.BufferAttribute: array should be a Typed Array.\\\\\\\");\\n }\\n this.name = \\\\\\\"\\\\\\\";\\n this.array = array;\\n this.itemSize = itemSize;\\n this.count = array !== void 0 ? array.length / itemSize : 0;\\n this.normalized = normalized === true;\\n this.usage = StaticDrawUsage;\\n this.updateRange = {offset: 0, count: -1};\\n this.version = 0;\\n }\\n onUploadCallback() {\\n }\\n set needsUpdate(value) {\\n if (value === true)\\n this.version++;\\n }\\n setUsage(value) {\\n this.usage = value;\\n return this;\\n }\\n copy(source) {\\n this.name = source.name;\\n this.array = new source.array.constructor(source.array);\\n this.itemSize = source.itemSize;\\n this.count = source.count;\\n this.normalized = source.normalized;\\n this.usage = source.usage;\\n return this;\\n }\\n copyAt(index1, attribute, index2) {\\n index1 *= this.itemSize;\\n index2 *= attribute.itemSize;\\n for (let i = 0, l = this.itemSize; i < l; i++) {\\n this.array[index1 + i] = attribute.array[index2 + i];\\n }\\n return this;\\n }\\n copyArray(array) {\\n this.array.set(array);\\n return this;\\n }\\n copyColorsArray(colors) {\\n const array = this.array;\\n let offset = 0;\\n for (let i = 0, l = colors.length; i < l; i++) {\\n let color = colors[i];\\n if (color === void 0) {\\n console.warn(\\\\\\\"THREE.BufferAttribute.copyColorsArray(): color is undefined\\\\\\\", i);\\n color = new Color();\\n }\\n array[offset++] = color.r;\\n array[offset++] = color.g;\\n array[offset++] = color.b;\\n }\\n return this;\\n }\\n copyVector2sArray(vectors) {\\n const array = this.array;\\n let offset = 0;\\n for (let i = 0, l = vectors.length; i < l; i++) {\\n let vector = vectors[i];\\n if (vector === void 0) {\\n console.warn(\\\\\\\"THREE.BufferAttribute.copyVector2sArray(): vector is undefined\\\\\\\", i);\\n vector = new Vector2();\\n }\\n array[offset++] = vector.x;\\n array[offset++] = vector.y;\\n }\\n return this;\\n }\\n copyVector3sArray(vectors) {\\n const array = this.array;\\n let offset = 0;\\n for (let i = 0, l = vectors.length; i < l; i++) {\\n let vector = vectors[i];\\n if (vector === void 0) {\\n console.warn(\\\\\\\"THREE.BufferAttribute.copyVector3sArray(): vector is undefined\\\\\\\", i);\\n vector = new Vector3();\\n }\\n array[offset++] = vector.x;\\n array[offset++] = vector.y;\\n array[offset++] = vector.z;\\n }\\n return this;\\n }\\n copyVector4sArray(vectors) {\\n const array = this.array;\\n let offset = 0;\\n for (let i = 0, l = vectors.length; i < l; i++) {\\n let vector = vectors[i];\\n if (vector === void 0) {\\n console.warn(\\\\\\\"THREE.BufferAttribute.copyVector4sArray(): vector is undefined\\\\\\\", i);\\n vector = new Vector4();\\n }\\n array[offset++] = vector.x;\\n array[offset++] = vector.y;\\n array[offset++] = vector.z;\\n array[offset++] = vector.w;\\n }\\n return this;\\n }\\n applyMatrix3(m) {\\n if (this.itemSize === 2) {\\n for (let i = 0, l = this.count; i < l; i++) {\\n _vector23.fromBufferAttribute(this, i);\\n _vector23.applyMatrix3(m);\\n this.setXY(i, _vector23.x, _vector23.y);\\n }\\n } else if (this.itemSize === 3) {\\n for (let i = 0, l = this.count; i < l; i++) {\\n _vector4.fromBufferAttribute(this, i);\\n _vector4.applyMatrix3(m);\\n this.setXYZ(i, _vector4.x, _vector4.y, _vector4.z);\\n }\\n }\\n return this;\\n }\\n applyMatrix4(m) {\\n for (let i = 0, l = this.count; i < l; i++) {\\n _vector4.x = this.getX(i);\\n _vector4.y = this.getY(i);\\n _vector4.z = this.getZ(i);\\n _vector4.applyMatrix4(m);\\n this.setXYZ(i, _vector4.x, _vector4.y, _vector4.z);\\n }\\n return this;\\n }\\n applyNormalMatrix(m) {\\n for (let i = 0, l = this.count; i < l; i++) {\\n _vector4.x = this.getX(i);\\n _vector4.y = this.getY(i);\\n _vector4.z = this.getZ(i);\\n _vector4.applyNormalMatrix(m);\\n this.setXYZ(i, _vector4.x, _vector4.y, _vector4.z);\\n }\\n return this;\\n }\\n transformDirection(m) {\\n for (let i = 0, l = this.count; i < l; i++) {\\n _vector4.x = this.getX(i);\\n _vector4.y = this.getY(i);\\n _vector4.z = this.getZ(i);\\n _vector4.transformDirection(m);\\n this.setXYZ(i, _vector4.x, _vector4.y, _vector4.z);\\n }\\n return this;\\n }\\n set(value, offset = 0) {\\n this.array.set(value, offset);\\n return this;\\n }\\n getX(index) {\\n return this.array[index * this.itemSize];\\n }\\n setX(index, x) {\\n this.array[index * this.itemSize] = x;\\n return this;\\n }\\n getY(index) {\\n return this.array[index * this.itemSize + 1];\\n }\\n setY(index, y) {\\n this.array[index * this.itemSize + 1] = y;\\n return this;\\n }\\n getZ(index) {\\n return this.array[index * this.itemSize + 2];\\n }\\n setZ(index, z) {\\n this.array[index * this.itemSize + 2] = z;\\n return this;\\n }\\n getW(index) {\\n return this.array[index * this.itemSize + 3];\\n }\\n setW(index, w) {\\n this.array[index * this.itemSize + 3] = w;\\n return this;\\n }\\n setXY(index, x, y) {\\n index *= this.itemSize;\\n this.array[index + 0] = x;\\n this.array[index + 1] = y;\\n return this;\\n }\\n setXYZ(index, x, y, z) {\\n index *= this.itemSize;\\n this.array[index + 0] = x;\\n this.array[index + 1] = y;\\n this.array[index + 2] = z;\\n return this;\\n }\\n setXYZW(index, x, y, z, w) {\\n index *= this.itemSize;\\n this.array[index + 0] = x;\\n this.array[index + 1] = y;\\n this.array[index + 2] = z;\\n this.array[index + 3] = w;\\n return this;\\n }\\n onUpload(callback) {\\n this.onUploadCallback = callback;\\n return this;\\n }\\n clone() {\\n return new this.constructor(this.array, this.itemSize).copy(this);\\n }\\n toJSON() {\\n const data = {\\n itemSize: this.itemSize,\\n type: this.array.constructor.name,\\n array: Array.prototype.slice.call(this.array),\\n normalized: this.normalized\\n };\\n if (this.name !== \\\\\\\"\\\\\\\")\\n data.name = this.name;\\n if (this.usage !== StaticDrawUsage)\\n data.usage = this.usage;\\n if (this.updateRange.offset !== 0 || this.updateRange.count !== -1)\\n data.updateRange = this.updateRange;\\n return data;\\n }\\n};\\nBufferAttribute.prototype.isBufferAttribute = true;\\nvar Uint16BufferAttribute = class extends BufferAttribute {\\n constructor(array, itemSize, normalized) {\\n super(new Uint16Array(array), itemSize, normalized);\\n }\\n};\\nvar Int32BufferAttribute = class extends BufferAttribute {\\n constructor(array, itemSize, normalized) {\\n super(new Int32Array(array), itemSize, normalized);\\n }\\n};\\nvar Uint32BufferAttribute = class extends BufferAttribute {\\n constructor(array, itemSize, normalized) {\\n super(new Uint32Array(array), itemSize, normalized);\\n }\\n};\\nvar Float16BufferAttribute = class extends BufferAttribute {\\n constructor(array, itemSize, normalized) {\\n super(new Uint16Array(array), itemSize, normalized);\\n }\\n};\\nFloat16BufferAttribute.prototype.isFloat16BufferAttribute = true;\\nvar Float32BufferAttribute = class extends BufferAttribute {\\n constructor(array, itemSize, normalized) {\\n super(new Float32Array(array), itemSize, normalized);\\n }\\n};\\n\\n// ../../../node_modules/three/src/utils.js\\nfunction arrayMax(array) {\\n if (array.length === 0)\\n return -Infinity;\\n let max = array[0];\\n for (let i = 1, l = array.length; i < l; ++i) {\\n if (array[i] > max)\\n max = array[i];\\n }\\n return max;\\n}\\nvar TYPED_ARRAYS = {\\n Int8Array,\\n Uint8Array,\\n Uint8ClampedArray,\\n Int16Array,\\n Uint16Array,\\n Int32Array,\\n Uint32Array,\\n Float32Array,\\n Float64Array\\n};\\nfunction createElementNS(name) {\\n return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\", name);\\n}\\n\\n// ../../../node_modules/three/src/core/BufferGeometry.js\\nvar _id = 0;\\nvar _m13 = /* @__PURE__ */ new Matrix4();\\nvar _obj = /* @__PURE__ */ new Object3D();\\nvar _offset = /* @__PURE__ */ new Vector3();\\nvar _box3 = /* @__PURE__ */ new Box3();\\nvar _boxMorphTargets = /* @__PURE__ */ new Box3();\\nvar _vector5 = /* @__PURE__ */ new Vector3();\\nvar BufferGeometry = class extends EventDispatcher {\\n constructor() {\\n super();\\n Object.defineProperty(this, \\\\\\\"id\\\\\\\", {value: _id++});\\n this.uuid = generateUUID();\\n this.name = \\\\\\\"\\\\\\\";\\n this.type = \\\\\\\"BufferGeometry\\\\\\\";\\n this.index = null;\\n this.attributes = {};\\n this.morphAttributes = {};\\n this.morphTargetsRelative = false;\\n this.groups = [];\\n this.boundingBox = null;\\n this.boundingSphere = null;\\n this.drawRange = {start: 0, count: Infinity};\\n this.userData = {};\\n }\\n getIndex() {\\n return this.index;\\n }\\n setIndex(index) {\\n if (Array.isArray(index)) {\\n this.index = new (arrayMax(index) > 65535 ? Uint32BufferAttribute : Uint16BufferAttribute)(index, 1);\\n } else {\\n this.index = index;\\n }\\n return this;\\n }\\n getAttribute(name) {\\n return this.attributes[name];\\n }\\n setAttribute(name, attribute) {\\n this.attributes[name] = attribute;\\n return this;\\n }\\n deleteAttribute(name) {\\n delete this.attributes[name];\\n return this;\\n }\\n hasAttribute(name) {\\n return this.attributes[name] !== void 0;\\n }\\n addGroup(start, count, materialIndex = 0) {\\n this.groups.push({\\n start,\\n count,\\n materialIndex\\n });\\n }\\n clearGroups() {\\n this.groups = [];\\n }\\n setDrawRange(start, count) {\\n this.drawRange.start = start;\\n this.drawRange.count = count;\\n }\\n applyMatrix4(matrix) {\\n const position = this.attributes.position;\\n if (position !== void 0) {\\n position.applyMatrix4(matrix);\\n position.needsUpdate = true;\\n }\\n const normal = this.attributes.normal;\\n if (normal !== void 0) {\\n const normalMatrix = new Matrix3().getNormalMatrix(matrix);\\n normal.applyNormalMatrix(normalMatrix);\\n normal.needsUpdate = true;\\n }\\n const tangent = this.attributes.tangent;\\n if (tangent !== void 0) {\\n tangent.transformDirection(matrix);\\n tangent.needsUpdate = true;\\n }\\n if (this.boundingBox !== null) {\\n this.computeBoundingBox();\\n }\\n if (this.boundingSphere !== null) {\\n this.computeBoundingSphere();\\n }\\n return this;\\n }\\n applyQuaternion(q) {\\n _m13.makeRotationFromQuaternion(q);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n rotateX(angle) {\\n _m13.makeRotationX(angle);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n rotateY(angle) {\\n _m13.makeRotationY(angle);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n rotateZ(angle) {\\n _m13.makeRotationZ(angle);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n translate(x, y, z) {\\n _m13.makeTranslation(x, y, z);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n scale(x, y, z) {\\n _m13.makeScale(x, y, z);\\n this.applyMatrix4(_m13);\\n return this;\\n }\\n lookAt(vector) {\\n _obj.lookAt(vector);\\n _obj.updateMatrix();\\n this.applyMatrix4(_obj.matrix);\\n return this;\\n }\\n center() {\\n this.computeBoundingBox();\\n this.boundingBox.getCenter(_offset).negate();\\n this.translate(_offset.x, _offset.y, _offset.z);\\n return this;\\n }\\n setFromPoints(points2) {\\n const position = [];\\n for (let i = 0, l = points2.length; i < l; i++) {\\n const point = points2[i];\\n position.push(point.x, point.y, point.z || 0);\\n }\\n this.setAttribute(\\\\\\\"position\\\\\\\", new Float32BufferAttribute(position, 3));\\n return this;\\n }\\n computeBoundingBox() {\\n if (this.boundingBox === null) {\\n this.boundingBox = new Box3();\\n }\\n const position = this.attributes.position;\\n const morphAttributesPosition = this.morphAttributes.position;\\n if (position && position.isGLBufferAttribute) {\\n console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".', this);\\n this.boundingBox.set(new Vector3(-Infinity, -Infinity, -Infinity), new Vector3(Infinity, Infinity, Infinity));\\n return;\\n }\\n if (position !== void 0) {\\n this.boundingBox.setFromBufferAttribute(position);\\n if (morphAttributesPosition) {\\n for (let i = 0, il = morphAttributesPosition.length; i < il; i++) {\\n const morphAttribute = morphAttributesPosition[i];\\n _box3.setFromBufferAttribute(morphAttribute);\\n if (this.morphTargetsRelative) {\\n _vector5.addVectors(this.boundingBox.min, _box3.min);\\n this.boundingBox.expandByPoint(_vector5);\\n _vector5.addVectors(this.boundingBox.max, _box3.max);\\n this.boundingBox.expandByPoint(_vector5);\\n } else {\\n this.boundingBox.expandByPoint(_box3.min);\\n this.boundingBox.expandByPoint(_box3.max);\\n }\\n }\\n }\\n } else {\\n this.boundingBox.makeEmpty();\\n }\\n if (isNaN(this.boundingBox.min.x) || isNaN(this.boundingBox.min.y) || isNaN(this.boundingBox.min.z)) {\\n console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.', this);\\n }\\n }\\n computeBoundingSphere() {\\n if (this.boundingSphere === null) {\\n this.boundingSphere = new Sphere();\\n }\\n const position = this.attributes.position;\\n const morphAttributesPosition = this.morphAttributes.position;\\n if (position && position.isGLBufferAttribute) {\\n console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".', this);\\n this.boundingSphere.set(new Vector3(), Infinity);\\n return;\\n }\\n if (position) {\\n const center = this.boundingSphere.center;\\n _box3.setFromBufferAttribute(position);\\n if (morphAttributesPosition) {\\n for (let i = 0, il = morphAttributesPosition.length; i < il; i++) {\\n const morphAttribute = morphAttributesPosition[i];\\n _boxMorphTargets.setFromBufferAttribute(morphAttribute);\\n if (this.morphTargetsRelative) {\\n _vector5.addVectors(_box3.min, _boxMorphTargets.min);\\n _box3.expandByPoint(_vector5);\\n _vector5.addVectors(_box3.max, _boxMorphTargets.max);\\n _box3.expandByPoint(_vector5);\\n } else {\\n _box3.expandByPoint(_boxMorphTargets.min);\\n _box3.expandByPoint(_boxMorphTargets.max);\\n }\\n }\\n }\\n _box3.getCenter(center);\\n let maxRadiusSq = 0;\\n for (let i = 0, il = position.count; i < il; i++) {\\n _vector5.fromBufferAttribute(position, i);\\n maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(_vector5));\\n }\\n if (morphAttributesPosition) {\\n for (let i = 0, il = morphAttributesPosition.length; i < il; i++) {\\n const morphAttribute = morphAttributesPosition[i];\\n const morphTargetsRelative = this.morphTargetsRelative;\\n for (let j = 0, jl = morphAttribute.count; j < jl; j++) {\\n _vector5.fromBufferAttribute(morphAttribute, j);\\n if (morphTargetsRelative) {\\n _offset.fromBufferAttribute(position, j);\\n _vector5.add(_offset);\\n }\\n maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(_vector5));\\n }\\n }\\n }\\n this.boundingSphere.radius = Math.sqrt(maxRadiusSq);\\n if (isNaN(this.boundingSphere.radius)) {\\n console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.', this);\\n }\\n }\\n }\\n computeTangents() {\\n const index = this.index;\\n const attributes = this.attributes;\\n if (index === null || attributes.position === void 0 || attributes.normal === void 0 || attributes.uv === void 0) {\\n console.error(\\\\\\\"THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)\\\\\\\");\\n return;\\n }\\n const indices = index.array;\\n const positions = attributes.position.array;\\n const normals = attributes.normal.array;\\n const uvs = attributes.uv.array;\\n const nVertices = positions.length / 3;\\n if (attributes.tangent === void 0) {\\n this.setAttribute(\\\\\\\"tangent\\\\\\\", new BufferAttribute(new Float32Array(4 * nVertices), 4));\\n }\\n const tangents = attributes.tangent.array;\\n const tan1 = [], tan2 = [];\\n for (let i = 0; i < nVertices; i++) {\\n tan1[i] = new Vector3();\\n tan2[i] = new Vector3();\\n }\\n const vA = new Vector3(), vB = new Vector3(), vC = new Vector3(), uvA = new Vector2(), uvB = new Vector2(), uvC = new Vector2(), sdir = new Vector3(), tdir = new Vector3();\\n function handleTriangle(a, b, c) {\\n vA.fromArray(positions, a * 3);\\n vB.fromArray(positions, b * 3);\\n vC.fromArray(positions, c * 3);\\n uvA.fromArray(uvs, a * 2);\\n uvB.fromArray(uvs, b * 2);\\n uvC.fromArray(uvs, c * 2);\\n vB.sub(vA);\\n vC.sub(vA);\\n uvB.sub(uvA);\\n uvC.sub(uvA);\\n const r = 1 / (uvB.x * uvC.y - uvC.x * uvB.y);\\n if (!isFinite(r))\\n return;\\n sdir.copy(vB).multiplyScalar(uvC.y).addScaledVector(vC, -uvB.y).multiplyScalar(r);\\n tdir.copy(vC).multiplyScalar(uvB.x).addScaledVector(vB, -uvC.x).multiplyScalar(r);\\n tan1[a].add(sdir);\\n tan1[b].add(sdir);\\n tan1[c].add(sdir);\\n tan2[a].add(tdir);\\n tan2[b].add(tdir);\\n tan2[c].add(tdir);\\n }\\n let groups = this.groups;\\n if (groups.length === 0) {\\n groups = [{\\n start: 0,\\n count: indices.length\\n }];\\n }\\n for (let i = 0, il = groups.length; i < il; ++i) {\\n const group = groups[i];\\n const start = group.start;\\n const count = group.count;\\n for (let j = start, jl = start + count; j < jl; j += 3) {\\n handleTriangle(indices[j + 0], indices[j + 1], indices[j + 2]);\\n }\\n }\\n const tmp = new Vector3(), tmp2 = new Vector3();\\n const n = new Vector3(), n2 = new Vector3();\\n function handleVertex(v) {\\n n.fromArray(normals, v * 3);\\n n2.copy(n);\\n const t = tan1[v];\\n tmp.copy(t);\\n tmp.sub(n.multiplyScalar(n.dot(t))).normalize();\\n tmp2.crossVectors(n2, t);\\n const test = tmp2.dot(tan2[v]);\\n const w = test < 0 ? -1 : 1;\\n tangents[v * 4] = tmp.x;\\n tangents[v * 4 + 1] = tmp.y;\\n tangents[v * 4 + 2] = tmp.z;\\n tangents[v * 4 + 3] = w;\\n }\\n for (let i = 0, il = groups.length; i < il; ++i) {\\n const group = groups[i];\\n const start = group.start;\\n const count = group.count;\\n for (let j = start, jl = start + count; j < jl; j += 3) {\\n handleVertex(indices[j + 0]);\\n handleVertex(indices[j + 1]);\\n handleVertex(indices[j + 2]);\\n }\\n }\\n }\\n computeVertexNormals() {\\n const index = this.index;\\n const positionAttribute = this.getAttribute(\\\\\\\"position\\\\\\\");\\n if (positionAttribute !== void 0) {\\n let normalAttribute = this.getAttribute(\\\\\\\"normal\\\\\\\");\\n if (normalAttribute === void 0) {\\n normalAttribute = new BufferAttribute(new Float32Array(positionAttribute.count * 3), 3);\\n this.setAttribute(\\\\\\\"normal\\\\\\\", normalAttribute);\\n } else {\\n for (let i = 0, il = normalAttribute.count; i < il; i++) {\\n normalAttribute.setXYZ(i, 0, 0, 0);\\n }\\n }\\n const pA = new Vector3(), pB = new Vector3(), pC = new Vector3();\\n const nA = new Vector3(), nB = new Vector3(), nC = new Vector3();\\n const cb = new Vector3(), ab = new Vector3();\\n if (index) {\\n for (let i = 0, il = index.count; i < il; i += 3) {\\n const vA = index.getX(i + 0);\\n const vB = index.getX(i + 1);\\n const vC = index.getX(i + 2);\\n pA.fromBufferAttribute(positionAttribute, vA);\\n pB.fromBufferAttribute(positionAttribute, vB);\\n pC.fromBufferAttribute(positionAttribute, vC);\\n cb.subVectors(pC, pB);\\n ab.subVectors(pA, pB);\\n cb.cross(ab);\\n nA.fromBufferAttribute(normalAttribute, vA);\\n nB.fromBufferAttribute(normalAttribute, vB);\\n nC.fromBufferAttribute(normalAttribute, vC);\\n nA.add(cb);\\n nB.add(cb);\\n nC.add(cb);\\n normalAttribute.setXYZ(vA, nA.x, nA.y, nA.z);\\n normalAttribute.setXYZ(vB, nB.x, nB.y, nB.z);\\n normalAttribute.setXYZ(vC, nC.x, nC.y, nC.z);\\n }\\n } else {\\n for (let i = 0, il = positionAttribute.count; i < il; i += 3) {\\n pA.fromBufferAttribute(positionAttribute, i + 0);\\n pB.fromBufferAttribute(positionAttribute, i + 1);\\n pC.fromBufferAttribute(positionAttribute, i + 2);\\n cb.subVectors(pC, pB);\\n ab.subVectors(pA, pB);\\n cb.cross(ab);\\n normalAttribute.setXYZ(i + 0, cb.x, cb.y, cb.z);\\n normalAttribute.setXYZ(i + 1, cb.x, cb.y, cb.z);\\n normalAttribute.setXYZ(i + 2, cb.x, cb.y, cb.z);\\n }\\n }\\n this.normalizeNormals();\\n normalAttribute.needsUpdate = true;\\n }\\n }\\n merge(geometry, offset) {\\n if (!(geometry && geometry.isBufferGeometry)) {\\n console.error(\\\\\\\"THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.\\\\\\\", geometry);\\n return;\\n }\\n if (offset === void 0) {\\n offset = 0;\\n console.warn(\\\\\\\"THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge.\\\\\\\");\\n }\\n const attributes = this.attributes;\\n for (const key in attributes) {\\n if (geometry.attributes[key] === void 0)\\n continue;\\n const attribute1 = attributes[key];\\n const attributeArray1 = attribute1.array;\\n const attribute2 = geometry.attributes[key];\\n const attributeArray2 = attribute2.array;\\n const attributeOffset = attribute2.itemSize * offset;\\n const length = Math.min(attributeArray2.length, attributeArray1.length - attributeOffset);\\n for (let i = 0, j = attributeOffset; i < length; i++, j++) {\\n attributeArray1[j] = attributeArray2[i];\\n }\\n }\\n return this;\\n }\\n normalizeNormals() {\\n const normals = this.attributes.normal;\\n for (let i = 0, il = normals.count; i < il; i++) {\\n _vector5.fromBufferAttribute(normals, i);\\n _vector5.normalize();\\n normals.setXYZ(i, _vector5.x, _vector5.y, _vector5.z);\\n }\\n }\\n toNonIndexed() {\\n function convertBufferAttribute(attribute, indices2) {\\n const array = attribute.array;\\n const itemSize = attribute.itemSize;\\n const normalized = attribute.normalized;\\n const array2 = new array.constructor(indices2.length * itemSize);\\n let index = 0, index2 = 0;\\n for (let i = 0, l = indices2.length; i < l; i++) {\\n if (attribute.isInterleavedBufferAttribute) {\\n index = indices2[i] * attribute.data.stride + attribute.offset;\\n } else {\\n index = indices2[i] * itemSize;\\n }\\n for (let j = 0; j < itemSize; j++) {\\n array2[index2++] = array[index++];\\n }\\n }\\n return new BufferAttribute(array2, itemSize, normalized);\\n }\\n if (this.index === null) {\\n console.warn(\\\\\\\"THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed.\\\\\\\");\\n return this;\\n }\\n const geometry2 = new BufferGeometry();\\n const indices = this.index.array;\\n const attributes = this.attributes;\\n for (const name in attributes) {\\n const attribute = attributes[name];\\n const newAttribute = convertBufferAttribute(attribute, indices);\\n geometry2.setAttribute(name, newAttribute);\\n }\\n const morphAttributes = this.morphAttributes;\\n for (const name in morphAttributes) {\\n const morphArray = [];\\n const morphAttribute = morphAttributes[name];\\n for (let i = 0, il = morphAttribute.length; i < il; i++) {\\n const attribute = morphAttribute[i];\\n const newAttribute = convertBufferAttribute(attribute, indices);\\n morphArray.push(newAttribute);\\n }\\n geometry2.morphAttributes[name] = morphArray;\\n }\\n geometry2.morphTargetsRelative = this.morphTargetsRelative;\\n const groups = this.groups;\\n for (let i = 0, l = groups.length; i < l; i++) {\\n const group = groups[i];\\n geometry2.addGroup(group.start, group.count, group.materialIndex);\\n }\\n return geometry2;\\n }\\n toJSON() {\\n const data = {\\n metadata: {\\n version: 4.5,\\n type: \\\\\\\"BufferGeometry\\\\\\\",\\n generator: \\\\\\\"BufferGeometry.toJSON\\\\\\\"\\n }\\n };\\n data.uuid = this.uuid;\\n data.type = this.type;\\n if (this.name !== \\\\\\\"\\\\\\\")\\n data.name = this.name;\\n if (Object.keys(this.userData).length > 0)\\n data.userData = this.userData;\\n if (this.parameters !== void 0) {\\n const parameters = this.parameters;\\n for (const key in parameters) {\\n if (parameters[key] !== void 0)\\n data[key] = parameters[key];\\n }\\n return data;\\n }\\n data.data = {attributes: {}};\\n const index = this.index;\\n if (index !== null) {\\n data.data.index = {\\n type: index.array.constructor.name,\\n array: Array.prototype.slice.call(index.array)\\n };\\n }\\n const attributes = this.attributes;\\n for (const key in attributes) {\\n const attribute = attributes[key];\\n data.data.attributes[key] = attribute.toJSON(data.data);\\n }\\n const morphAttributes = {};\\n let hasMorphAttributes = false;\\n for (const key in this.morphAttributes) {\\n const attributeArray = this.morphAttributes[key];\\n const array = [];\\n for (let i = 0, il = attributeArray.length; i < il; i++) {\\n const attribute = attributeArray[i];\\n array.push(attribute.toJSON(data.data));\\n }\\n if (array.length > 0) {\\n morphAttributes[key] = array;\\n hasMorphAttributes = true;\\n }\\n }\\n if (hasMorphAttributes) {\\n data.data.morphAttributes = morphAttributes;\\n data.data.morphTargetsRelative = this.morphTargetsRelative;\\n }\\n const groups = this.groups;\\n if (groups.length > 0) {\\n data.data.groups = JSON.parse(JSON.stringify(groups));\\n }\\n const boundingSphere = this.boundingSphere;\\n if (boundingSphere !== null) {\\n data.data.boundingSphere = {\\n center: boundingSphere.center.toArray(),\\n radius: boundingSphere.radius\\n };\\n }\\n return data;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(source) {\\n this.index = null;\\n this.attributes = {};\\n this.morphAttributes = {};\\n this.groups = [];\\n this.boundingBox = null;\\n this.boundingSphere = null;\\n const data = {};\\n this.name = source.name;\\n const index = source.index;\\n if (index !== null) {\\n this.setIndex(index.clone(data));\\n }\\n const attributes = source.attributes;\\n for (const name in attributes) {\\n const attribute = attributes[name];\\n this.setAttribute(name, attribute.clone(data));\\n }\\n const morphAttributes = source.morphAttributes;\\n for (const name in morphAttributes) {\\n const array = [];\\n const morphAttribute = morphAttributes[name];\\n for (let i = 0, l = morphAttribute.length; i < l; i++) {\\n array.push(morphAttribute[i].clone(data));\\n }\\n this.morphAttributes[name] = array;\\n }\\n this.morphTargetsRelative = source.morphTargetsRelative;\\n const groups = source.groups;\\n for (let i = 0, l = groups.length; i < l; i++) {\\n const group = groups[i];\\n this.addGroup(group.start, group.count, group.materialIndex);\\n }\\n const boundingBox = source.boundingBox;\\n if (boundingBox !== null) {\\n this.boundingBox = boundingBox.clone();\\n }\\n const boundingSphere = source.boundingSphere;\\n if (boundingSphere !== null) {\\n this.boundingSphere = boundingSphere.clone();\\n }\\n this.drawRange.start = source.drawRange.start;\\n this.drawRange.count = source.drawRange.count;\\n this.userData = source.userData;\\n if (source.parameters !== void 0)\\n this.parameters = Object.assign({}, source.parameters);\\n return this;\\n }\\n dispose() {\\n this.dispatchEvent({type: \\\\\\\"dispose\\\\\\\"});\\n }\\n};\\nBufferGeometry.prototype.isBufferGeometry = true;\\n\\n// ../../../node_modules/three/src/geometries/BoxGeometry.js\\nvar BoxGeometry = class extends BufferGeometry {\\n constructor(width = 1, height = 1, depth2 = 1, widthSegments = 1, heightSegments = 1, depthSegments = 1) {\\n super();\\n this.type = \\\\\\\"BoxGeometry\\\\\\\";\\n this.parameters = {\\n width,\\n height,\\n depth: depth2,\\n widthSegments,\\n heightSegments,\\n depthSegments\\n };\\n const scope = this;\\n widthSegments = Math.floor(widthSegments);\\n heightSegments = Math.floor(heightSegments);\\n depthSegments = Math.floor(depthSegments);\\n const indices = [];\\n const vertices = [];\\n const normals = [];\\n const uvs = [];\\n let numberOfVertices = 0;\\n let groupStart = 0;\\n buildPlane(\\\\\\\"z\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"x\\\\\\\", -1, -1, depth2, height, width, depthSegments, heightSegments, 0);\\n buildPlane(\\\\\\\"z\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"x\\\\\\\", 1, -1, depth2, height, -width, depthSegments, heightSegments, 1);\\n buildPlane(\\\\\\\"x\\\\\\\", \\\\\\\"z\\\\\\\", \\\\\\\"y\\\\\\\", 1, 1, width, depth2, height, widthSegments, depthSegments, 2);\\n buildPlane(\\\\\\\"x\\\\\\\", \\\\\\\"z\\\\\\\", \\\\\\\"y\\\\\\\", 1, -1, width, depth2, -height, widthSegments, depthSegments, 3);\\n buildPlane(\\\\\\\"x\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"z\\\\\\\", 1, -1, width, height, depth2, widthSegments, heightSegments, 4);\\n buildPlane(\\\\\\\"x\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"z\\\\\\\", -1, -1, width, height, -depth2, widthSegments, heightSegments, 5);\\n this.setIndex(indices);\\n this.setAttribute(\\\\\\\"position\\\\\\\", new Float32BufferAttribute(vertices, 3));\\n this.setAttribute(\\\\\\\"normal\\\\\\\", new Float32BufferAttribute(normals, 3));\\n this.setAttribute(\\\\\\\"uv\\\\\\\", new Float32BufferAttribute(uvs, 2));\\n function buildPlane(u, v, w, udir, vdir, width2, height2, depth3, gridX, gridY, materialIndex) {\\n const segmentWidth = width2 / gridX;\\n const segmentHeight = height2 / gridY;\\n const widthHalf = width2 / 2;\\n const heightHalf = height2 / 2;\\n const depthHalf = depth3 / 2;\\n const gridX1 = gridX + 1;\\n const gridY1 = gridY + 1;\\n let vertexCounter = 0;\\n let groupCount = 0;\\n const vector = new Vector3();\\n for (let iy = 0; iy < gridY1; iy++) {\\n const y = iy * segmentHeight - heightHalf;\\n for (let ix = 0; ix < gridX1; ix++) {\\n const x = ix * segmentWidth - widthHalf;\\n vector[u] = x * udir;\\n vector[v] = y * vdir;\\n vector[w] = depthHalf;\\n vertices.push(vector.x, vector.y, vector.z);\\n vector[u] = 0;\\n vector[v] = 0;\\n vector[w] = depth3 > 0 ? 1 : -1;\\n normals.push(vector.x, vector.y, vector.z);\\n uvs.push(ix / gridX);\\n uvs.push(1 - iy / gridY);\\n vertexCounter += 1;\\n }\\n }\\n for (let iy = 0; iy < gridY; iy++) {\\n for (let ix = 0; ix < gridX; ix++) {\\n const a = numberOfVertices + ix + gridX1 * iy;\\n const b = numberOfVertices + ix + gridX1 * (iy + 1);\\n const c = numberOfVertices + (ix + 1) + gridX1 * (iy + 1);\\n const d = numberOfVertices + (ix + 1) + gridX1 * iy;\\n indices.push(a, b, d);\\n indices.push(b, c, d);\\n groupCount += 6;\\n }\\n }\\n scope.addGroup(groupStart, groupCount, materialIndex);\\n groupStart += groupCount;\\n numberOfVertices += vertexCounter;\\n }\\n }\\n static fromJSON(data) {\\n return new BoxGeometry(data.width, data.height, data.depth, data.widthSegments, data.heightSegments, data.depthSegments);\\n }\\n};\\n\\n// ../../../node_modules/three/src/geometries/PlaneGeometry.js\\nvar PlaneGeometry = class extends BufferGeometry {\\n constructor(width = 1, height = 1, widthSegments = 1, heightSegments = 1) {\\n super();\\n this.type = \\\\\\\"PlaneGeometry\\\\\\\";\\n this.parameters = {\\n width,\\n height,\\n widthSegments,\\n heightSegments\\n };\\n const width_half = width / 2;\\n const height_half = height / 2;\\n const gridX = Math.floor(widthSegments);\\n const gridY = Math.floor(heightSegments);\\n const gridX1 = gridX + 1;\\n const gridY1 = gridY + 1;\\n const segment_width = width / gridX;\\n const segment_height = height / gridY;\\n const indices = [];\\n const vertices = [];\\n const normals = [];\\n const uvs = [];\\n for (let iy = 0; iy < gridY1; iy++) {\\n const y = iy * segment_height - height_half;\\n for (let ix = 0; ix < gridX1; ix++) {\\n const x = ix * segment_width - width_half;\\n vertices.push(x, -y, 0);\\n normals.push(0, 0, 1);\\n uvs.push(ix / gridX);\\n uvs.push(1 - iy / gridY);\\n }\\n }\\n for (let iy = 0; iy < gridY; iy++) {\\n for (let ix = 0; ix < gridX; ix++) {\\n const a = ix + gridX1 * iy;\\n const b = ix + gridX1 * (iy + 1);\\n const c = ix + 1 + gridX1 * (iy + 1);\\n const d = ix + 1 + gridX1 * iy;\\n indices.push(a, b, d);\\n indices.push(b, c, d);\\n }\\n }\\n this.setIndex(indices);\\n this.setAttribute(\\\\\\\"position\\\\\\\", new Float32BufferAttribute(vertices, 3));\\n this.setAttribute(\\\\\\\"normal\\\\\\\", new Float32BufferAttribute(normals, 3));\\n this.setAttribute(\\\\\\\"uv\\\\\\\", new Float32BufferAttribute(uvs, 2));\\n }\\n static fromJSON(data) {\\n return new PlaneGeometry(data.width, data.height, data.widthSegments, data.heightSegments);\\n }\\n};\\n\\n// ../../../node_modules/three/src/materials/Material.js\\nvar materialId = 0;\\nvar Material = class extends EventDispatcher {\\n constructor() {\\n super();\\n Object.defineProperty(this, \\\\\\\"id\\\\\\\", {value: materialId++});\\n this.uuid = generateUUID();\\n this.name = \\\\\\\"\\\\\\\";\\n this.type = \\\\\\\"Material\\\\\\\";\\n this.fog = true;\\n this.blending = NormalBlending;\\n this.side = FrontSide;\\n this.vertexColors = false;\\n this.opacity = 1;\\n this.format = RGBAFormat;\\n this.transparent = false;\\n this.blendSrc = SrcAlphaFactor;\\n this.blendDst = OneMinusSrcAlphaFactor;\\n this.blendEquation = AddEquation;\\n this.blendSrcAlpha = null;\\n this.blendDstAlpha = null;\\n this.blendEquationAlpha = null;\\n this.depthFunc = LessEqualDepth;\\n this.depthTest = true;\\n this.depthWrite = true;\\n this.stencilWriteMask = 255;\\n this.stencilFunc = AlwaysStencilFunc;\\n this.stencilRef = 0;\\n this.stencilFuncMask = 255;\\n this.stencilFail = KeepStencilOp;\\n this.stencilZFail = KeepStencilOp;\\n this.stencilZPass = KeepStencilOp;\\n this.stencilWrite = false;\\n this.clippingPlanes = null;\\n this.clipIntersection = false;\\n this.clipShadows = false;\\n this.shadowSide = null;\\n this.colorWrite = true;\\n this.precision = null;\\n this.polygonOffset = false;\\n this.polygonOffsetFactor = 0;\\n this.polygonOffsetUnits = 0;\\n this.dithering = false;\\n this.alphaToCoverage = false;\\n this.premultipliedAlpha = false;\\n this.visible = true;\\n this.toneMapped = true;\\n this.userData = {};\\n this.version = 0;\\n this._alphaTest = 0;\\n }\\n get alphaTest() {\\n return this._alphaTest;\\n }\\n set alphaTest(value) {\\n if (this._alphaTest > 0 !== value > 0) {\\n this.version++;\\n }\\n this._alphaTest = value;\\n }\\n onBuild() {\\n }\\n onBeforeRender() {\\n }\\n onBeforeCompile() {\\n }\\n customProgramCacheKey() {\\n return this.onBeforeCompile.toString();\\n }\\n setValues(values) {\\n if (values === void 0)\\n return;\\n for (const key in values) {\\n const newValue = values[key];\\n if (newValue === void 0) {\\n console.warn(\\\\\\\"THREE.Material: '\\\\\\\" + key + \\\\\\\"' parameter is undefined.\\\\\\\");\\n continue;\\n }\\n if (key === \\\\\\\"shading\\\\\\\") {\\n console.warn(\\\\\\\"THREE.\\\\\\\" + this.type + \\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\");\\n this.flatShading = newValue === FlatShading ? true : false;\\n continue;\\n }\\n const currentValue = this[key];\\n if (currentValue === void 0) {\\n console.warn(\\\\\\\"THREE.\\\\\\\" + this.type + \\\\\\\": '\\\\\\\" + key + \\\\\\\"' is not a property of this material.\\\\\\\");\\n continue;\\n }\\n if (currentValue && currentValue.isColor) {\\n currentValue.set(newValue);\\n } else if (currentValue && currentValue.isVector3 && (newValue && newValue.isVector3)) {\\n currentValue.copy(newValue);\\n } else {\\n this[key] = newValue;\\n }\\n }\\n }\\n toJSON(meta) {\\n const isRoot = meta === void 0 || typeof meta === \\\\\\\"string\\\\\\\";\\n if (isRoot) {\\n meta = {\\n textures: {},\\n images: {}\\n };\\n }\\n const data = {\\n metadata: {\\n version: 4.5,\\n type: \\\\\\\"Material\\\\\\\",\\n generator: \\\\\\\"Material.toJSON\\\\\\\"\\n }\\n };\\n data.uuid = this.uuid;\\n data.type = this.type;\\n if (this.name !== \\\\\\\"\\\\\\\")\\n data.name = this.name;\\n if (this.color && this.color.isColor)\\n data.color = this.color.getHex();\\n if (this.roughness !== void 0)\\n data.roughness = this.roughness;\\n if (this.metalness !== void 0)\\n data.metalness = this.metalness;\\n if (this.sheen !== void 0)\\n data.sheen = this.sheen;\\n if (this.sheenTint && this.sheenTint.isColor)\\n data.sheenTint = this.sheenTint.getHex();\\n if (this.sheenRoughness !== void 0)\\n data.sheenRoughness = this.sheenRoughness;\\n if (this.emissive && this.emissive.isColor)\\n data.emissive = this.emissive.getHex();\\n if (this.emissiveIntensity && this.emissiveIntensity !== 1)\\n data.emissiveIntensity = this.emissiveIntensity;\\n if (this.specular && this.specular.isColor)\\n data.specular = this.specular.getHex();\\n if (this.specularIntensity !== void 0)\\n data.specularIntensity = this.specularIntensity;\\n if (this.specularTint && this.specularTint.isColor)\\n data.specularTint = this.specularTint.getHex();\\n if (this.shininess !== void 0)\\n data.shininess = this.shininess;\\n if (this.clearcoat !== void 0)\\n data.clearcoat = this.clearcoat;\\n if (this.clearcoatRoughness !== void 0)\\n data.clearcoatRoughness = this.clearcoatRoughness;\\n if (this.clearcoatMap && this.clearcoatMap.isTexture) {\\n data.clearcoatMap = this.clearcoatMap.toJSON(meta).uuid;\\n }\\n if (this.clearcoatRoughnessMap && this.clearcoatRoughnessMap.isTexture) {\\n data.clearcoatRoughnessMap = this.clearcoatRoughnessMap.toJSON(meta).uuid;\\n }\\n if (this.clearcoatNormalMap && this.clearcoatNormalMap.isTexture) {\\n data.clearcoatNormalMap = this.clearcoatNormalMap.toJSON(meta).uuid;\\n data.clearcoatNormalScale = this.clearcoatNormalScale.toArray();\\n }\\n if (this.map && this.map.isTexture)\\n data.map = this.map.toJSON(meta).uuid;\\n if (this.matcap && this.matcap.isTexture)\\n data.matcap = this.matcap.toJSON(meta).uuid;\\n if (this.alphaMap && this.alphaMap.isTexture)\\n data.alphaMap = this.alphaMap.toJSON(meta).uuid;\\n if (this.lightMap && this.lightMap.isTexture) {\\n data.lightMap = this.lightMap.toJSON(meta).uuid;\\n data.lightMapIntensity = this.lightMapIntensity;\\n }\\n if (this.aoMap && this.aoMap.isTexture) {\\n data.aoMap = this.aoMap.toJSON(meta).uuid;\\n data.aoMapIntensity = this.aoMapIntensity;\\n }\\n if (this.bumpMap && this.bumpMap.isTexture) {\\n data.bumpMap = this.bumpMap.toJSON(meta).uuid;\\n data.bumpScale = this.bumpScale;\\n }\\n if (this.normalMap && this.normalMap.isTexture) {\\n data.normalMap = this.normalMap.toJSON(meta).uuid;\\n data.normalMapType = this.normalMapType;\\n data.normalScale = this.normalScale.toArray();\\n }\\n if (this.displacementMap && this.displacementMap.isTexture) {\\n data.displacementMap = this.displacementMap.toJSON(meta).uuid;\\n data.displacementScale = this.displacementScale;\\n data.displacementBias = this.displacementBias;\\n }\\n if (this.roughnessMap && this.roughnessMap.isTexture)\\n data.roughnessMap = this.roughnessMap.toJSON(meta).uuid;\\n if (this.metalnessMap && this.metalnessMap.isTexture)\\n data.metalnessMap = this.metalnessMap.toJSON(meta).uuid;\\n if (this.emissiveMap && this.emissiveMap.isTexture)\\n data.emissiveMap = this.emissiveMap.toJSON(meta).uuid;\\n if (this.specularMap && this.specularMap.isTexture)\\n data.specularMap = this.specularMap.toJSON(meta).uuid;\\n if (this.specularIntensityMap && this.specularIntensityMap.isTexture)\\n data.specularIntensityMap = this.specularIntensityMap.toJSON(meta).uuid;\\n if (this.specularTintMap && this.specularTintMap.isTexture)\\n data.specularTintMap = this.specularTintMap.toJSON(meta).uuid;\\n if (this.envMap && this.envMap.isTexture) {\\n data.envMap = this.envMap.toJSON(meta).uuid;\\n if (this.combine !== void 0)\\n data.combine = this.combine;\\n }\\n if (this.envMapIntensity !== void 0)\\n data.envMapIntensity = this.envMapIntensity;\\n if (this.reflectivity !== void 0)\\n data.reflectivity = this.reflectivity;\\n if (this.refractionRatio !== void 0)\\n data.refractionRatio = this.refractionRatio;\\n if (this.gradientMap && this.gradientMap.isTexture) {\\n data.gradientMap = this.gradientMap.toJSON(meta).uuid;\\n }\\n if (this.transmission !== void 0)\\n data.transmission = this.transmission;\\n if (this.transmissionMap && this.transmissionMap.isTexture)\\n data.transmissionMap = this.transmissionMap.toJSON(meta).uuid;\\n if (this.thickness !== void 0)\\n data.thickness = this.thickness;\\n if (this.thicknessMap && this.thicknessMap.isTexture)\\n data.thicknessMap = this.thicknessMap.toJSON(meta).uuid;\\n if (this.attenuationDistance !== void 0)\\n data.attenuationDistance = this.attenuationDistance;\\n if (this.attenuationTint !== void 0)\\n data.attenuationTint = this.attenuationTint.getHex();\\n if (this.size !== void 0)\\n data.size = this.size;\\n if (this.shadowSide !== null)\\n data.shadowSide = this.shadowSide;\\n if (this.sizeAttenuation !== void 0)\\n data.sizeAttenuation = this.sizeAttenuation;\\n if (this.blending !== NormalBlending)\\n data.blending = this.blending;\\n if (this.side !== FrontSide)\\n data.side = this.side;\\n if (this.vertexColors)\\n data.vertexColors = true;\\n if (this.opacity < 1)\\n data.opacity = this.opacity;\\n if (this.format !== RGBAFormat)\\n data.format = this.format;\\n if (this.transparent === true)\\n data.transparent = this.transparent;\\n data.depthFunc = this.depthFunc;\\n data.depthTest = this.depthTest;\\n data.depthWrite = this.depthWrite;\\n data.colorWrite = this.colorWrite;\\n data.stencilWrite = this.stencilWrite;\\n data.stencilWriteMask = this.stencilWriteMask;\\n data.stencilFunc = this.stencilFunc;\\n data.stencilRef = this.stencilRef;\\n data.stencilFuncMask = this.stencilFuncMask;\\n data.stencilFail = this.stencilFail;\\n data.stencilZFail = this.stencilZFail;\\n data.stencilZPass = this.stencilZPass;\\n if (this.rotation && this.rotation !== 0)\\n data.rotation = this.rotation;\\n if (this.polygonOffset === true)\\n data.polygonOffset = true;\\n if (this.polygonOffsetFactor !== 0)\\n data.polygonOffsetFactor = this.polygonOffsetFactor;\\n if (this.polygonOffsetUnits !== 0)\\n data.polygonOffsetUnits = this.polygonOffsetUnits;\\n if (this.linewidth && this.linewidth !== 1)\\n data.linewidth = this.linewidth;\\n if (this.dashSize !== void 0)\\n data.dashSize = this.dashSize;\\n if (this.gapSize !== void 0)\\n data.gapSize = this.gapSize;\\n if (this.scale !== void 0)\\n data.scale = this.scale;\\n if (this.dithering === true)\\n data.dithering = true;\\n if (this.alphaTest > 0)\\n data.alphaTest = this.alphaTest;\\n if (this.alphaToCoverage === true)\\n data.alphaToCoverage = this.alphaToCoverage;\\n if (this.premultipliedAlpha === true)\\n data.premultipliedAlpha = this.premultipliedAlpha;\\n if (this.wireframe === true)\\n data.wireframe = this.wireframe;\\n if (this.wireframeLinewidth > 1)\\n data.wireframeLinewidth = this.wireframeLinewidth;\\n if (this.wireframeLinecap !== \\\\\\\"round\\\\\\\")\\n data.wireframeLinecap = this.wireframeLinecap;\\n if (this.wireframeLinejoin !== \\\\\\\"round\\\\\\\")\\n data.wireframeLinejoin = this.wireframeLinejoin;\\n if (this.flatShading === true)\\n data.flatShading = this.flatShading;\\n if (this.visible === false)\\n data.visible = false;\\n if (this.toneMapped === false)\\n data.toneMapped = false;\\n if (JSON.stringify(this.userData) !== \\\\\\\"{}\\\\\\\")\\n data.userData = this.userData;\\n function extractFromCache(cache) {\\n const values = [];\\n for (const key in cache) {\\n const data2 = cache[key];\\n delete data2.metadata;\\n values.push(data2);\\n }\\n return values;\\n }\\n if (isRoot) {\\n const textures = extractFromCache(meta.textures);\\n const images = extractFromCache(meta.images);\\n if (textures.length > 0)\\n data.textures = textures;\\n if (images.length > 0)\\n data.images = images;\\n }\\n return data;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(source) {\\n this.name = source.name;\\n this.fog = source.fog;\\n this.blending = source.blending;\\n this.side = source.side;\\n this.vertexColors = source.vertexColors;\\n this.opacity = source.opacity;\\n this.format = source.format;\\n this.transparent = source.transparent;\\n this.blendSrc = source.blendSrc;\\n this.blendDst = source.blendDst;\\n this.blendEquation = source.blendEquation;\\n this.blendSrcAlpha = source.blendSrcAlpha;\\n this.blendDstAlpha = source.blendDstAlpha;\\n this.blendEquationAlpha = source.blendEquationAlpha;\\n this.depthFunc = source.depthFunc;\\n this.depthTest = source.depthTest;\\n this.depthWrite = source.depthWrite;\\n this.stencilWriteMask = source.stencilWriteMask;\\n this.stencilFunc = source.stencilFunc;\\n this.stencilRef = source.stencilRef;\\n this.stencilFuncMask = source.stencilFuncMask;\\n this.stencilFail = source.stencilFail;\\n this.stencilZFail = source.stencilZFail;\\n this.stencilZPass = source.stencilZPass;\\n this.stencilWrite = source.stencilWrite;\\n const srcPlanes = source.clippingPlanes;\\n let dstPlanes = null;\\n if (srcPlanes !== null) {\\n const n = srcPlanes.length;\\n dstPlanes = new Array(n);\\n for (let i = 0; i !== n; ++i) {\\n dstPlanes[i] = srcPlanes[i].clone();\\n }\\n }\\n this.clippingPlanes = dstPlanes;\\n this.clipIntersection = source.clipIntersection;\\n this.clipShadows = source.clipShadows;\\n this.shadowSide = source.shadowSide;\\n this.colorWrite = source.colorWrite;\\n this.precision = source.precision;\\n this.polygonOffset = source.polygonOffset;\\n this.polygonOffsetFactor = source.polygonOffsetFactor;\\n this.polygonOffsetUnits = source.polygonOffsetUnits;\\n this.dithering = source.dithering;\\n this.alphaTest = source.alphaTest;\\n this.alphaToCoverage = source.alphaToCoverage;\\n this.premultipliedAlpha = source.premultipliedAlpha;\\n this.visible = source.visible;\\n this.toneMapped = source.toneMapped;\\n this.userData = JSON.parse(JSON.stringify(source.userData));\\n return this;\\n }\\n dispose() {\\n this.dispatchEvent({type: \\\\\\\"dispose\\\\\\\"});\\n }\\n set needsUpdate(value) {\\n if (value === true)\\n this.version++;\\n }\\n};\\nMaterial.prototype.isMaterial = true;\\n\\n// ../../../node_modules/three/src/renderers/shaders/UniformsUtils.js\\nfunction cloneUniforms(src) {\\n const dst = {};\\n for (const u in src) {\\n dst[u] = {};\\n for (const p in src[u]) {\\n const property = src[u][p];\\n if (property && (property.isColor || property.isMatrix3 || property.isMatrix4 || property.isVector2 || property.isVector3 || property.isVector4 || property.isTexture || property.isQuaternion)) {\\n dst[u][p] = property.clone();\\n } else if (Array.isArray(property)) {\\n dst[u][p] = property.slice();\\n } else {\\n dst[u][p] = property;\\n }\\n }\\n }\\n return dst;\\n}\\nfunction mergeUniforms(uniforms) {\\n const merged = {};\\n for (let u = 0; u < uniforms.length; u++) {\\n const tmp = cloneUniforms(uniforms[u]);\\n for (const p in tmp) {\\n merged[p] = tmp[p];\\n }\\n }\\n return merged;\\n}\\nvar UniformsUtils = {clone: cloneUniforms, merge: mergeUniforms};\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/default_vertex.glsl.js\\nvar default_vertex_glsl_default = `\\nvoid main() {\\n\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/default_fragment.glsl.js\\nvar default_fragment_glsl_default = `\\nvoid main() {\\n\\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\\n}\\n`;\\n\\n// ../../../node_modules/three/src/materials/ShaderMaterial.js\\nvar ShaderMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"ShaderMaterial\\\\\\\";\\n this.defines = {};\\n this.uniforms = {};\\n this.vertexShader = default_vertex_glsl_default;\\n this.fragmentShader = default_fragment_glsl_default;\\n this.linewidth = 1;\\n this.wireframe = false;\\n this.wireframeLinewidth = 1;\\n this.fog = false;\\n this.lights = false;\\n this.clipping = false;\\n this.extensions = {\\n derivatives: false,\\n fragDepth: false,\\n drawBuffers: false,\\n shaderTextureLOD: false\\n };\\n this.defaultAttributeValues = {\\n color: [1, 1, 1],\\n uv: [0, 0],\\n uv2: [0, 0]\\n };\\n this.index0AttributeName = void 0;\\n this.uniformsNeedUpdate = false;\\n this.glslVersion = null;\\n if (parameters !== void 0) {\\n if (parameters.attributes !== void 0) {\\n console.error(\\\\\\\"THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead.\\\\\\\");\\n }\\n this.setValues(parameters);\\n }\\n }\\n copy(source) {\\n super.copy(source);\\n this.fragmentShader = source.fragmentShader;\\n this.vertexShader = source.vertexShader;\\n this.uniforms = cloneUniforms(source.uniforms);\\n this.defines = Object.assign({}, source.defines);\\n this.wireframe = source.wireframe;\\n this.wireframeLinewidth = source.wireframeLinewidth;\\n this.lights = source.lights;\\n this.clipping = source.clipping;\\n this.extensions = Object.assign({}, source.extensions);\\n this.glslVersion = source.glslVersion;\\n return this;\\n }\\n toJSON(meta) {\\n const data = super.toJSON(meta);\\n data.glslVersion = this.glslVersion;\\n data.uniforms = {};\\n for (const name in this.uniforms) {\\n const uniform = this.uniforms[name];\\n const value = uniform.value;\\n if (value && value.isTexture) {\\n data.uniforms[name] = {\\n type: \\\\\\\"t\\\\\\\",\\n value: value.toJSON(meta).uuid\\n };\\n } else if (value && value.isColor) {\\n data.uniforms[name] = {\\n type: \\\\\\\"c\\\\\\\",\\n value: value.getHex()\\n };\\n } else if (value && value.isVector2) {\\n data.uniforms[name] = {\\n type: \\\\\\\"v2\\\\\\\",\\n value: value.toArray()\\n };\\n } else if (value && value.isVector3) {\\n data.uniforms[name] = {\\n type: \\\\\\\"v3\\\\\\\",\\n value: value.toArray()\\n };\\n } else if (value && value.isVector4) {\\n data.uniforms[name] = {\\n type: \\\\\\\"v4\\\\\\\",\\n value: value.toArray()\\n };\\n } else if (value && value.isMatrix3) {\\n data.uniforms[name] = {\\n type: \\\\\\\"m3\\\\\\\",\\n value: value.toArray()\\n };\\n } else if (value && value.isMatrix4) {\\n data.uniforms[name] = {\\n type: \\\\\\\"m4\\\\\\\",\\n value: value.toArray()\\n };\\n } else {\\n data.uniforms[name] = {\\n value\\n };\\n }\\n }\\n if (Object.keys(this.defines).length > 0)\\n data.defines = this.defines;\\n data.vertexShader = this.vertexShader;\\n data.fragmentShader = this.fragmentShader;\\n const extensions = {};\\n for (const key in this.extensions) {\\n if (this.extensions[key] === true)\\n extensions[key] = true;\\n }\\n if (Object.keys(extensions).length > 0)\\n data.extensions = extensions;\\n return data;\\n }\\n};\\nShaderMaterial.prototype.isShaderMaterial = true;\\n\\n// ../../../node_modules/three/src/math/Ray.js\\nvar _vector6 = /* @__PURE__ */ new Vector3();\\nvar _segCenter = /* @__PURE__ */ new Vector3();\\nvar _segDir = /* @__PURE__ */ new Vector3();\\nvar _diff = /* @__PURE__ */ new Vector3();\\nvar _edge1 = /* @__PURE__ */ new Vector3();\\nvar _edge2 = /* @__PURE__ */ new Vector3();\\nvar _normal = /* @__PURE__ */ new Vector3();\\nvar Ray = class {\\n constructor(origin = new Vector3(), direction = new Vector3(0, 0, -1)) {\\n this.origin = origin;\\n this.direction = direction;\\n }\\n set(origin, direction) {\\n this.origin.copy(origin);\\n this.direction.copy(direction);\\n return this;\\n }\\n copy(ray) {\\n this.origin.copy(ray.origin);\\n this.direction.copy(ray.direction);\\n return this;\\n }\\n at(t, target) {\\n return target.copy(this.direction).multiplyScalar(t).add(this.origin);\\n }\\n lookAt(v) {\\n this.direction.copy(v).sub(this.origin).normalize();\\n return this;\\n }\\n recast(t) {\\n this.origin.copy(this.at(t, _vector6));\\n return this;\\n }\\n closestPointToPoint(point, target) {\\n target.subVectors(point, this.origin);\\n const directionDistance = target.dot(this.direction);\\n if (directionDistance < 0) {\\n return target.copy(this.origin);\\n }\\n return target.copy(this.direction).multiplyScalar(directionDistance).add(this.origin);\\n }\\n distanceToPoint(point) {\\n return Math.sqrt(this.distanceSqToPoint(point));\\n }\\n distanceSqToPoint(point) {\\n const directionDistance = _vector6.subVectors(point, this.origin).dot(this.direction);\\n if (directionDistance < 0) {\\n return this.origin.distanceToSquared(point);\\n }\\n _vector6.copy(this.direction).multiplyScalar(directionDistance).add(this.origin);\\n return _vector6.distanceToSquared(point);\\n }\\n distanceSqToSegment(v0, v1, optionalPointOnRay, optionalPointOnSegment) {\\n _segCenter.copy(v0).add(v1).multiplyScalar(0.5);\\n _segDir.copy(v1).sub(v0).normalize();\\n _diff.copy(this.origin).sub(_segCenter);\\n const segExtent = v0.distanceTo(v1) * 0.5;\\n const a01 = -this.direction.dot(_segDir);\\n const b0 = _diff.dot(this.direction);\\n const b1 = -_diff.dot(_segDir);\\n const c = _diff.lengthSq();\\n const det = Math.abs(1 - a01 * a01);\\n let s0, s1, sqrDist, extDet;\\n if (det > 0) {\\n s0 = a01 * b1 - b0;\\n s1 = a01 * b0 - b1;\\n extDet = segExtent * det;\\n if (s0 >= 0) {\\n if (s1 >= -extDet) {\\n if (s1 <= extDet) {\\n const invDet = 1 / det;\\n s0 *= invDet;\\n s1 *= invDet;\\n sqrDist = s0 * (s0 + a01 * s1 + 2 * b0) + s1 * (a01 * s0 + s1 + 2 * b1) + c;\\n } else {\\n s1 = segExtent;\\n s0 = Math.max(0, -(a01 * s1 + b0));\\n sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c;\\n }\\n } else {\\n s1 = -segExtent;\\n s0 = Math.max(0, -(a01 * s1 + b0));\\n sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c;\\n }\\n } else {\\n if (s1 <= -extDet) {\\n s0 = Math.max(0, -(-a01 * segExtent + b0));\\n s1 = s0 > 0 ? -segExtent : Math.min(Math.max(-segExtent, -b1), segExtent);\\n sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c;\\n } else if (s1 <= extDet) {\\n s0 = 0;\\n s1 = Math.min(Math.max(-segExtent, -b1), segExtent);\\n sqrDist = s1 * (s1 + 2 * b1) + c;\\n } else {\\n s0 = Math.max(0, -(a01 * segExtent + b0));\\n s1 = s0 > 0 ? segExtent : Math.min(Math.max(-segExtent, -b1), segExtent);\\n sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c;\\n }\\n }\\n } else {\\n s1 = a01 > 0 ? -segExtent : segExtent;\\n s0 = Math.max(0, -(a01 * s1 + b0));\\n sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c;\\n }\\n if (optionalPointOnRay) {\\n optionalPointOnRay.copy(this.direction).multiplyScalar(s0).add(this.origin);\\n }\\n if (optionalPointOnSegment) {\\n optionalPointOnSegment.copy(_segDir).multiplyScalar(s1).add(_segCenter);\\n }\\n return sqrDist;\\n }\\n intersectSphere(sphere, target) {\\n _vector6.subVectors(sphere.center, this.origin);\\n const tca = _vector6.dot(this.direction);\\n const d2 = _vector6.dot(_vector6) - tca * tca;\\n const radius2 = sphere.radius * sphere.radius;\\n if (d2 > radius2)\\n return null;\\n const thc = Math.sqrt(radius2 - d2);\\n const t0 = tca - thc;\\n const t1 = tca + thc;\\n if (t0 < 0 && t1 < 0)\\n return null;\\n if (t0 < 0)\\n return this.at(t1, target);\\n return this.at(t0, target);\\n }\\n intersectsSphere(sphere) {\\n return this.distanceSqToPoint(sphere.center) <= sphere.radius * sphere.radius;\\n }\\n distanceToPlane(plane) {\\n const denominator = plane.normal.dot(this.direction);\\n if (denominator === 0) {\\n if (plane.distanceToPoint(this.origin) === 0) {\\n return 0;\\n }\\n return null;\\n }\\n const t = -(this.origin.dot(plane.normal) + plane.constant) / denominator;\\n return t >= 0 ? t : null;\\n }\\n intersectPlane(plane, target) {\\n const t = this.distanceToPlane(plane);\\n if (t === null) {\\n return null;\\n }\\n return this.at(t, target);\\n }\\n intersectsPlane(plane) {\\n const distToPoint = plane.distanceToPoint(this.origin);\\n if (distToPoint === 0) {\\n return true;\\n }\\n const denominator = plane.normal.dot(this.direction);\\n if (denominator * distToPoint < 0) {\\n return true;\\n }\\n return false;\\n }\\n intersectBox(box, target) {\\n let tmin, tmax, tymin, tymax, tzmin, tzmax;\\n const invdirx = 1 / this.direction.x, invdiry = 1 / this.direction.y, invdirz = 1 / this.direction.z;\\n const origin = this.origin;\\n if (invdirx >= 0) {\\n tmin = (box.min.x - origin.x) * invdirx;\\n tmax = (box.max.x - origin.x) * invdirx;\\n } else {\\n tmin = (box.max.x - origin.x) * invdirx;\\n tmax = (box.min.x - origin.x) * invdirx;\\n }\\n if (invdiry >= 0) {\\n tymin = (box.min.y - origin.y) * invdiry;\\n tymax = (box.max.y - origin.y) * invdiry;\\n } else {\\n tymin = (box.max.y - origin.y) * invdiry;\\n tymax = (box.min.y - origin.y) * invdiry;\\n }\\n if (tmin > tymax || tymin > tmax)\\n return null;\\n if (tymin > tmin || tmin !== tmin)\\n tmin = tymin;\\n if (tymax < tmax || tmax !== tmax)\\n tmax = tymax;\\n if (invdirz >= 0) {\\n tzmin = (box.min.z - origin.z) * invdirz;\\n tzmax = (box.max.z - origin.z) * invdirz;\\n } else {\\n tzmin = (box.max.z - origin.z) * invdirz;\\n tzmax = (box.min.z - origin.z) * invdirz;\\n }\\n if (tmin > tzmax || tzmin > tmax)\\n return null;\\n if (tzmin > tmin || tmin !== tmin)\\n tmin = tzmin;\\n if (tzmax < tmax || tmax !== tmax)\\n tmax = tzmax;\\n if (tmax < 0)\\n return null;\\n return this.at(tmin >= 0 ? tmin : tmax, target);\\n }\\n intersectsBox(box) {\\n return this.intersectBox(box, _vector6) !== null;\\n }\\n intersectTriangle(a, b, c, backfaceCulling, target) {\\n _edge1.subVectors(b, a);\\n _edge2.subVectors(c, a);\\n _normal.crossVectors(_edge1, _edge2);\\n let DdN = this.direction.dot(_normal);\\n let sign;\\n if (DdN > 0) {\\n if (backfaceCulling)\\n return null;\\n sign = 1;\\n } else if (DdN < 0) {\\n sign = -1;\\n DdN = -DdN;\\n } else {\\n return null;\\n }\\n _diff.subVectors(this.origin, a);\\n const DdQxE2 = sign * this.direction.dot(_edge2.crossVectors(_diff, _edge2));\\n if (DdQxE2 < 0) {\\n return null;\\n }\\n const DdE1xQ = sign * this.direction.dot(_edge1.cross(_diff));\\n if (DdE1xQ < 0) {\\n return null;\\n }\\n if (DdQxE2 + DdE1xQ > DdN) {\\n return null;\\n }\\n const QdN = -sign * _diff.dot(_normal);\\n if (QdN < 0) {\\n return null;\\n }\\n return this.at(QdN / DdN, target);\\n }\\n applyMatrix4(matrix4) {\\n this.origin.applyMatrix4(matrix4);\\n this.direction.transformDirection(matrix4);\\n return this;\\n }\\n equals(ray) {\\n return ray.origin.equals(this.origin) && ray.direction.equals(this.direction);\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n};\\n\\n// ../../../node_modules/three/src/math/Triangle.js\\nvar _v02 = /* @__PURE__ */ new Vector3();\\nvar _v15 = /* @__PURE__ */ new Vector3();\\nvar _v22 = /* @__PURE__ */ new Vector3();\\nvar _v3 = /* @__PURE__ */ new Vector3();\\nvar _vab = /* @__PURE__ */ new Vector3();\\nvar _vac = /* @__PURE__ */ new Vector3();\\nvar _vbc = /* @__PURE__ */ new Vector3();\\nvar _vap = /* @__PURE__ */ new Vector3();\\nvar _vbp = /* @__PURE__ */ new Vector3();\\nvar _vcp = /* @__PURE__ */ new Vector3();\\nvar Triangle = class {\\n constructor(a = new Vector3(), b = new Vector3(), c = new Vector3()) {\\n this.a = a;\\n this.b = b;\\n this.c = c;\\n }\\n static getNormal(a, b, c, target) {\\n target.subVectors(c, b);\\n _v02.subVectors(a, b);\\n target.cross(_v02);\\n const targetLengthSq = target.lengthSq();\\n if (targetLengthSq > 0) {\\n return target.multiplyScalar(1 / Math.sqrt(targetLengthSq));\\n }\\n return target.set(0, 0, 0);\\n }\\n static getBarycoord(point, a, b, c, target) {\\n _v02.subVectors(c, a);\\n _v15.subVectors(b, a);\\n _v22.subVectors(point, a);\\n const dot00 = _v02.dot(_v02);\\n const dot01 = _v02.dot(_v15);\\n const dot02 = _v02.dot(_v22);\\n const dot11 = _v15.dot(_v15);\\n const dot12 = _v15.dot(_v22);\\n const denom = dot00 * dot11 - dot01 * dot01;\\n if (denom === 0) {\\n return target.set(-2, -1, -1);\\n }\\n const invDenom = 1 / denom;\\n const u = (dot11 * dot02 - dot01 * dot12) * invDenom;\\n const v = (dot00 * dot12 - dot01 * dot02) * invDenom;\\n return target.set(1 - u - v, v, u);\\n }\\n static containsPoint(point, a, b, c) {\\n this.getBarycoord(point, a, b, c, _v3);\\n return _v3.x >= 0 && _v3.y >= 0 && _v3.x + _v3.y <= 1;\\n }\\n static getUV(point, p1, p2, p3, uv1, uv2, uv3, target) {\\n this.getBarycoord(point, p1, p2, p3, _v3);\\n target.set(0, 0);\\n target.addScaledVector(uv1, _v3.x);\\n target.addScaledVector(uv2, _v3.y);\\n target.addScaledVector(uv3, _v3.z);\\n return target;\\n }\\n static isFrontFacing(a, b, c, direction) {\\n _v02.subVectors(c, b);\\n _v15.subVectors(a, b);\\n return _v02.cross(_v15).dot(direction) < 0 ? true : false;\\n }\\n set(a, b, c) {\\n this.a.copy(a);\\n this.b.copy(b);\\n this.c.copy(c);\\n return this;\\n }\\n setFromPointsAndIndices(points2, i0, i1, i2) {\\n this.a.copy(points2[i0]);\\n this.b.copy(points2[i1]);\\n this.c.copy(points2[i2]);\\n return this;\\n }\\n setFromAttributeAndIndices(attribute, i0, i1, i2) {\\n this.a.fromBufferAttribute(attribute, i0);\\n this.b.fromBufferAttribute(attribute, i1);\\n this.c.fromBufferAttribute(attribute, i2);\\n return this;\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(triangle) {\\n this.a.copy(triangle.a);\\n this.b.copy(triangle.b);\\n this.c.copy(triangle.c);\\n return this;\\n }\\n getArea() {\\n _v02.subVectors(this.c, this.b);\\n _v15.subVectors(this.a, this.b);\\n return _v02.cross(_v15).length() * 0.5;\\n }\\n getMidpoint(target) {\\n return target.addVectors(this.a, this.b).add(this.c).multiplyScalar(1 / 3);\\n }\\n getNormal(target) {\\n return Triangle.getNormal(this.a, this.b, this.c, target);\\n }\\n getPlane(target) {\\n return target.setFromCoplanarPoints(this.a, this.b, this.c);\\n }\\n getBarycoord(point, target) {\\n return Triangle.getBarycoord(point, this.a, this.b, this.c, target);\\n }\\n getUV(point, uv1, uv2, uv3, target) {\\n return Triangle.getUV(point, this.a, this.b, this.c, uv1, uv2, uv3, target);\\n }\\n containsPoint(point) {\\n return Triangle.containsPoint(point, this.a, this.b, this.c);\\n }\\n isFrontFacing(direction) {\\n return Triangle.isFrontFacing(this.a, this.b, this.c, direction);\\n }\\n intersectsBox(box) {\\n return box.intersectsTriangle(this);\\n }\\n closestPointToPoint(p, target) {\\n const a = this.a, b = this.b, c = this.c;\\n let v, w;\\n _vab.subVectors(b, a);\\n _vac.subVectors(c, a);\\n _vap.subVectors(p, a);\\n const d1 = _vab.dot(_vap);\\n const d2 = _vac.dot(_vap);\\n if (d1 <= 0 && d2 <= 0) {\\n return target.copy(a);\\n }\\n _vbp.subVectors(p, b);\\n const d3 = _vab.dot(_vbp);\\n const d4 = _vac.dot(_vbp);\\n if (d3 >= 0 && d4 <= d3) {\\n return target.copy(b);\\n }\\n const vc = d1 * d4 - d3 * d2;\\n if (vc <= 0 && d1 >= 0 && d3 <= 0) {\\n v = d1 / (d1 - d3);\\n return target.copy(a).addScaledVector(_vab, v);\\n }\\n _vcp.subVectors(p, c);\\n const d5 = _vab.dot(_vcp);\\n const d6 = _vac.dot(_vcp);\\n if (d6 >= 0 && d5 <= d6) {\\n return target.copy(c);\\n }\\n const vb = d5 * d2 - d1 * d6;\\n if (vb <= 0 && d2 >= 0 && d6 <= 0) {\\n w = d2 / (d2 - d6);\\n return target.copy(a).addScaledVector(_vac, w);\\n }\\n const va = d3 * d6 - d5 * d4;\\n if (va <= 0 && d4 - d3 >= 0 && d5 - d6 >= 0) {\\n _vbc.subVectors(c, b);\\n w = (d4 - d3) / (d4 - d3 + (d5 - d6));\\n return target.copy(b).addScaledVector(_vbc, w);\\n }\\n const denom = 1 / (va + vb + vc);\\n v = vb * denom;\\n w = vc * denom;\\n return target.copy(a).addScaledVector(_vab, v).addScaledVector(_vac, w);\\n }\\n equals(triangle) {\\n return triangle.a.equals(this.a) && triangle.b.equals(this.b) && triangle.c.equals(this.c);\\n }\\n};\\n\\n// ../../../node_modules/three/src/materials/MeshBasicMaterial.js\\nvar MeshBasicMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"MeshBasicMaterial\\\\\\\";\\n this.color = new Color(16777215);\\n this.map = null;\\n this.lightMap = null;\\n this.lightMapIntensity = 1;\\n this.aoMap = null;\\n this.aoMapIntensity = 1;\\n this.specularMap = null;\\n this.alphaMap = null;\\n this.envMap = null;\\n this.combine = MultiplyOperation;\\n this.reflectivity = 1;\\n this.refractionRatio = 0.98;\\n this.wireframe = false;\\n this.wireframeLinewidth = 1;\\n this.wireframeLinecap = \\\\\\\"round\\\\\\\";\\n this.wireframeLinejoin = \\\\\\\"round\\\\\\\";\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.color.copy(source.color);\\n this.map = source.map;\\n this.lightMap = source.lightMap;\\n this.lightMapIntensity = source.lightMapIntensity;\\n this.aoMap = source.aoMap;\\n this.aoMapIntensity = source.aoMapIntensity;\\n this.specularMap = source.specularMap;\\n this.alphaMap = source.alphaMap;\\n this.envMap = source.envMap;\\n this.combine = source.combine;\\n this.reflectivity = source.reflectivity;\\n this.refractionRatio = source.refractionRatio;\\n this.wireframe = source.wireframe;\\n this.wireframeLinewidth = source.wireframeLinewidth;\\n this.wireframeLinecap = source.wireframeLinecap;\\n this.wireframeLinejoin = source.wireframeLinejoin;\\n return this;\\n }\\n};\\nMeshBasicMaterial.prototype.isMeshBasicMaterial = true;\\n\\n// ../../../node_modules/three/src/objects/Mesh.js\\nvar _inverseMatrix = /* @__PURE__ */ new Matrix4();\\nvar _ray = /* @__PURE__ */ new Ray();\\nvar _sphere2 = /* @__PURE__ */ new Sphere();\\nvar _vA = /* @__PURE__ */ new Vector3();\\nvar _vB = /* @__PURE__ */ new Vector3();\\nvar _vC = /* @__PURE__ */ new Vector3();\\nvar _tempA = /* @__PURE__ */ new Vector3();\\nvar _tempB = /* @__PURE__ */ new Vector3();\\nvar _tempC = /* @__PURE__ */ new Vector3();\\nvar _morphA = /* @__PURE__ */ new Vector3();\\nvar _morphB = /* @__PURE__ */ new Vector3();\\nvar _morphC = /* @__PURE__ */ new Vector3();\\nvar _uvA = /* @__PURE__ */ new Vector2();\\nvar _uvB = /* @__PURE__ */ new Vector2();\\nvar _uvC = /* @__PURE__ */ new Vector2();\\nvar _intersectionPoint = /* @__PURE__ */ new Vector3();\\nvar _intersectionPointWorld = /* @__PURE__ */ new Vector3();\\nvar Mesh = class extends Object3D {\\n constructor(geometry = new BufferGeometry(), material = new MeshBasicMaterial()) {\\n super();\\n this.type = \\\\\\\"Mesh\\\\\\\";\\n this.geometry = geometry;\\n this.material = material;\\n this.updateMorphTargets();\\n }\\n copy(source) {\\n super.copy(source);\\n if (source.morphTargetInfluences !== void 0) {\\n this.morphTargetInfluences = source.morphTargetInfluences.slice();\\n }\\n if (source.morphTargetDictionary !== void 0) {\\n this.morphTargetDictionary = Object.assign({}, source.morphTargetDictionary);\\n }\\n this.material = source.material;\\n this.geometry = source.geometry;\\n return this;\\n }\\n updateMorphTargets() {\\n const geometry = this.geometry;\\n if (geometry.isBufferGeometry) {\\n const morphAttributes = geometry.morphAttributes;\\n const keys = Object.keys(morphAttributes);\\n if (keys.length > 0) {\\n const morphAttribute = morphAttributes[keys[0]];\\n if (morphAttribute !== void 0) {\\n this.morphTargetInfluences = [];\\n this.morphTargetDictionary = {};\\n for (let m = 0, ml = morphAttribute.length; m < ml; m++) {\\n const name = morphAttribute[m].name || String(m);\\n this.morphTargetInfluences.push(0);\\n this.morphTargetDictionary[name] = m;\\n }\\n }\\n }\\n } else {\\n const morphTargets = geometry.morphTargets;\\n if (morphTargets !== void 0 && morphTargets.length > 0) {\\n console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n }\\n raycast(raycaster, intersects) {\\n const geometry = this.geometry;\\n const material = this.material;\\n const matrixWorld = this.matrixWorld;\\n if (material === void 0)\\n return;\\n if (geometry.boundingSphere === null)\\n geometry.computeBoundingSphere();\\n _sphere2.copy(geometry.boundingSphere);\\n _sphere2.applyMatrix4(matrixWorld);\\n if (raycaster.ray.intersectsSphere(_sphere2) === false)\\n return;\\n _inverseMatrix.copy(matrixWorld).invert();\\n _ray.copy(raycaster.ray).applyMatrix4(_inverseMatrix);\\n if (geometry.boundingBox !== null) {\\n if (_ray.intersectsBox(geometry.boundingBox) === false)\\n return;\\n }\\n let intersection;\\n if (geometry.isBufferGeometry) {\\n const index = geometry.index;\\n const position = geometry.attributes.position;\\n const morphPosition = geometry.morphAttributes.position;\\n const morphTargetsRelative = geometry.morphTargetsRelative;\\n const uv = geometry.attributes.uv;\\n const uv2 = geometry.attributes.uv2;\\n const groups = geometry.groups;\\n const drawRange = geometry.drawRange;\\n if (index !== null) {\\n if (Array.isArray(material)) {\\n for (let i = 0, il = groups.length; i < il; i++) {\\n const group = groups[i];\\n const groupMaterial = material[group.materialIndex];\\n const start = Math.max(group.start, drawRange.start);\\n const end = Math.min(index.count, Math.min(group.start + group.count, drawRange.start + drawRange.count));\\n for (let j = start, jl = end; j < jl; j += 3) {\\n const a = index.getX(j);\\n const b = index.getX(j + 1);\\n const c = index.getX(j + 2);\\n intersection = checkBufferGeometryIntersection(this, groupMaterial, raycaster, _ray, position, morphPosition, morphTargetsRelative, uv, uv2, a, b, c);\\n if (intersection) {\\n intersection.faceIndex = Math.floor(j / 3);\\n intersection.face.materialIndex = group.materialIndex;\\n intersects.push(intersection);\\n }\\n }\\n }\\n } else {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(index.count, drawRange.start + drawRange.count);\\n for (let i = start, il = end; i < il; i += 3) {\\n const a = index.getX(i);\\n const b = index.getX(i + 1);\\n const c = index.getX(i + 2);\\n intersection = checkBufferGeometryIntersection(this, material, raycaster, _ray, position, morphPosition, morphTargetsRelative, uv, uv2, a, b, c);\\n if (intersection) {\\n intersection.faceIndex = Math.floor(i / 3);\\n intersects.push(intersection);\\n }\\n }\\n }\\n } else if (position !== void 0) {\\n if (Array.isArray(material)) {\\n for (let i = 0, il = groups.length; i < il; i++) {\\n const group = groups[i];\\n const groupMaterial = material[group.materialIndex];\\n const start = Math.max(group.start, drawRange.start);\\n const end = Math.min(position.count, Math.min(group.start + group.count, drawRange.start + drawRange.count));\\n for (let j = start, jl = end; j < jl; j += 3) {\\n const a = j;\\n const b = j + 1;\\n const c = j + 2;\\n intersection = checkBufferGeometryIntersection(this, groupMaterial, raycaster, _ray, position, morphPosition, morphTargetsRelative, uv, uv2, a, b, c);\\n if (intersection) {\\n intersection.faceIndex = Math.floor(j / 3);\\n intersection.face.materialIndex = group.materialIndex;\\n intersects.push(intersection);\\n }\\n }\\n }\\n } else {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(position.count, drawRange.start + drawRange.count);\\n for (let i = start, il = end; i < il; i += 3) {\\n const a = i;\\n const b = i + 1;\\n const c = i + 2;\\n intersection = checkBufferGeometryIntersection(this, material, raycaster, _ray, position, morphPosition, morphTargetsRelative, uv, uv2, a, b, c);\\n if (intersection) {\\n intersection.faceIndex = Math.floor(i / 3);\\n intersects.push(intersection);\\n }\\n }\\n }\\n }\\n } else if (geometry.isGeometry) {\\n console.error(\\\\\\\"THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n};\\nMesh.prototype.isMesh = true;\\nfunction checkIntersection(object, material, raycaster, ray, pA, pB, pC, point) {\\n let intersect;\\n if (material.side === BackSide) {\\n intersect = ray.intersectTriangle(pC, pB, pA, true, point);\\n } else {\\n intersect = ray.intersectTriangle(pA, pB, pC, material.side !== DoubleSide, point);\\n }\\n if (intersect === null)\\n return null;\\n _intersectionPointWorld.copy(point);\\n _intersectionPointWorld.applyMatrix4(object.matrixWorld);\\n const distance = raycaster.ray.origin.distanceTo(_intersectionPointWorld);\\n if (distance < raycaster.near || distance > raycaster.far)\\n return null;\\n return {\\n distance,\\n point: _intersectionPointWorld.clone(),\\n object\\n };\\n}\\nfunction checkBufferGeometryIntersection(object, material, raycaster, ray, position, morphPosition, morphTargetsRelative, uv, uv2, a, b, c) {\\n _vA.fromBufferAttribute(position, a);\\n _vB.fromBufferAttribute(position, b);\\n _vC.fromBufferAttribute(position, c);\\n const morphInfluences = object.morphTargetInfluences;\\n if (morphPosition && morphInfluences) {\\n _morphA.set(0, 0, 0);\\n _morphB.set(0, 0, 0);\\n _morphC.set(0, 0, 0);\\n for (let i = 0, il = morphPosition.length; i < il; i++) {\\n const influence = morphInfluences[i];\\n const morphAttribute = morphPosition[i];\\n if (influence === 0)\\n continue;\\n _tempA.fromBufferAttribute(morphAttribute, a);\\n _tempB.fromBufferAttribute(morphAttribute, b);\\n _tempC.fromBufferAttribute(morphAttribute, c);\\n if (morphTargetsRelative) {\\n _morphA.addScaledVector(_tempA, influence);\\n _morphB.addScaledVector(_tempB, influence);\\n _morphC.addScaledVector(_tempC, influence);\\n } else {\\n _morphA.addScaledVector(_tempA.sub(_vA), influence);\\n _morphB.addScaledVector(_tempB.sub(_vB), influence);\\n _morphC.addScaledVector(_tempC.sub(_vC), influence);\\n }\\n }\\n _vA.add(_morphA);\\n _vB.add(_morphB);\\n _vC.add(_morphC);\\n }\\n if (object.isSkinnedMesh) {\\n object.boneTransform(a, _vA);\\n object.boneTransform(b, _vB);\\n object.boneTransform(c, _vC);\\n }\\n const intersection = checkIntersection(object, material, raycaster, ray, _vA, _vB, _vC, _intersectionPoint);\\n if (intersection) {\\n if (uv) {\\n _uvA.fromBufferAttribute(uv, a);\\n _uvB.fromBufferAttribute(uv, b);\\n _uvC.fromBufferAttribute(uv, c);\\n intersection.uv = Triangle.getUV(_intersectionPoint, _vA, _vB, _vC, _uvA, _uvB, _uvC, new Vector2());\\n }\\n if (uv2) {\\n _uvA.fromBufferAttribute(uv2, a);\\n _uvB.fromBufferAttribute(uv2, b);\\n _uvC.fromBufferAttribute(uv2, c);\\n intersection.uv2 = Triangle.getUV(_intersectionPoint, _vA, _vB, _vC, _uvA, _uvB, _uvC, new Vector2());\\n }\\n const face = {\\n a,\\n b,\\n c,\\n normal: new Vector3(),\\n materialIndex: 0\\n };\\n Triangle.getNormal(_vA, _vB, _vC, face.normal);\\n intersection.face = face;\\n }\\n return intersection;\\n}\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/alphamap_fragment.glsl.js\\nvar alphamap_fragment_glsl_default = `\\n#ifdef USE_ALPHAMAP\\n\\n\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/alphamap_pars_fragment.glsl.js\\nvar alphamap_pars_fragment_glsl_default = `\\n#ifdef USE_ALPHAMAP\\n\\n\\tuniform sampler2D alphaMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/alphatest_fragment.glsl.js\\nvar alphatest_fragment_glsl_default = `\\n#ifdef USE_ALPHATEST\\n\\n\\tif ( diffuseColor.a < alphaTest ) discard;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/alphatest_pars_fragment.glsl.js\\nvar alphatest_pars_fragment_glsl_default = `\\n#ifdef USE_ALPHATEST\\n\\tuniform float alphaTest;\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/aomap_fragment.glsl.js\\nvar aomap_fragment_glsl_default = `\\n#ifdef USE_AOMAP\\n\\n\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\n\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\n\\n\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\n\\n\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\n\\n\\t\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\n\\t\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/aomap_pars_fragment.glsl.js\\nvar aomap_pars_fragment_glsl_default = `\\n#ifdef USE_AOMAP\\n\\n\\tuniform sampler2D aoMap;\\n\\tuniform float aoMapIntensity;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/begin_vertex.glsl.js\\nvar begin_vertex_glsl_default = `\\nvec3 transformed = vec3( position );\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/beginnormal_vertex.glsl.js\\nvar beginnormal_vertex_glsl_default = `\\nvec3 objectNormal = vec3( normal );\\n\\n#ifdef USE_TANGENT\\n\\n\\tvec3 objectTangent = vec3( tangent.xyz );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/bsdfs.glsl.js\\nvar bsdfs_glsl_default = `\\n\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\n\\n\\treturn RECIPROCAL_PI * diffuseColor;\\n\\n} // validated\\n\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\n\\n\\t// Original approximation by Christophe Schlick '94\\n\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\n\\n\\t// Optimized variant (presented by Epic at SIGGRAPH '13)\\n\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\n\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\n\\n\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\n\\n} // validated\\n\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\n\\n\\tfloat a2 = pow2( alpha );\\n\\n\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\n\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\n\\n\\treturn 0.5 / max( gv + gl, EPSILON );\\n\\n}\\n\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney\\\\u2019s reparameterization\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\n\\n\\tfloat a2 = pow2( alpha );\\n\\n\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\n\\n\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\n\\n}\\n\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\n\\n\\tfloat alpha = pow2( roughness ); // UE4's roughness\\n\\n\\tvec3 halfDir = normalize( lightDir + viewDir );\\n\\n\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\n\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\n\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\n\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\n\\n\\tvec3 F = F_Schlick( f0, f90, dotVH );\\n\\n\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\n\\n\\tfloat D = D_GGX( alpha, dotNH );\\n\\n\\treturn F * ( V * D );\\n\\n}\\n\\n// Rect Area Light\\n\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\n// code: https://github.com/selfshadow/ltc_code/\\n\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\n\\n\\tconst float LUT_SIZE = 64.0;\\n\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\n\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\n\\n\\tfloat dotNV = saturate( dot( N, V ) );\\n\\n\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\n\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\n\\n\\tuv = uv * LUT_SCALE + LUT_BIAS;\\n\\n\\treturn uv;\\n\\n}\\n\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\n\\n\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\n\\t// An approximation of the form factor of a horizon-clipped rectangle.\\n\\n\\tfloat l = length( f );\\n\\n\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\n\\n}\\n\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\n\\n\\tfloat x = dot( v1, v2 );\\n\\n\\tfloat y = abs( x );\\n\\n\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\n\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\n\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\n\\tfloat v = a / b;\\n\\n\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\n\\n\\treturn cross( v1, v2 ) * theta_sintheta;\\n\\n}\\n\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\n\\n\\t// bail if point is on back side of plane of light\\n\\t// assumes ccw winding order of light vertices\\n\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\n\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\n\\tvec3 lightNormal = cross( v1, v2 );\\n\\n\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\n\\n\\t// construct orthonormal basis around N\\n\\tvec3 T1, T2;\\n\\tT1 = normalize( V - N * dot( V, N ) );\\n\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\n\\n\\t// compute transform\\n\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\n\\n\\t// transform rect\\n\\tvec3 coords[ 4 ];\\n\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\n\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\n\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\n\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\n\\n\\t// project rect onto sphere\\n\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\n\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\n\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\n\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\n\\n\\t// calculate vector form factor\\n\\tvec3 vectorFormFactor = vec3( 0.0 );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\n\\n\\t// adjust for horizon clipping\\n\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\n\\n/*\\n\\t// alternate method of adjusting for horizon clipping (see referece)\\n\\t// refactoring required\\n\\tfloat len = length( vectorFormFactor );\\n\\tfloat z = vectorFormFactor.z / len;\\n\\n\\tconst float LUT_SIZE = 64.0;\\n\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\n\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\n\\n\\t// tabulated horizon-clipped sphere, apparently...\\n\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\n\\tuv = uv * LUT_SCALE + LUT_BIAS;\\n\\n\\tfloat scale = texture2D( ltc_2, uv ).w;\\n\\n\\tfloat result = len * scale;\\n*/\\n\\n\\treturn vec3( result );\\n\\n}\\n\\n// End Rect Area Light\\n\\n\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\n\\n\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\n\\treturn 0.25;\\n\\n}\\n\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\n\\n\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\n\\n}\\n\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\n\\n\\tvec3 halfDir = normalize( lightDir + viewDir );\\n\\n\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\n\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\n\\n\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\n\\n\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\n\\n\\tfloat D = D_BlinnPhong( shininess, dotNH );\\n\\n\\treturn F * ( G * D );\\n\\n} // validated\\n\\n#if defined( USE_SHEEN )\\n\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\nfloat D_Charlie( float roughness, float dotNH ) {\\n\\n\\tfloat alpha = pow2( roughness );\\n\\n\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\n\\tfloat invAlpha = 1.0 / alpha;\\n\\tfloat cos2h = dotNH * dotNH;\\n\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\n\\n\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\n\\n}\\n\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\n\\n\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\n\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\n\\n}\\n\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\n\\n\\tvec3 halfDir = normalize( lightDir + viewDir );\\n\\n\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\n\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\n\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\n\\n\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\n\\tfloat V = V_Neubelt( dotNV, dotNL );\\n\\n\\treturn sheenTint * ( D * V );\\n\\n}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/bumpmap_pars_fragment.glsl.js\\nvar bumpmap_pars_fragment_glsl_default = `\\n#ifdef USE_BUMPMAP\\n\\n\\tuniform sampler2D bumpMap;\\n\\tuniform float bumpScale;\\n\\n\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\n\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\n\\n\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\n\\n\\tvec2 dHdxy_fwd() {\\n\\n\\t\\tvec2 dSTdx = dFdx( vUv );\\n\\t\\tvec2 dSTdy = dFdy( vUv );\\n\\n\\t\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\n\\t\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\n\\t\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\n\\n\\t\\treturn vec2( dBx, dBy );\\n\\n\\t}\\n\\n\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\n\\n\\t\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\n\\n\\t\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\n\\t\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\n\\t\\tvec3 vN = surf_norm;\\t\\t// normalized\\n\\n\\t\\tvec3 R1 = cross( vSigmaY, vN );\\n\\t\\tvec3 R2 = cross( vN, vSigmaX );\\n\\n\\t\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\n\\n\\t\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\n\\t\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clipping_planes_fragment.glsl.js\\nvar clipping_planes_fragment_glsl_default = `\\n#if NUM_CLIPPING_PLANES > 0\\n\\n\\tvec4 plane;\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\n\\n\\t\\tplane = clippingPlanes[ i ];\\n\\t\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\n\\n\\t\\tbool clipped = true;\\n\\n\\t\\t#pragma unroll_loop_start\\n\\t\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\n\\n\\t\\t\\tplane = clippingPlanes[ i ];\\n\\t\\t\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\n\\n\\t\\t}\\n\\t\\t#pragma unroll_loop_end\\n\\n\\t\\tif ( clipped ) discard;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clipping_planes_pars_fragment.glsl.js\\nvar clipping_planes_pars_fragment_glsl_default = `\\n#if NUM_CLIPPING_PLANES > 0\\n\\n\\tvarying vec3 vClipPosition;\\n\\n\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clipping_planes_pars_vertex.glsl.js\\nvar clipping_planes_pars_vertex_glsl_default = `\\n#if NUM_CLIPPING_PLANES > 0\\n\\n\\tvarying vec3 vClipPosition;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clipping_planes_vertex.glsl.js\\nvar clipping_planes_vertex_glsl_default = `\\n#if NUM_CLIPPING_PLANES > 0\\n\\n\\tvClipPosition = - mvPosition.xyz;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/color_fragment.glsl.js\\nvar color_fragment_glsl_default = `\\n#if defined( USE_COLOR_ALPHA )\\n\\n\\tdiffuseColor *= vColor;\\n\\n#elif defined( USE_COLOR )\\n\\n\\tdiffuseColor.rgb *= vColor;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/color_pars_fragment.glsl.js\\nvar color_pars_fragment_glsl_default = `\\n#if defined( USE_COLOR_ALPHA )\\n\\n\\tvarying vec4 vColor;\\n\\n#elif defined( USE_COLOR )\\n\\n\\tvarying vec3 vColor;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/color_pars_vertex.glsl.js\\nvar color_pars_vertex_glsl_default = `\\n#if defined( USE_COLOR_ALPHA )\\n\\n\\tvarying vec4 vColor;\\n\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\n\\n\\tvarying vec3 vColor;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/color_vertex.glsl.js\\nvar color_vertex_glsl_default = `\\n#if defined( USE_COLOR_ALPHA )\\n\\n\\tvColor = vec4( 1.0 );\\n\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\n\\n\\tvColor = vec3( 1.0 );\\n\\n#endif\\n\\n#ifdef USE_COLOR\\n\\n\\tvColor *= color;\\n\\n#endif\\n\\n#ifdef USE_INSTANCING_COLOR\\n\\n\\tvColor.xyz *= instanceColor.xyz;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/common.glsl.js\\nvar common_glsl_default = `\\n#define PI 3.141592653589793\\n#define PI2 6.283185307179586\\n#define PI_HALF 1.5707963267948966\\n#define RECIPROCAL_PI 0.3183098861837907\\n#define RECIPROCAL_PI2 0.15915494309189535\\n#define EPSILON 1e-6\\n\\n#ifndef saturate\\n// may have defined saturate() already\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\n#endif\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\n\\nfloat pow2( const in float x ) { return x*x; }\\nfloat pow3( const in float x ) { return x*x*x; }\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\n\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\nhighp float rand( const in vec2 uv ) {\\n\\n\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\n\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\n\\n\\treturn fract( sin( sn ) * c );\\n\\n}\\n\\n#ifdef HIGH_PRECISION\\n\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\n#else\\n\\tfloat precisionSafeLength( vec3 v ) {\\n\\t\\tfloat maxComponent = max3( abs( v ) );\\n\\t\\treturn length( v / maxComponent ) * maxComponent;\\n\\t}\\n#endif\\n\\nstruct IncidentLight {\\n\\tvec3 color;\\n\\tvec3 direction;\\n\\tbool visible;\\n};\\n\\nstruct ReflectedLight {\\n\\tvec3 directDiffuse;\\n\\tvec3 directSpecular;\\n\\tvec3 indirectDiffuse;\\n\\tvec3 indirectSpecular;\\n};\\n\\nstruct GeometricContext {\\n\\tvec3 position;\\n\\tvec3 normal;\\n\\tvec3 viewDir;\\n#ifdef USE_CLEARCOAT\\n\\tvec3 clearcoatNormal;\\n#endif\\n};\\n\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\n\\n\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\n\\n}\\n\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\n\\n\\t// dir can be either a direction vector or a normal vector\\n\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\n\\n\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\n\\n}\\n\\nmat3 transposeMat3( const in mat3 m ) {\\n\\n\\tmat3 tmp;\\n\\n\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\n\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\n\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\n\\n\\treturn tmp;\\n\\n}\\n\\n// https://en.wikipedia.org/wiki/Relative_luminance\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\n\\n\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\n\\n\\treturn dot( weights, color.rgb );\\n\\n}\\n\\nbool isPerspectiveMatrix( mat4 m ) {\\n\\n\\treturn m[ 2 ][ 3 ] == - 1.0;\\n\\n}\\n\\nvec2 equirectUv( in vec3 dir ) {\\n\\n\\t// dir is assumed to be unit length\\n\\n\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\n\\n\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\n\\n\\treturn vec2( u, v );\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/cube_uv_reflection_fragment.glsl.js\\nvar cube_uv_reflection_fragment_glsl_default = `\\n#ifdef ENVMAP_TYPE_CUBE_UV\\n\\n\\t#define cubeUV_maxMipLevel 8.0\\n\\t#define cubeUV_minMipLevel 4.0\\n\\t#define cubeUV_maxTileSize 256.0\\n\\t#define cubeUV_minTileSize 16.0\\n\\n\\t// These shader functions convert between the UV coordinates of a single face of\\n\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\n\\t// sampling a textureCube (not generally normalized ).\\n\\n\\tfloat getFace( vec3 direction ) {\\n\\n\\t\\tvec3 absDirection = abs( direction );\\n\\n\\t\\tfloat face = - 1.0;\\n\\n\\t\\tif ( absDirection.x > absDirection.z ) {\\n\\n\\t\\t\\tif ( absDirection.x > absDirection.y )\\n\\n\\t\\t\\t\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\n\\n\\t\\t\\telse\\n\\n\\t\\t\\t\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tif ( absDirection.z > absDirection.y )\\n\\n\\t\\t\\t\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\n\\n\\t\\t\\telse\\n\\n\\t\\t\\t\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\n\\n\\t\\t}\\n\\n\\t\\treturn face;\\n\\n\\t}\\n\\n\\t// RH coordinate system; PMREM face-indexing convention\\n\\tvec2 getUV( vec3 direction, float face ) {\\n\\n\\t\\tvec2 uv;\\n\\n\\t\\tif ( face == 0.0 ) {\\n\\n\\t\\t\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\n\\n\\t\\t} else if ( face == 1.0 ) {\\n\\n\\t\\t\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\n\\n\\t\\t} else if ( face == 2.0 ) {\\n\\n\\t\\t\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\n\\n\\t\\t} else if ( face == 3.0 ) {\\n\\n\\t\\t\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\n\\n\\t\\t} else if ( face == 4.0 ) {\\n\\n\\t\\t\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\n\\n\\t\\t}\\n\\n\\t\\treturn 0.5 * ( uv + 1.0 );\\n\\n\\t}\\n\\n\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\n\\n\\t\\tfloat face = getFace( direction );\\n\\n\\t\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\n\\n\\t\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\n\\n\\t\\tfloat faceSize = exp2( mipInt );\\n\\n\\t\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\n\\n\\t\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\n\\n\\t\\tvec2 f = fract( uv );\\n\\n\\t\\tuv += 0.5 - f;\\n\\n\\t\\tif ( face > 2.0 ) {\\n\\n\\t\\t\\tuv.y += faceSize;\\n\\n\\t\\t\\tface -= 3.0;\\n\\n\\t\\t}\\n\\n\\t\\tuv.x += face * faceSize;\\n\\n\\t\\tif ( mipInt < cubeUV_maxMipLevel ) {\\n\\n\\t\\t\\tuv.y += 2.0 * cubeUV_maxTileSize;\\n\\n\\t\\t}\\n\\n\\t\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\n\\n\\t\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\n\\n\\t\\tuv *= texelSize;\\n\\n\\t\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\n\\n\\t\\tuv.x += texelSize;\\n\\n\\t\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\n\\n\\t\\tuv.y += texelSize;\\n\\n\\t\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\n\\n\\t\\tuv.x -= texelSize;\\n\\n\\t\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\n\\n\\t\\tvec3 tm = mix( tl, tr, f.x );\\n\\n\\t\\tvec3 bm = mix( bl, br, f.x );\\n\\n\\t\\treturn mix( tm, bm, f.y );\\n\\n\\t}\\n\\n\\t// These defines must match with PMREMGenerator\\n\\n\\t#define r0 1.0\\n\\t#define v0 0.339\\n\\t#define m0 - 2.0\\n\\t#define r1 0.8\\n\\t#define v1 0.276\\n\\t#define m1 - 1.0\\n\\t#define r4 0.4\\n\\t#define v4 0.046\\n\\t#define m4 2.0\\n\\t#define r5 0.305\\n\\t#define v5 0.016\\n\\t#define m5 3.0\\n\\t#define r6 0.21\\n\\t#define v6 0.0038\\n\\t#define m6 4.0\\n\\n\\tfloat roughnessToMip( float roughness ) {\\n\\n\\t\\tfloat mip = 0.0;\\n\\n\\t\\tif ( roughness >= r1 ) {\\n\\n\\t\\t\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\n\\n\\t\\t} else if ( roughness >= r4 ) {\\n\\n\\t\\t\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\n\\n\\t\\t} else if ( roughness >= r5 ) {\\n\\n\\t\\t\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\n\\n\\t\\t} else if ( roughness >= r6 ) {\\n\\n\\t\\t\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\n\\t\\t}\\n\\n\\t\\treturn mip;\\n\\n\\t}\\n\\n\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\n\\n\\t\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\n\\n\\t\\tfloat mipF = fract( mip );\\n\\n\\t\\tfloat mipInt = floor( mip );\\n\\n\\t\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\n\\n\\t\\tif ( mipF == 0.0 ) {\\n\\n\\t\\t\\treturn vec4( color0, 1.0 );\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\n\\n\\t\\t\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\n\\n\\t\\t}\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/defaultnormal_vertex.glsl.js\\nvar defaultnormal_vertex_glsl_default = `\\nvec3 transformedNormal = objectNormal;\\n\\n#ifdef USE_INSTANCING\\n\\n\\t// this is in lieu of a per-instance normal-matrix\\n\\t// shear transforms in the instance matrix are not supported\\n\\n\\tmat3 m = mat3( instanceMatrix );\\n\\n\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\n\\n\\ttransformedNormal = m * transformedNormal;\\n\\n#endif\\n\\ntransformedNormal = normalMatrix * transformedNormal;\\n\\n#ifdef FLIP_SIDED\\n\\n\\ttransformedNormal = - transformedNormal;\\n\\n#endif\\n\\n#ifdef USE_TANGENT\\n\\n\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\n\\n\\t#ifdef FLIP_SIDED\\n\\n\\t\\ttransformedTangent = - transformedTangent;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/displacementmap_pars_vertex.glsl.js\\nvar displacementmap_pars_vertex_glsl_default = `\\n#ifdef USE_DISPLACEMENTMAP\\n\\n\\tuniform sampler2D displacementMap;\\n\\tuniform float displacementScale;\\n\\tuniform float displacementBias;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/displacementmap_vertex.glsl.js\\nvar displacementmap_vertex_glsl_default = `\\n#ifdef USE_DISPLACEMENTMAP\\n\\n\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/emissivemap_fragment.glsl.js\\nvar emissivemap_fragment_glsl_default = `\\n#ifdef USE_EMISSIVEMAP\\n\\n\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\n\\n\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\n\\n\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/emissivemap_pars_fragment.glsl.js\\nvar emissivemap_pars_fragment_glsl_default = `\\n#ifdef USE_EMISSIVEMAP\\n\\n\\tuniform sampler2D emissiveMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/encodings_fragment.glsl.js\\nvar encodings_fragment_glsl_default = `\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/encodings_pars_fragment.glsl.js\\nvar encodings_pars_fragment_glsl_default = `\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\n\\nvec4 LinearToLinear( in vec4 value ) {\\n\\treturn value;\\n}\\n\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\n\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\n}\\n\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\n\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\n}\\n\\nvec4 sRGBToLinear( in vec4 value ) {\\n\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\n}\\n\\nvec4 LinearTosRGB( in vec4 value ) {\\n\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\n}\\n\\nvec4 RGBEToLinear( in vec4 value ) {\\n\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\n}\\n\\nvec4 LinearToRGBE( in vec4 value ) {\\n\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\n\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\n\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\n\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\n}\\n\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\n\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\n}\\n\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\n\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\n\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\n\\tM = ceil( M * 255.0 ) / 255.0;\\n\\treturn vec4( value.rgb / ( M * maxRange ), M );\\n}\\n\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\n\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\n}\\n\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\n\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\n\\tfloat D = max( maxRange / maxRGB, 1.0 );\\n\\t// NOTE: The implementation with min causes the shader to not compile on\\n\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\n\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\n\\t// D = min( floor( D ) / 255.0, 1.0 );\\n\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\n\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\n}\\n\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\n\\n// M matrix, for encoding\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\nvec4 LinearToLogLuv( in vec4 value ) {\\n\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\n\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\n\\tvec4 vResult;\\n\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\n\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\n\\tvResult.w = fract( Le );\\n\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\n\\treturn vResult;\\n}\\n\\n// Inverse M matrix, for decoding\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\nvec4 LogLuvToLinear( in vec4 value ) {\\n\\tfloat Le = value.z * 255.0 + value.w;\\n\\tvec3 Xp_Y_XYZp;\\n\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\n\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\n\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\n\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\n\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_fragment.glsl.js\\nvar envmap_fragment_glsl_default = `\\n#ifdef USE_ENVMAP\\n\\n\\t#ifdef ENV_WORLDPOS\\n\\n\\t\\tvec3 cameraToFrag;\\n\\n\\t\\tif ( isOrthographic ) {\\n\\n\\t\\t\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\n\\n\\t\\t}\\n\\n\\t\\t// Transforming Normal Vectors with the Inverse Transformation\\n\\t\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\n\\n\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\n\\t\\t\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\n\\n\\t\\t#else\\n\\n\\t\\t\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\n\\n\\t\\t#endif\\n\\n\\t#else\\n\\n\\t\\tvec3 reflectVec = vReflect;\\n\\n\\t#endif\\n\\n\\t#ifdef ENVMAP_TYPE_CUBE\\n\\n\\t\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\n\\n\\t\\tenvColor = envMapTexelToLinear( envColor );\\n\\n\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\n\\n\\t\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\n\\n\\t#else\\n\\n\\t\\tvec4 envColor = vec4( 0.0 );\\n\\n\\t#endif\\n\\n\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\n\\n\\t\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\n\\n\\t#elif defined( ENVMAP_BLENDING_MIX )\\n\\n\\t\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\n\\n\\t#elif defined( ENVMAP_BLENDING_ADD )\\n\\n\\t\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_common_pars_fragment.glsl.js\\nvar envmap_common_pars_fragment_glsl_default = `\\n#ifdef USE_ENVMAP\\n\\n\\tuniform float envMapIntensity;\\n\\tuniform float flipEnvMap;\\n\\tuniform int maxMipLevel;\\n\\n\\t#ifdef ENVMAP_TYPE_CUBE\\n\\t\\tuniform samplerCube envMap;\\n\\t#else\\n\\t\\tuniform sampler2D envMap;\\n\\t#endif\\n\\t\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_pars_fragment.glsl.js\\nvar envmap_pars_fragment_glsl_default = `\\n#ifdef USE_ENVMAP\\n\\n\\tuniform float reflectivity;\\n\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\n\\n\\t\\t#define ENV_WORLDPOS\\n\\n\\t#endif\\n\\n\\t#ifdef ENV_WORLDPOS\\n\\n\\t\\tvarying vec3 vWorldPosition;\\n\\t\\tuniform float refractionRatio;\\n\\t#else\\n\\t\\tvarying vec3 vReflect;\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_pars_vertex.glsl.js\\nvar envmap_pars_vertex_glsl_default = `\\n#ifdef USE_ENVMAP\\n\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\n\\n\\t\\t#define ENV_WORLDPOS\\n\\n\\t#endif\\n\\n\\t#ifdef ENV_WORLDPOS\\n\\t\\t\\n\\t\\tvarying vec3 vWorldPosition;\\n\\n\\t#else\\n\\n\\t\\tvarying vec3 vReflect;\\n\\t\\tuniform float refractionRatio;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_vertex.glsl.js\\nvar envmap_vertex_glsl_default = `\\n#ifdef USE_ENVMAP\\n\\n\\t#ifdef ENV_WORLDPOS\\n\\n\\t\\tvWorldPosition = worldPosition.xyz;\\n\\n\\t#else\\n\\n\\t\\tvec3 cameraToVertex;\\n\\n\\t\\tif ( isOrthographic ) {\\n\\n\\t\\t\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\n\\n\\t\\t}\\n\\n\\t\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\n\\n\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\n\\t\\t\\tvReflect = reflect( cameraToVertex, worldNormal );\\n\\n\\t\\t#else\\n\\n\\t\\t\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\n\\n\\t\\t#endif\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/fog_vertex.glsl.js\\nvar fog_vertex_glsl_default = `\\n#ifdef USE_FOG\\n\\n\\tvFogDepth = - mvPosition.z;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/fog_pars_vertex.glsl.js\\nvar fog_pars_vertex_glsl_default = `\\n#ifdef USE_FOG\\n\\n\\tvarying float vFogDepth;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/fog_fragment.glsl.js\\nvar fog_fragment_glsl_default = `\\n#ifdef USE_FOG\\n\\n\\t#ifdef FOG_EXP2\\n\\n\\t\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\n\\n\\t#else\\n\\n\\t\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\n\\n\\t#endif\\n\\n\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/fog_pars_fragment.glsl.js\\nvar fog_pars_fragment_glsl_default = `\\n#ifdef USE_FOG\\n\\n\\tuniform vec3 fogColor;\\n\\tvarying float vFogDepth;\\n\\n\\t#ifdef FOG_EXP2\\n\\n\\t\\tuniform float fogDensity;\\n\\n\\t#else\\n\\n\\t\\tuniform float fogNear;\\n\\t\\tuniform float fogFar;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/gradientmap_pars_fragment.glsl.js\\nvar gradientmap_pars_fragment_glsl_default = `\\n\\n#ifdef USE_GRADIENTMAP\\n\\n\\tuniform sampler2D gradientMap;\\n\\n#endif\\n\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\n\\n\\t// dotNL will be from -1.0 to 1.0\\n\\tfloat dotNL = dot( normal, lightDirection );\\n\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\n\\n\\t#ifdef USE_GRADIENTMAP\\n\\n\\t\\treturn texture2D( gradientMap, coord ).rgb;\\n\\n\\t#else\\n\\n\\t\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\n\\n\\t#endif\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lightmap_fragment.glsl.js\\nvar lightmap_fragment_glsl_default = `\\n#ifdef USE_LIGHTMAP\\n\\n\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\n\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\n\\n\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\n\\t\\tlightMapIrradiance *= PI;\\n\\n\\t#endif\\n\\n\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lightmap_pars_fragment.glsl.js\\nvar lightmap_pars_fragment_glsl_default = `\\n#ifdef USE_LIGHTMAP\\n\\n\\tuniform sampler2D lightMap;\\n\\tuniform float lightMapIntensity;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_lambert_vertex.glsl.js\\nvar lights_lambert_vertex_glsl_default = `\\nvec3 diffuse = vec3( 1.0 );\\n\\nGeometricContext geometry;\\ngeometry.position = mvPosition.xyz;\\ngeometry.normal = normalize( transformedNormal );\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\n\\nGeometricContext backGeometry;\\nbackGeometry.position = geometry.position;\\nbackGeometry.normal = -geometry.normal;\\nbackGeometry.viewDir = geometry.viewDir;\\n\\nvLightFront = vec3( 0.0 );\\nvIndirectFront = vec3( 0.0 );\\n#ifdef DOUBLE_SIDED\\n\\tvLightBack = vec3( 0.0 );\\n\\tvIndirectBack = vec3( 0.0 );\\n#endif\\n\\nIncidentLight directLight;\\nfloat dotNL;\\nvec3 directLightColor_Diffuse;\\n\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\n\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\n\\n#ifdef DOUBLE_SIDED\\n\\n\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\n\\n\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\n\\n#endif\\n\\n#if NUM_POINT_LIGHTS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\n\\t\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\n\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = directLight.color;\\n\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\t\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if NUM_SPOT_LIGHTS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\n\\t\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\n\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = directLight.color;\\n\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\t\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#endif\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if NUM_DIR_LIGHTS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\n\\t\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\n\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = directLight.color;\\n\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\t\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if NUM_HEMI_LIGHTS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\n\\n\\t\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\n\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\t\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_pars_begin.glsl.js\\nvar lights_pars_begin_glsl_default = `\\nuniform bool receiveShadow;\\nuniform vec3 ambientLightColor;\\nuniform vec3 lightProbe[ 9 ];\\n\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\n\\n\\t// normal is assumed to have unit length\\n\\n\\tfloat x = normal.x, y = normal.y, z = normal.z;\\n\\n\\t// band 0\\n\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\n\\n\\t// band 1\\n\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\n\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\n\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\n\\n\\t// band 2\\n\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\n\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\n\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\n\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\n\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\n\\n\\treturn result;\\n\\n}\\n\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\n\\n\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\n\\n\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\n\\n\\treturn irradiance;\\n\\n}\\n\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\n\\n\\tvec3 irradiance = ambientLightColor;\\n\\n\\treturn irradiance;\\n\\n}\\n\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\n\\n\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\n\\n\\t\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\n\\t\\t// page 32, equation 26: E[window1]\\n\\t\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\n\\t\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\n\\n\\t\\tif ( cutoffDistance > 0.0 ) {\\n\\n\\t\\t\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\n\\n\\t\\t}\\n\\n\\t\\treturn distanceFalloff;\\n\\n\\t#else\\n\\n\\t\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\n\\n\\t\\t\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\n\\n\\t\\t}\\n\\n\\t\\treturn 1.0;\\n\\n\\t#endif\\n\\n}\\n\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\n\\n\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\n\\n}\\n\\n#if NUM_DIR_LIGHTS > 0\\n\\n\\tstruct DirectionalLight {\\n\\t\\tvec3 direction;\\n\\t\\tvec3 color;\\n\\t};\\n\\n\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\n\\n\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\n\\n\\t\\tlight.color = directionalLight.color;\\n\\t\\tlight.direction = directionalLight.direction;\\n\\t\\tlight.visible = true;\\n\\n\\t}\\n\\n#endif\\n\\n\\n#if NUM_POINT_LIGHTS > 0\\n\\n\\tstruct PointLight {\\n\\t\\tvec3 position;\\n\\t\\tvec3 color;\\n\\t\\tfloat distance;\\n\\t\\tfloat decay;\\n\\t};\\n\\n\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\n\\n\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\n\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\n\\n\\t\\tvec3 lVector = pointLight.position - geometry.position;\\n\\n\\t\\tlight.direction = normalize( lVector );\\n\\n\\t\\tfloat lightDistance = length( lVector );\\n\\n\\t\\tlight.color = pointLight.color;\\n\\t\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\n\\t\\tlight.visible = ( light.color != vec3( 0.0 ) );\\n\\n\\t}\\n\\n#endif\\n\\n\\n#if NUM_SPOT_LIGHTS > 0\\n\\n\\tstruct SpotLight {\\n\\t\\tvec3 position;\\n\\t\\tvec3 direction;\\n\\t\\tvec3 color;\\n\\t\\tfloat distance;\\n\\t\\tfloat decay;\\n\\t\\tfloat coneCos;\\n\\t\\tfloat penumbraCos;\\n\\t};\\n\\n\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\n\\n\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\n\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\n\\n\\t\\tvec3 lVector = spotLight.position - geometry.position;\\n\\n\\t\\tlight.direction = normalize( lVector );\\n\\n\\t\\tfloat angleCos = dot( light.direction, spotLight.direction );\\n\\n\\t\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\n\\n\\t\\tif ( spotAttenuation > 0.0 ) {\\n\\n\\t\\t\\tfloat lightDistance = length( lVector );\\n\\n\\t\\t\\tlight.color = spotLight.color * spotAttenuation;\\n\\t\\t\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\n\\t\\t\\tlight.visible = ( light.color != vec3( 0.0 ) );\\n\\n\\t\\t} else {\\n\\n\\t\\t\\tlight.color = vec3( 0.0 );\\n\\t\\t\\tlight.visible = false;\\n\\n\\t\\t}\\n\\n\\t}\\n\\n#endif\\n\\n\\n#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\tstruct RectAreaLight {\\n\\t\\tvec3 color;\\n\\t\\tvec3 position;\\n\\t\\tvec3 halfWidth;\\n\\t\\tvec3 halfHeight;\\n\\t};\\n\\n\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\n\\t// BRDF approximation Texture is 64x64\\n\\tuniform sampler2D ltc_1; // RGBA Float\\n\\tuniform sampler2D ltc_2; // RGBA Float\\n\\n\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\n\\n#endif\\n\\n\\n#if NUM_HEMI_LIGHTS > 0\\n\\n\\tstruct HemisphereLight {\\n\\t\\tvec3 direction;\\n\\t\\tvec3 skyColor;\\n\\t\\tvec3 groundColor;\\n\\t};\\n\\n\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\n\\n\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\n\\n\\t\\tfloat dotNL = dot( normal, hemiLight.direction );\\n\\t\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\n\\n\\t\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\n\\n\\t\\treturn irradiance;\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/envmap_physical_pars_fragment.glsl.js\\nvar envmap_physical_pars_fragment_glsl_default = `\\n#if defined( USE_ENVMAP )\\n\\n\\t#ifdef ENVMAP_MODE_REFRACTION\\n\\n\\t\\tuniform float refractionRatio;\\n\\n\\t#endif\\n\\n\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\n\\n\\t\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\n\\n\\t\\t\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\n\\n\\t\\t\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\n\\n\\t\\t\\treturn PI * envMapColor.rgb * envMapIntensity;\\n\\n\\t\\t#else\\n\\n\\t\\t\\treturn vec3( 0.0 );\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\n\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\n\\n\\t\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\n\\n\\t\\t\\tvec3 reflectVec;\\n\\n\\t\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\n\\t\\t\\t\\treflectVec = reflect( - viewDir, normal );\\n\\n\\t\\t\\t\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\n\\t\\t\\t\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\n\\n\\t\\t\\t#else\\n\\n\\t\\t\\t\\treflectVec = refract( - viewDir, normal, refractionRatio );\\n\\n\\t\\t\\t#endif\\n\\n\\t\\t\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\n\\n\\t\\t\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\n\\n\\t\\t\\treturn envMapColor.rgb * envMapIntensity;\\n\\n\\t\\t#else\\n\\n\\t\\t\\treturn vec3( 0.0 );\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_toon_fragment.glsl.js\\nvar lights_toon_fragment_glsl_default = `\\nToonMaterial material;\\nmaterial.diffuseColor = diffuseColor.rgb;\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_toon_pars_fragment.glsl.js\\nvar lights_toon_pars_fragment_glsl_default = `\\nvarying vec3 vViewPosition;\\n\\nstruct ToonMaterial {\\n\\n\\tvec3 diffuseColor;\\n\\n};\\n\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\n\\n\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n\\n}\\n\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n\\n}\\n\\n#define RE_Direct\\t\\t\\t\\tRE_Direct_Toon\\n#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_Toon\\n\\n#define Material_LightProbeLOD( material )\\t(0)\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_phong_fragment.glsl.js\\nvar lights_phong_fragment_glsl_default = `\\nBlinnPhongMaterial material;\\nmaterial.diffuseColor = diffuseColor.rgb;\\nmaterial.specularColor = specular;\\nmaterial.specularShininess = shininess;\\nmaterial.specularStrength = specularStrength;\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_phong_pars_fragment.glsl.js\\nvar lights_phong_pars_fragment_glsl_default = `\\nvarying vec3 vViewPosition;\\n\\nstruct BlinnPhongMaterial {\\n\\n\\tvec3 diffuseColor;\\n\\tvec3 specularColor;\\n\\tfloat specularShininess;\\n\\tfloat specularStrength;\\n\\n};\\n\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\n\\tvec3 irradiance = dotNL * directLight.color;\\n\\n\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n\\n\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\n\\n}\\n\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n\\n}\\n\\n#define RE_Direct\\t\\t\\t\\tRE_Direct_BlinnPhong\\n#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_BlinnPhong\\n\\n#define Material_LightProbeLOD( material )\\t(0)\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_physical_fragment.glsl.js\\nvar lights_physical_fragment_glsl_default = `\\nPhysicalMaterial material;\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\n\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\n\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\nmaterial.roughness += geometryRoughness;\\nmaterial.roughness = min( material.roughness, 1.0 );\\n\\n#ifdef IOR\\n\\n\\t#ifdef SPECULAR\\n\\n\\t\\tfloat specularIntensityFactor = specularIntensity;\\n\\t\\tvec3 specularTintFactor = specularTint;\\n\\n\\t\\t#ifdef USE_SPECULARINTENSITYMAP\\n\\n\\t\\t\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\n\\n\\t\\t#endif\\n\\n\\t\\t#ifdef USE_SPECULARTINTMAP\\n\\n\\t\\t\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\n\\n\\t\\t#endif\\n\\n\\t\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\n\\n\\t#else\\n\\n\\t\\tfloat specularIntensityFactor = 1.0;\\n\\t\\tvec3 specularTintFactor = vec3( 1.0 );\\n\\t\\tmaterial.specularF90 = 1.0;\\n\\n\\t#endif\\n\\n\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\n\\n#else\\n\\n\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\n\\tmaterial.specularF90 = 1.0;\\n\\n#endif\\n\\n#ifdef USE_CLEARCOAT\\n\\n\\tmaterial.clearcoat = clearcoat;\\n\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\n\\tmaterial.clearcoatF0 = vec3( 0.04 );\\n\\tmaterial.clearcoatF90 = 1.0;\\n\\n\\t#ifdef USE_CLEARCOATMAP\\n\\n\\t\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\n\\n\\t#endif\\n\\n\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\n\\n\\t\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\n\\n\\t#endif\\n\\n\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\n\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\n\\tmaterial.clearcoatRoughness += geometryRoughness;\\n\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\n\\n#endif\\n\\n#ifdef USE_SHEEN\\n\\n\\tmaterial.sheenTint = sheenTint;\\n\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_physical_pars_fragment.glsl.js\\nvar lights_physical_pars_fragment_glsl_default = `\\nstruct PhysicalMaterial {\\n\\n\\tvec3 diffuseColor;\\n\\tfloat roughness;\\n\\tvec3 specularColor;\\n\\tfloat specularF90;\\n\\n\\t#ifdef USE_CLEARCOAT\\n\\t\\tfloat clearcoat;\\n\\t\\tfloat clearcoatRoughness;\\n\\t\\tvec3 clearcoatF0;\\n\\t\\tfloat clearcoatF90;\\n\\t#endif\\n\\n\\t#ifdef USE_SHEEN\\n\\t\\tvec3 sheenTint;\\n\\t\\tfloat sheenRoughness;\\n\\t#endif\\n\\n};\\n\\n// temporary\\nvec3 clearcoatSpecular = vec3( 0.0 );\\n\\n// Analytical approximation of the DFG LUT, one half of the\\n// split-sum approximation used in indirect specular lighting.\\n// via 'environmentBRDF' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\n\\n\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\n\\n\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\n\\n\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\n\\n\\tvec4 r = roughness * c0 + c1;\\n\\n\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\n\\n\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\n\\n\\treturn fab;\\n\\n}\\n\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\n\\n\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\n\\n\\treturn specularColor * fab.x + specularF90 * fab.y;\\n\\n}\\n\\n// Fdez-Ag\\\\xFCera's \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\n// Approximates multiscattering in order to preserve energy.\\n// http://www.jcgt.org/published/0008/01/03/\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\n\\n\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\n\\n\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\n\\n\\tfloat Ess = fab.x + fab.y;\\n\\tfloat Ems = 1.0 - Ess;\\n\\n\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\n\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\n\\n\\tsingleScatter += FssEss;\\n\\tmultiScatter += Fms * Ems;\\n\\n}\\n\\n#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\t\\tvec3 normal = geometry.normal;\\n\\t\\tvec3 viewDir = geometry.viewDir;\\n\\t\\tvec3 position = geometry.position;\\n\\t\\tvec3 lightPos = rectAreaLight.position;\\n\\t\\tvec3 halfWidth = rectAreaLight.halfWidth;\\n\\t\\tvec3 halfHeight = rectAreaLight.halfHeight;\\n\\t\\tvec3 lightColor = rectAreaLight.color;\\n\\t\\tfloat roughness = material.roughness;\\n\\n\\t\\tvec3 rectCoords[ 4 ];\\n\\t\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\n\\t\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\n\\t\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\n\\t\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\n\\n\\t\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\n\\n\\t\\tvec4 t1 = texture2D( ltc_1, uv );\\n\\t\\tvec4 t2 = texture2D( ltc_2, uv );\\n\\n\\t\\tmat3 mInv = mat3(\\n\\t\\t\\tvec3( t1.x, 0, t1.y ),\\n\\t\\t\\tvec3( 0, 1, 0 ),\\n\\t\\t\\tvec3( t1.z, 0, t1.w )\\n\\t\\t);\\n\\n\\t\\t// LTC Fresnel Approximation by Stephen Hill\\n\\t\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\n\\t\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\n\\n\\t\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\n\\n\\t\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\n\\n\\t}\\n\\n#endif\\n\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\n\\n\\tvec3 irradiance = dotNL * directLight.color;\\n\\n\\t#ifdef USE_CLEARCOAT\\n\\n\\t\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\n\\n\\t\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\n\\n\\t\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\n\\n\\t#endif\\n\\n\\t#ifdef USE_SHEEN\\n\\n\\t\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\n\\n\\t#endif\\n\\n\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\n\\n\\n\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n}\\n\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\n\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\n\\n}\\n\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\n\\n\\t#ifdef USE_CLEARCOAT\\n\\n\\t\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\n\\n\\t#endif\\n\\n\\t// Both indirect specular and indirect diffuse light accumulate here\\n\\n\\tvec3 singleScattering = vec3( 0.0 );\\n\\tvec3 multiScattering = vec3( 0.0 );\\n\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\n\\n\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\n\\n\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\n\\n\\treflectedLight.indirectSpecular += radiance * singleScattering;\\n\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\n\\n\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\n\\n}\\n\\n#define RE_Direct\\t\\t\\t\\tRE_Direct_Physical\\n#define RE_Direct_RectArea\\t\\tRE_Direct_RectArea_Physical\\n#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_Physical\\n#define RE_IndirectSpecular\\t\\tRE_IndirectSpecular_Physical\\n\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\n\\n\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_fragment_begin.glsl.js\\nvar lights_fragment_begin_glsl_default = `\\n/**\\n * This is a template that can be used to light a material, it uses pluggable\\n * RenderEquations (RE)for specific lighting scenarios.\\n *\\n * Instructions for use:\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\n *\\n * TODO:\\n * - Add area light support.\\n * - Add sphere light support.\\n * - Add diffuse light probe (irradiance cubemap) support.\\n */\\n\\nGeometricContext geometry;\\n\\ngeometry.position = - vViewPosition;\\ngeometry.normal = normal;\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\n\\n#ifdef USE_CLEARCOAT\\n\\n\\tgeometry.clearcoatNormal = clearcoatNormal;\\n\\n#endif\\n\\nIncidentLight directLight;\\n\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\n\\n\\tPointLight pointLight;\\n\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\n\\tPointLightShadow pointLightShadow;\\n\\t#endif\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\n\\t\\tpointLight = pointLights[ i ];\\n\\n\\t\\tgetPointLightInfo( pointLight, geometry, directLight );\\n\\n\\t\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\n\\t\\tpointLightShadow = pointLightShadows[ i ];\\n\\t\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\n\\t\\t#endif\\n\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\n\\n\\tSpotLight spotLight;\\n\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\n\\tSpotLightShadow spotLightShadow;\\n\\t#endif\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\n\\t\\tspotLight = spotLights[ i ];\\n\\n\\t\\tgetSpotLightInfo( spotLight, geometry, directLight );\\n\\n\\t\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\n\\t\\tspotLightShadow = spotLightShadows[ i ];\\n\\t\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\n\\t\\t#endif\\n\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\n\\n\\tDirectionalLight directionalLight;\\n\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\n\\tDirectionalLightShadow directionalLightShadow;\\n\\t#endif\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\n\\t\\tdirectionalLight = directionalLights[ i ];\\n\\n\\t\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\n\\n\\t\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\n\\t\\tdirectionalLightShadow = directionalLightShadows[ i ];\\n\\t\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\n\\t\\t#endif\\n\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\n\\n\\tRectAreaLight rectAreaLight;\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\n\\n\\t\\trectAreaLight = rectAreaLights[ i ];\\n\\t\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n#endif\\n\\n#if defined( RE_IndirectDiffuse )\\n\\n\\tvec3 iblIrradiance = vec3( 0.0 );\\n\\n\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\n\\n\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\n\\n\\t#if ( NUM_HEMI_LIGHTS > 0 )\\n\\n\\t\\t#pragma unroll_loop_start\\n\\t\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\n\\n\\t\\t\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\n\\n\\t\\t}\\n\\t\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n#endif\\n\\n#if defined( RE_IndirectSpecular )\\n\\n\\tvec3 radiance = vec3( 0.0 );\\n\\tvec3 clearcoatRadiance = vec3( 0.0 );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_fragment_maps.glsl.js\\nvar lights_fragment_maps_glsl_default = `\\n#if defined( RE_IndirectDiffuse )\\n\\n\\t#ifdef USE_LIGHTMAP\\n\\n\\t\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\n\\t\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\n\\n\\t\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\n\\t\\t\\tlightMapIrradiance *= PI;\\n\\n\\t\\t#endif\\n\\n\\t\\tirradiance += lightMapIrradiance;\\n\\n\\t#endif\\n\\n\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\n\\n\\t\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\n\\n\\t#endif\\n\\n#endif\\n\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\n\\n\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\n\\n\\t#ifdef USE_CLEARCOAT\\n\\n\\t\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/lights_fragment_end.glsl.js\\nvar lights_fragment_end_glsl_default = `\\n#if defined( RE_IndirectDiffuse )\\n\\n\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\n\\n#endif\\n\\n#if defined( RE_IndirectSpecular )\\n\\n\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/logdepthbuf_fragment.glsl.js\\nvar logdepthbuf_fragment_glsl_default = `\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\n\\n\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\n\\t// on some platforms. See issue #17623.\\n\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/logdepthbuf_pars_fragment.glsl.js\\nvar logdepthbuf_pars_fragment_glsl_default = `\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\n\\n\\tuniform float logDepthBufFC;\\n\\tvarying float vFragDepth;\\n\\tvarying float vIsPerspective;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/logdepthbuf_pars_vertex.glsl.js\\nvar logdepthbuf_pars_vertex_glsl_default = `\\n#ifdef USE_LOGDEPTHBUF\\n\\n\\t#ifdef USE_LOGDEPTHBUF_EXT\\n\\n\\t\\tvarying float vFragDepth;\\n\\t\\tvarying float vIsPerspective;\\n\\n\\t#else\\n\\n\\t\\tuniform float logDepthBufFC;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/logdepthbuf_vertex.glsl.js\\nvar logdepthbuf_vertex_glsl_default = `\\n#ifdef USE_LOGDEPTHBUF\\n\\n\\t#ifdef USE_LOGDEPTHBUF_EXT\\n\\n\\t\\tvFragDepth = 1.0 + gl_Position.w;\\n\\t\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\n\\n\\t#else\\n\\n\\t\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\n\\n\\t\\t\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\n\\n\\t\\t\\tgl_Position.z *= gl_Position.w;\\n\\n\\t\\t}\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/map_fragment.glsl.js\\nvar map_fragment_glsl_default = `\\n#ifdef USE_MAP\\n\\n\\tvec4 texelColor = texture2D( map, vUv );\\n\\n\\ttexelColor = mapTexelToLinear( texelColor );\\n\\tdiffuseColor *= texelColor;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/map_pars_fragment.glsl.js\\nvar map_pars_fragment_glsl_default = `\\n#ifdef USE_MAP\\n\\n\\tuniform sampler2D map;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/map_particle_fragment.glsl.js\\nvar map_particle_fragment_glsl_default = `\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\n\\n\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\n\\n#endif\\n\\n#ifdef USE_MAP\\n\\n\\tvec4 mapTexel = texture2D( map, uv );\\n\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\n\\n#endif\\n\\n#ifdef USE_ALPHAMAP\\n\\n\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/map_particle_pars_fragment.glsl.js\\nvar map_particle_pars_fragment_glsl_default = `\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\n\\n\\tuniform mat3 uvTransform;\\n\\n#endif\\n\\n#ifdef USE_MAP\\n\\n\\tuniform sampler2D map;\\n\\n#endif\\n\\n#ifdef USE_ALPHAMAP\\n\\n\\tuniform sampler2D alphaMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/metalnessmap_fragment.glsl.js\\nvar metalnessmap_fragment_glsl_default = `\\nfloat metalnessFactor = metalness;\\n\\n#ifdef USE_METALNESSMAP\\n\\n\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\n\\n\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\n\\tmetalnessFactor *= texelMetalness.b;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/metalnessmap_pars_fragment.glsl.js\\nvar metalnessmap_pars_fragment_glsl_default = `\\n#ifdef USE_METALNESSMAP\\n\\n\\tuniform sampler2D metalnessMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/morphnormal_vertex.glsl.js\\nvar morphnormal_vertex_glsl_default = `\\n#ifdef USE_MORPHNORMALS\\n\\n\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\n\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\n\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\n\\tobjectNormal *= morphTargetBaseInfluence;\\n\\n\\t#ifdef MORPHTARGETS_TEXTURE\\n\\n\\t\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\n\\n\\t\\t\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\n\\n\\t\\t}\\n\\n\\t#else\\n\\n\\t\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\n\\t\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\n\\t\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\n\\t\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/morphtarget_pars_vertex.glsl.js\\nvar morphtarget_pars_vertex_glsl_default = `\\n#ifdef USE_MORPHTARGETS\\n\\n\\tuniform float morphTargetBaseInfluence;\\n\\n\\t#ifdef MORPHTARGETS_TEXTURE\\n\\n\\t\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\n\\t\\tuniform sampler2DArray morphTargetsTexture;\\n\\t\\tuniform vec2 morphTargetsTextureSize;\\n\\n\\t\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\n\\n\\t\\t\\tfloat texelIndex = float( vertexIndex * stride + offset );\\n\\t\\t\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\n\\t\\t\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\n\\n\\t\\t\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\n\\t\\t\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\n\\n\\t\\t}\\n\\n\\t#else\\n\\n\\t\\t#ifndef USE_MORPHNORMALS\\n\\n\\t\\t\\tuniform float morphTargetInfluences[ 8 ];\\n\\n\\t\\t#else\\n\\n\\t\\t\\tuniform float morphTargetInfluences[ 4 ];\\n\\n\\t\\t#endif\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/morphtarget_vertex.glsl.js\\nvar morphtarget_vertex_glsl_default = `\\n#ifdef USE_MORPHTARGETS\\n\\n\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\n\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\n\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\n\\ttransformed *= morphTargetBaseInfluence;\\n\\n\\t#ifdef MORPHTARGETS_TEXTURE\\n\\n\\t\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\n\\n\\t\\t\\t#ifndef USE_MORPHNORMALS\\n\\n\\t\\t\\t\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\n\\n\\t\\t\\t#else\\n\\n\\t\\t\\t\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\n\\n\\t\\t\\t#endif\\n\\n\\t\\t}\\n\\n\\t#else\\n\\n\\t\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\n\\t\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\n\\t\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\n\\t\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\n\\n\\t\\t#ifndef USE_MORPHNORMALS\\n\\n\\t\\t\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\n\\t\\t\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\n\\t\\t\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\n\\t\\t\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\n\\n\\t\\t#endif\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normal_fragment_begin.glsl.js\\nvar normal_fragment_begin_glsl_default = `\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\n\\n#ifdef FLAT_SHADED\\n\\n\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\n\\n\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\n\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\n\\tvec3 normal = normalize( cross( fdx, fdy ) );\\n\\n#else\\n\\n\\tvec3 normal = normalize( vNormal );\\n\\n\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\tnormal = normal * faceDirection;\\n\\n\\t#endif\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tvec3 tangent = normalize( vTangent );\\n\\t\\tvec3 bitangent = normalize( vBitangent );\\n\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\t\\ttangent = tangent * faceDirection;\\n\\t\\t\\tbitangent = bitangent * faceDirection;\\n\\n\\t\\t#endif\\n\\n\\t\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\n\\n\\t\\t\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\n\\n\\t\\t#endif\\n\\n\\t#endif\\n\\n#endif\\n\\n// non perturbed normal for clearcoat among others\\n\\nvec3 geometryNormal = normal;\\n\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normal_fragment_maps.glsl.js\\nvar normal_fragment_maps_glsl_default = `\\n\\n#ifdef OBJECTSPACE_NORMALMAP\\n\\n\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\n\\n\\t#ifdef FLIP_SIDED\\n\\n\\t\\tnormal = - normal;\\n\\n\\t#endif\\n\\n\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\tnormal = normal * faceDirection;\\n\\n\\t#endif\\n\\n\\tnormal = normalize( normalMatrix * normal );\\n\\n#elif defined( TANGENTSPACE_NORMALMAP )\\n\\n\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\n\\tmapN.xy *= normalScale;\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tnormal = normalize( vTBN * mapN );\\n\\n\\t#else\\n\\n\\t\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\n\\n\\t#endif\\n\\n#elif defined( USE_BUMPMAP )\\n\\n\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normal_pars_fragment.glsl.js\\nvar normal_pars_fragment_glsl_default = `\\n#ifndef FLAT_SHADED\\n\\n\\tvarying vec3 vNormal;\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tvarying vec3 vTangent;\\n\\t\\tvarying vec3 vBitangent;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normal_pars_vertex.glsl.js\\nvar normal_pars_vertex_glsl_default = `\\n#ifndef FLAT_SHADED\\n\\n\\tvarying vec3 vNormal;\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tvarying vec3 vTangent;\\n\\t\\tvarying vec3 vBitangent;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normal_vertex.glsl.js\\nvar normal_vertex_glsl_default = `\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\n\\n\\tvNormal = normalize( transformedNormal );\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tvTangent = normalize( transformedTangent );\\n\\t\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/normalmap_pars_fragment.glsl.js\\nvar normalmap_pars_fragment_glsl_default = `\\n#ifdef USE_NORMALMAP\\n\\n\\tuniform sampler2D normalMap;\\n\\tuniform vec2 normalScale;\\n\\n#endif\\n\\n#ifdef OBJECTSPACE_NORMALMAP\\n\\n\\tuniform mat3 normalMatrix;\\n\\n#endif\\n\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\n\\n\\t// Normal Mapping Without Precomputed Tangents\\n\\t// http://www.thetenthplanet.de/archives/1180\\n\\n\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\n\\n\\t\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\n\\n\\t\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\n\\t\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\n\\t\\tvec2 st0 = dFdx( vUv.st );\\n\\t\\tvec2 st1 = dFdy( vUv.st );\\n\\n\\t\\tvec3 N = surf_norm; // normalized\\n\\n\\t\\tvec3 q1perp = cross( q1, N );\\n\\t\\tvec3 q0perp = cross( N, q0 );\\n\\n\\t\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\n\\t\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\n\\n\\t\\tfloat det = max( dot( T, T ), dot( B, B ) );\\n\\t\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\n\\n\\t\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clearcoat_normal_fragment_begin.glsl.js\\nvar clearcoat_normal_fragment_begin_glsl_default = `\\n#ifdef USE_CLEARCOAT\\n\\n\\tvec3 clearcoatNormal = geometryNormal;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clearcoat_normal_fragment_maps.glsl.js\\nvar clearcoat_normal_fragment_maps_glsl_default = `\\n#ifdef USE_CLEARCOAT_NORMALMAP\\n\\n\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\n\\tclearcoatMapN.xy *= clearcoatNormalScale;\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\n\\n\\t#else\\n\\n\\t\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/clearcoat_pars_fragment.glsl.js\\nvar clearcoat_pars_fragment_glsl_default = `\\n\\n#ifdef USE_CLEARCOATMAP\\n\\n\\tuniform sampler2D clearcoatMap;\\n\\n#endif\\n\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\n\\n\\tuniform sampler2D clearcoatRoughnessMap;\\n\\n#endif\\n\\n#ifdef USE_CLEARCOAT_NORMALMAP\\n\\n\\tuniform sampler2D clearcoatNormalMap;\\n\\tuniform vec2 clearcoatNormalScale;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/output_fragment.glsl.js\\nvar output_fragment_glsl_default = `\\n#ifdef OPAQUE\\ndiffuseColor.a = 1.0;\\n#endif\\n\\n// https://github.com/mrdoob/three.js/pull/22425\\n#ifdef USE_TRANSMISSION\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\n#endif\\n\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/packing.glsl.js\\nvar packing_glsl_default = `\\nvec3 packNormalToRGB( const in vec3 normal ) {\\n\\treturn normalize( normal ) * 0.5 + 0.5;\\n}\\n\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\n\\treturn 2.0 * rgb.xyz - 1.0;\\n}\\n\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\n\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\n\\nconst float ShiftRight8 = 1. / 256.;\\n\\nvec4 packDepthToRGBA( const in float v ) {\\n\\tvec4 r = vec4( fract( v * PackFactors ), v );\\n\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\n\\treturn r * PackUpscale;\\n}\\n\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\n\\treturn dot( v, UnpackFactors );\\n}\\n\\nvec4 pack2HalfToRGBA( vec2 v ) {\\n\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\n\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\n}\\n\\nvec2 unpackRGBATo2Half( vec4 v ) {\\n\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\n}\\n\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\n\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\n\\treturn ( viewZ + near ) / ( near - far );\\n}\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\n\\treturn linearClipZ * ( near - far ) - near;\\n}\\n\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\n\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\n\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\n}\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\n\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/premultiplied_alpha_fragment.glsl.js\\nvar premultiplied_alpha_fragment_glsl_default = `\\n#ifdef PREMULTIPLIED_ALPHA\\n\\n\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\n\\tgl_FragColor.rgb *= gl_FragColor.a;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/project_vertex.glsl.js\\nvar project_vertex_glsl_default = `\\nvec4 mvPosition = vec4( transformed, 1.0 );\\n\\n#ifdef USE_INSTANCING\\n\\n\\tmvPosition = instanceMatrix * mvPosition;\\n\\n#endif\\n\\nmvPosition = modelViewMatrix * mvPosition;\\n\\ngl_Position = projectionMatrix * mvPosition;\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/dithering_fragment.glsl.js\\nvar dithering_fragment_glsl_default = `\\n#ifdef DITHERING\\n\\n\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/dithering_pars_fragment.glsl.js\\nvar dithering_pars_fragment_glsl_default = `\\n#ifdef DITHERING\\n\\n\\t// based on https://www.shadertoy.com/view/MslGR8\\n\\tvec3 dithering( vec3 color ) {\\n\\t\\t//Calculate grid position\\n\\t\\tfloat grid_position = rand( gl_FragCoord.xy );\\n\\n\\t\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\n\\t\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\n\\n\\t\\t//modify shift acording to grid position.\\n\\t\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\n\\n\\t\\t//shift the color by dither_shift\\n\\t\\treturn color + dither_shift_RGB;\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/roughnessmap_fragment.glsl.js\\nvar roughnessmap_fragment_glsl_default = `\\nfloat roughnessFactor = roughness;\\n\\n#ifdef USE_ROUGHNESSMAP\\n\\n\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\n\\n\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\n\\troughnessFactor *= texelRoughness.g;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/roughnessmap_pars_fragment.glsl.js\\nvar roughnessmap_pars_fragment_glsl_default = `\\n#ifdef USE_ROUGHNESSMAP\\n\\n\\tuniform sampler2D roughnessMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_pars_fragment.glsl.js\\nvar shadowmap_pars_fragment_glsl_default = `\\n#ifdef USE_SHADOWMAP\\n\\n\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct DirectionalLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t};\\n\\n\\t\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct SpotLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t};\\n\\n\\t\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct PointLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t\\tfloat shadowCameraNear;\\n\\t\\t\\tfloat shadowCameraFar;\\n\\t\\t};\\n\\n\\t\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t/*\\n\\t#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\t\\t// TODO (abelnation): create uniforms for area light shadows\\n\\n\\t#endif\\n\\t*/\\n\\n\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\n\\n\\t\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\n\\n\\t}\\n\\n\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\n\\n\\t\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\n\\n\\t}\\n\\n\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\n\\n\\t\\tfloat occlusion = 1.0;\\n\\n\\t\\tvec2 distribution = texture2DDistribution( shadow, uv );\\n\\n\\t\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\n\\n\\t\\tif (hard_shadow != 1.0 ) {\\n\\n\\t\\t\\tfloat distance = compare - distribution.x ;\\n\\t\\t\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\n\\t\\t\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\n\\t\\t\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\n\\t\\t\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\n\\n\\t\\t}\\n\\t\\treturn occlusion;\\n\\n\\t}\\n\\n\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\n\\n\\t\\tfloat shadow = 1.0;\\n\\n\\t\\tshadowCoord.xyz /= shadowCoord.w;\\n\\t\\tshadowCoord.z += shadowBias;\\n\\n\\t\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\n\\t\\t// if ( all( something, something ) ) using this instead\\n\\n\\t\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\n\\t\\tbool inFrustum = all( inFrustumVec );\\n\\n\\t\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\n\\n\\t\\tbool frustumTest = all( frustumTestVec );\\n\\n\\t\\tif ( frustumTest ) {\\n\\n\\t\\t#if defined( SHADOWMAP_TYPE_PCF )\\n\\n\\t\\t\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\n\\n\\t\\t\\tfloat dx0 = - texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy0 = - texelSize.y * shadowRadius;\\n\\t\\t\\tfloat dx1 = + texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy1 = + texelSize.y * shadowRadius;\\n\\t\\t\\tfloat dx2 = dx0 / 2.0;\\n\\t\\t\\tfloat dy2 = dy0 / 2.0;\\n\\t\\t\\tfloat dx3 = dx1 / 2.0;\\n\\t\\t\\tfloat dy3 = dy1 / 2.0;\\n\\n\\t\\t\\tshadow = (\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\n\\t\\t\\t) * ( 1.0 / 17.0 );\\n\\n\\t\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\n\\n\\t\\t\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\n\\t\\t\\tfloat dx = texelSize.x;\\n\\t\\t\\tfloat dy = texelSize.y;\\n\\n\\t\\t\\tvec2 uv = shadowCoord.xy;\\n\\t\\t\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\n\\t\\t\\tuv -= f * texelSize;\\n\\n\\t\\t\\tshadow = (\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\n\\t\\t\\t\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t f.x ) +\\n\\t\\t\\t\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t f.x ) +\\n\\t\\t\\t\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t f.y ) +\\n\\t\\t\\t\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t f.y ) +\\n\\t\\t\\t\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t\\t f.x ),\\n\\t\\t\\t\\t\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\n\\t\\t\\t\\t\\t\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\n\\t\\t\\t\\t\\t\\t f.x ),\\n\\t\\t\\t\\t\\t f.y )\\n\\t\\t\\t) * ( 1.0 / 9.0 );\\n\\n\\t\\t#elif defined( SHADOWMAP_TYPE_VSM )\\n\\n\\t\\t\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\n\\n\\t\\t#else // no percentage-closer filtering:\\n\\n\\t\\t\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\n\\n\\t\\t#endif\\n\\n\\t\\t}\\n\\n\\t\\treturn shadow;\\n\\n\\t}\\n\\n\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\n\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\n\\t// 2D texture:\\n\\t//\\n\\t// xzXZ\\n\\t// y Y\\n\\t//\\n\\t// Y - Positive y direction\\n\\t// y - Negative y direction\\n\\t// X - Positive x direction\\n\\t// x - Negative x direction\\n\\t// Z - Positive z direction\\n\\t// z - Negative z direction\\n\\t//\\n\\t// Source and test bed:\\n\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\n\\n\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\n\\n\\t\\t// Number of texels to avoid at the edge of each square\\n\\n\\t\\tvec3 absV = abs( v );\\n\\n\\t\\t// Intersect unit cube\\n\\n\\t\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\n\\t\\tabsV *= scaleToCube;\\n\\n\\t\\t// Apply scale to avoid seams\\n\\n\\t\\t// two texels less per square (one texel will do for NEAREST)\\n\\t\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\n\\n\\t\\t// Unwrap\\n\\n\\t\\t// space: -1 ... 1 range for each square\\n\\t\\t//\\n\\t\\t// #X##\\t\\tdim := ( 4 , 2 )\\n\\t\\t// # #\\t\\tcenter := ( 1 , 1 )\\n\\n\\t\\tvec2 planar = v.xy;\\n\\n\\t\\tfloat almostATexel = 1.5 * texelSizeY;\\n\\t\\tfloat almostOne = 1.0 - almostATexel;\\n\\n\\t\\tif ( absV.z >= almostOne ) {\\n\\n\\t\\t\\tif ( v.z > 0.0 )\\n\\t\\t\\t\\tplanar.x = 4.0 - v.x;\\n\\n\\t\\t} else if ( absV.x >= almostOne ) {\\n\\n\\t\\t\\tfloat signX = sign( v.x );\\n\\t\\t\\tplanar.x = v.z * signX + 2.0 * signX;\\n\\n\\t\\t} else if ( absV.y >= almostOne ) {\\n\\n\\t\\t\\tfloat signY = sign( v.y );\\n\\t\\t\\tplanar.x = v.x + 2.0 * signY + 2.0;\\n\\t\\t\\tplanar.y = v.z * signY - 2.0;\\n\\n\\t\\t}\\n\\n\\t\\t// Transform to UV space\\n\\n\\t\\t// scale := 0.5 / dim\\n\\t\\t// translate := ( center + 0.5 ) / dim\\n\\t\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\n\\n\\t}\\n\\n\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\n\\n\\t\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\n\\n\\t\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\n\\t\\t// the vector from the light to the world-space position of the fragment.\\n\\t\\tvec3 lightToPosition = shadowCoord.xyz;\\n\\n\\t\\t// dp = normalized distance from light to fragment position\\n\\t\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\n\\t\\tdp += shadowBias;\\n\\n\\t\\t// bd3D = base direction 3D\\n\\t\\tvec3 bd3D = normalize( lightToPosition );\\n\\n\\t\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\n\\n\\t\\t\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\n\\n\\t\\t\\treturn (\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\n\\t\\t\\t) * ( 1.0 / 9.0 );\\n\\n\\t\\t#else // no percentage-closer filtering\\n\\n\\t\\t\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_pars_vertex.glsl.js\\nvar shadowmap_pars_vertex_glsl_default = `\\n#ifdef USE_SHADOWMAP\\n\\n\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct DirectionalLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t};\\n\\n\\t\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct SpotLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t};\\n\\n\\t\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\n\\n\\t\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\n\\t\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\n\\n\\t\\tstruct PointLightShadow {\\n\\t\\t\\tfloat shadowBias;\\n\\t\\t\\tfloat shadowNormalBias;\\n\\t\\t\\tfloat shadowRadius;\\n\\t\\t\\tvec2 shadowMapSize;\\n\\t\\t\\tfloat shadowCameraNear;\\n\\t\\t\\tfloat shadowCameraFar;\\n\\t\\t};\\n\\n\\t\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\n\\n\\t#endif\\n\\n\\t/*\\n\\t#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\t\\t// TODO (abelnation): uniforms for area light shadows\\n\\n\\t#endif\\n\\t*/\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_vertex.glsl.js\\nvar shadowmap_vertex_glsl_default = `\\n#ifdef USE_SHADOWMAP\\n\\n\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\n\\n\\t\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\n\\t\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\n\\t\\tvec4 shadowWorldPosition;\\n\\n\\t#endif\\n\\n\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\n\\t\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\n\\t\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\n\\t\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t/*\\n\\t#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\t\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\n\\n\\t#endif\\n\\t*/\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/shadowmask_pars_fragment.glsl.js\\nvar shadowmask_pars_fragment_glsl_default = `\\nfloat getShadowMask() {\\n\\n\\tfloat shadow = 1.0;\\n\\n\\t#ifdef USE_SHADOWMAP\\n\\n\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\n\\n\\tDirectionalLightShadow directionalLight;\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tdirectionalLight = directionalLightShadows[ i ];\\n\\t\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\n\\n\\tSpotLightShadow spotLight;\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tspotLight = spotLightShadows[ i ];\\n\\t\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\n\\n\\tPointLightShadow pointLight;\\n\\n\\t#pragma unroll_loop_start\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\n\\n\\t\\tpointLight = pointLightShadows[ i ];\\n\\t\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\n\\n\\t}\\n\\t#pragma unroll_loop_end\\n\\n\\t#endif\\n\\n\\t/*\\n\\t#if NUM_RECT_AREA_LIGHTS > 0\\n\\n\\t\\t// TODO (abelnation): update shadow for Area light\\n\\n\\t#endif\\n\\t*/\\n\\n\\t#endif\\n\\n\\treturn shadow;\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/skinbase_vertex.glsl.js\\nvar skinbase_vertex_glsl_default = `\\n#ifdef USE_SKINNING\\n\\n\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\n\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\n\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\n\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/skinning_pars_vertex.glsl.js\\nvar skinning_pars_vertex_glsl_default = `\\n#ifdef USE_SKINNING\\n\\n\\tuniform mat4 bindMatrix;\\n\\tuniform mat4 bindMatrixInverse;\\n\\n\\t#ifdef BONE_TEXTURE\\n\\n\\t\\tuniform highp sampler2D boneTexture;\\n\\t\\tuniform int boneTextureSize;\\n\\n\\t\\tmat4 getBoneMatrix( const in float i ) {\\n\\n\\t\\t\\tfloat j = i * 4.0;\\n\\t\\t\\tfloat x = mod( j, float( boneTextureSize ) );\\n\\t\\t\\tfloat y = floor( j / float( boneTextureSize ) );\\n\\n\\t\\t\\tfloat dx = 1.0 / float( boneTextureSize );\\n\\t\\t\\tfloat dy = 1.0 / float( boneTextureSize );\\n\\n\\t\\t\\ty = dy * ( y + 0.5 );\\n\\n\\t\\t\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\n\\t\\t\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\n\\t\\t\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\n\\t\\t\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\n\\n\\t\\t\\tmat4 bone = mat4( v1, v2, v3, v4 );\\n\\n\\t\\t\\treturn bone;\\n\\n\\t\\t}\\n\\n\\t#else\\n\\n\\t\\tuniform mat4 boneMatrices[ MAX_BONES ];\\n\\n\\t\\tmat4 getBoneMatrix( const in float i ) {\\n\\n\\t\\t\\tmat4 bone = boneMatrices[ int(i) ];\\n\\t\\t\\treturn bone;\\n\\n\\t\\t}\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/skinning_vertex.glsl.js\\nvar skinning_vertex_glsl_default = `\\n#ifdef USE_SKINNING\\n\\n\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\n\\n\\tvec4 skinned = vec4( 0.0 );\\n\\tskinned += boneMatX * skinVertex * skinWeight.x;\\n\\tskinned += boneMatY * skinVertex * skinWeight.y;\\n\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\n\\tskinned += boneMatW * skinVertex * skinWeight.w;\\n\\n\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/skinnormal_vertex.glsl.js\\nvar skinnormal_vertex_glsl_default = `\\n#ifdef USE_SKINNING\\n\\n\\tmat4 skinMatrix = mat4( 0.0 );\\n\\tskinMatrix += skinWeight.x * boneMatX;\\n\\tskinMatrix += skinWeight.y * boneMatY;\\n\\tskinMatrix += skinWeight.z * boneMatZ;\\n\\tskinMatrix += skinWeight.w * boneMatW;\\n\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\n\\n\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\n\\n\\t#ifdef USE_TANGENT\\n\\n\\t\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\n\\n\\t#endif\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/specularmap_fragment.glsl.js\\nvar specularmap_fragment_glsl_default = `\\nfloat specularStrength;\\n\\n#ifdef USE_SPECULARMAP\\n\\n\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\n\\tspecularStrength = texelSpecular.r;\\n\\n#else\\n\\n\\tspecularStrength = 1.0;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/specularmap_pars_fragment.glsl.js\\nvar specularmap_pars_fragment_glsl_default = `\\n#ifdef USE_SPECULARMAP\\n\\n\\tuniform sampler2D specularMap;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/tonemapping_fragment.glsl.js\\nvar tonemapping_fragment_glsl_default = `\\n#if defined( TONE_MAPPING )\\n\\n\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/tonemapping_pars_fragment.glsl.js\\nvar tonemapping_pars_fragment_glsl_default = `\\n#ifndef saturate\\n// may have defined saturate() already\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\n#endif\\n\\nuniform float toneMappingExposure;\\n\\n// exposure only\\nvec3 LinearToneMapping( vec3 color ) {\\n\\n\\treturn toneMappingExposure * color;\\n\\n}\\n\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\nvec3 ReinhardToneMapping( vec3 color ) {\\n\\n\\tcolor *= toneMappingExposure;\\n\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\n\\n}\\n\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\n\\n\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\n\\tcolor *= toneMappingExposure;\\n\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\n\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\n\\n}\\n\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\nvec3 RRTAndODTFit( vec3 v ) {\\n\\n\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\n\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\n\\treturn a / b;\\n\\n}\\n\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\n\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\n\\n\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\n\\tconst mat3 ACESInputMat = mat3(\\n\\t\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\n\\t\\tvec3( 0.35458, 0.90834, 0.13383 ),\\n\\t\\tvec3( 0.04823, 0.01566, 0.83777 )\\n\\t);\\n\\n\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\n\\tconst mat3 ACESOutputMat = mat3(\\n\\t\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\n\\t\\tvec3( -0.53108, 1.10813, -0.07276 ),\\n\\t\\tvec3( -0.07367, -0.00605, 1.07602 )\\n\\t);\\n\\n\\tcolor *= toneMappingExposure / 0.6;\\n\\n\\tcolor = ACESInputMat * color;\\n\\n\\t// Apply RRT and ODT\\n\\tcolor = RRTAndODTFit( color );\\n\\n\\tcolor = ACESOutputMat * color;\\n\\n\\t// Clamp to [0, 1]\\n\\treturn saturate( color );\\n\\n}\\n\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/transmission_fragment.glsl.js\\nvar transmission_fragment_glsl_default = `\\n#ifdef USE_TRANSMISSION\\n\\n\\tfloat transmissionAlpha = 1.0;\\n\\tfloat transmissionFactor = transmission;\\n\\tfloat thicknessFactor = thickness;\\n\\n\\t#ifdef USE_TRANSMISSIONMAP\\n\\n\\t\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\n\\n\\t#endif\\n\\n\\t#ifdef USE_THICKNESSMAP\\n\\n\\t\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\n\\n\\t#endif\\n\\n\\tvec3 pos = vWorldPosition;\\n\\tvec3 v = normalize( cameraPosition - pos );\\n\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\n\\n\\tvec4 transmission = getIBLVolumeRefraction(\\n\\t\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\n\\t\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\n\\t\\tattenuationTint, attenuationDistance );\\n\\n\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\n\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/transmission_pars_fragment.glsl.js\\nvar transmission_pars_fragment_glsl_default = `\\n#ifdef USE_TRANSMISSION\\n\\n\\t// Transmission code is based on glTF-Sampler-Viewer\\n\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\n\\n\\tuniform float transmission;\\n\\tuniform float thickness;\\n\\tuniform float attenuationDistance;\\n\\tuniform vec3 attenuationTint;\\n\\n\\t#ifdef USE_TRANSMISSIONMAP\\n\\n\\t\\tuniform sampler2D transmissionMap;\\n\\n\\t#endif\\n\\n\\t#ifdef USE_THICKNESSMAP\\n\\n\\t\\tuniform sampler2D thicknessMap;\\n\\n\\t#endif\\n\\n\\tuniform vec2 transmissionSamplerSize;\\n\\tuniform sampler2D transmissionSamplerMap;\\n\\n\\tuniform mat4 modelMatrix;\\n\\tuniform mat4 projectionMatrix;\\n\\n\\tvarying vec3 vWorldPosition;\\n\\n\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\n\\n\\t\\t// Direction of refracted light.\\n\\t\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\n\\n\\t\\t// Compute rotation-independant scaling of the model matrix.\\n\\t\\tvec3 modelScale;\\n\\t\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\n\\t\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\n\\t\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\n\\n\\t\\t// The thickness is specified in local space.\\n\\t\\treturn normalize( refractionVector ) * thickness * modelScale;\\n\\n\\t}\\n\\n\\tfloat applyIorToRoughness( float roughness, float ior ) {\\n\\n\\t\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\n\\t\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\n\\t\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\n\\n\\t}\\n\\n\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\n\\n\\t\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\n\\n\\t\\t#ifdef TEXTURE_LOD_EXT\\n\\n\\t\\t\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\n\\n\\t\\t#else\\n\\n\\t\\t\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\n\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\n\\n\\t\\tif ( attenuationDistance == 0.0 ) {\\n\\n\\t\\t\\t// Attenuation distance is +\\\\u221E (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\n\\t\\t\\treturn radiance;\\n\\n\\t\\t} else {\\n\\n\\t\\t\\t// Compute light attenuation using Beer's law.\\n\\t\\t\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\n\\t\\t\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\n\\t\\t\\treturn transmittance * radiance;\\n\\n\\t\\t}\\n\\n\\t}\\n\\n\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\n\\t\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\n\\t\\tvec3 attenuationColor, float attenuationDistance ) {\\n\\n\\t\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\n\\t\\tvec3 refractedRayExit = position + transmissionRay;\\n\\n\\t\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\n\\t\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\n\\t\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\n\\t\\trefractionCoords += 1.0;\\n\\t\\trefractionCoords /= 2.0;\\n\\n\\t\\t// Sample framebuffer to get pixel the refracted ray hits.\\n\\t\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\n\\n\\t\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\n\\n\\t\\t// Get the specular component.\\n\\t\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\n\\n\\t\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\n\\n\\t}\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv_pars_fragment.glsl.js\\nvar uv_pars_fragment_glsl_default = `\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\n\\n\\tvarying vec2 vUv;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv_pars_vertex.glsl.js\\nvar uv_pars_vertex_glsl_default = `\\n#ifdef USE_UV\\n\\n\\t#ifdef UVS_VERTEX_ONLY\\n\\n\\t\\tvec2 vUv;\\n\\n\\t#else\\n\\n\\t\\tvarying vec2 vUv;\\n\\n\\t#endif\\n\\n\\tuniform mat3 uvTransform;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv_vertex.glsl.js\\nvar uv_vertex_glsl_default = `\\n#ifdef USE_UV\\n\\n\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv2_pars_fragment.glsl.js\\nvar uv2_pars_fragment_glsl_default = `\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\n\\tvarying vec2 vUv2;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv2_pars_vertex.glsl.js\\nvar uv2_pars_vertex_glsl_default = `\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\n\\tattribute vec2 uv2;\\n\\tvarying vec2 vUv2;\\n\\n\\tuniform mat3 uv2Transform;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/uv2_vertex.glsl.js\\nvar uv2_vertex_glsl_default = `\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\n\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk/worldpos_vertex.glsl.js\\nvar worldpos_vertex_glsl_default = `\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\n\\n\\tvec4 worldPosition = vec4( transformed, 1.0 );\\n\\n\\t#ifdef USE_INSTANCING\\n\\n\\t\\tworldPosition = instanceMatrix * worldPosition;\\n\\n\\t#endif\\n\\n\\tworldPosition = modelMatrix * worldPosition;\\n\\n#endif\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/background.glsl.js\\nvar vertex = `\\nvarying vec2 vUv;\\nuniform mat3 uvTransform;\\n\\nvoid main() {\\n\\n\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\n\\n\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\n\\n}\\n`;\\nvar fragment = `\\nuniform sampler2D t2D;\\n\\nvarying vec2 vUv;\\n\\nvoid main() {\\n\\n\\tvec4 texColor = texture2D( t2D, vUv );\\n\\n\\tgl_FragColor = mapTexelToLinear( texColor );\\n\\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/cube.glsl.js\\nvar vertex2 = `\\nvarying vec3 vWorldDirection;\\n\\n#include \\n\\nvoid main() {\\n\\n\\tvWorldDirection = transformDirection( position, modelMatrix );\\n\\n\\t#include \\n\\t#include \\n\\n\\tgl_Position.z = gl_Position.w; // set z to camera.far\\n\\n}\\n`;\\nvar fragment2 = `\\n#include \\nuniform float opacity;\\n\\nvarying vec3 vWorldDirection;\\n\\n#include \\n\\nvoid main() {\\n\\n\\tvec3 vReflect = vWorldDirection;\\n\\t#include \\n\\n\\tgl_FragColor = envColor;\\n\\tgl_FragColor.a *= opacity;\\n\\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/depth.glsl.js\\nvar vertex3 = `\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\nvarying vec2 vHighPrecisionZW;\\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\t#include \\n\\n\\t#ifdef USE_DISPLACEMENTMAP\\n\\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvHighPrecisionZW = gl_Position.zw;\\n\\n}\\n`;\\nvar fragment3 = `\\n#if DEPTH_PACKING == 3200\\n\\n\\tuniform float opacity;\\n\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvarying vec2 vHighPrecisionZW;\\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( 1.0 );\\n\\n\\t#if DEPTH_PACKING == 3200\\n\\n\\t\\tdiffuseColor.a = opacity;\\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\n\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\n\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\n\\n\\t#if DEPTH_PACKING == 3200\\n\\n\\t\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\n\\n\\t#elif DEPTH_PACKING == 3201\\n\\n\\t\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\n\\n\\t#endif\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA.glsl.js\\nvar vertex4 = `\\n#define DISTANCE\\n\\nvarying vec3 vWorldPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\t#include \\n\\n\\t#ifdef USE_DISPLACEMENTMAP\\n\\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvWorldPosition = worldPosition.xyz;\\n\\n}\\n`;\\nvar fragment4 = `\\n#define DISTANCE\\n\\nuniform vec3 referencePosition;\\nuniform float nearDistance;\\nuniform float farDistance;\\nvarying vec3 vWorldPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main () {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( 1.0 );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tfloat dist = length( vWorldPosition - referencePosition );\\n\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\n\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\n\\n\\tgl_FragColor = packDepthToRGBA( dist );\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/equirect.glsl.js\\nvar vertex5 = `\\nvarying vec3 vWorldDirection;\\n\\n#include \\n\\nvoid main() {\\n\\n\\tvWorldDirection = transformDirection( position, modelMatrix );\\n\\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment5 = `\\nuniform sampler2D tEquirect;\\n\\nvarying vec3 vWorldDirection;\\n\\n#include \\n\\nvoid main() {\\n\\n\\tvec3 direction = normalize( vWorldDirection );\\n\\n\\tvec2 sampleUV = equirectUv( direction );\\n\\n\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\n\\n\\tgl_FragColor = mapTexelToLinear( texColor );\\n\\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/linedashed.glsl.js\\nvar vertex6 = `\\nuniform float scale;\\nattribute float lineDistance;\\n\\nvarying float vLineDistance;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\tvLineDistance = scale * lineDistance;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment6 = `\\nuniform vec3 diffuse;\\nuniform float opacity;\\n\\nuniform float dashSize;\\nuniform float totalSize;\\n\\nvarying float vLineDistance;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\n\\n\\t\\tdiscard;\\n\\n\\t}\\n\\n\\tvec3 outgoingLight = vec3( 0.0 );\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\n\\t#include \\n\\t#include \\n\\n\\toutgoingLight = diffuseColor.rgb; // simple shader\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshbasic.glsl.js\\nvar vertex7 = `\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\n\\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment7 = `\\nuniform vec3 diffuse;\\nuniform float opacity;\\n\\n#ifndef FLAT_SHADED\\n\\n\\tvarying vec3 vNormal;\\n\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\n\\t// accumulation (baked indirect lighting only)\\n\\t#ifdef USE_LIGHTMAP\\n\\n\\t\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\n\\t\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\n\\n\\t#else\\n\\n\\t\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\n\\n\\t#endif\\n\\n\\t// modulation\\n\\t#include \\n\\n\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\n\\n\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\n\\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshlambert.glsl.js\\nvar vertex8 = `\\n#define LAMBERT\\n\\nvarying vec3 vLightFront;\\nvarying vec3 vIndirectFront;\\n\\n#ifdef DOUBLE_SIDED\\n\\tvarying vec3 vLightBack;\\n\\tvarying vec3 vIndirectBack;\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\\n`;\\nvar fragment8 = `\\nuniform vec3 diffuse;\\nuniform vec3 emissive;\\nuniform float opacity;\\n\\nvarying vec3 vLightFront;\\nvarying vec3 vIndirectFront;\\n\\n#ifdef DOUBLE_SIDED\\n\\tvarying vec3 vLightBack;\\n\\tvarying vec3 vIndirectBack;\\n#endif\\n\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\tvec3 totalEmissiveRadiance = emissive;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// accumulation\\n\\n\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\n\\n\\t#else\\n\\n\\t\\treflectedLight.indirectDiffuse += vIndirectFront;\\n\\n\\t#endif\\n\\n\\t#include \\n\\n\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\n\\n\\t#ifdef DOUBLE_SIDED\\n\\n\\t\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\n\\n\\t#else\\n\\n\\t\\treflectedLight.directDiffuse = vLightFront;\\n\\n\\t#endif\\n\\n\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\n\\n\\t// modulation\\n\\n\\t#include \\n\\n\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\n\\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshmatcap.glsl.js\\nvar vertex9 = `\\n#define MATCAP\\n\\nvarying vec3 vViewPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvViewPosition = - mvPosition.xyz;\\n\\n}\\n`;\\nvar fragment9 = `\\n#define MATCAP\\n\\nuniform vec3 diffuse;\\nuniform float opacity;\\nuniform sampler2D matcap;\\n\\nvarying vec3 vViewPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvec3 viewDir = normalize( vViewPosition );\\n\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\n\\tvec3 y = cross( viewDir, x );\\n\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\n\\n\\t#ifdef USE_MATCAP\\n\\n\\t\\tvec4 matcapColor = texture2D( matcap, uv );\\n\\t\\tmatcapColor = matcapTexelToLinear( matcapColor );\\n\\n\\t#else\\n\\n\\t\\tvec4 matcapColor = vec4( 1.0 );\\n\\n\\t#endif\\n\\n\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshnormal.glsl.js\\nvar vertex10 = `\\n#define NORMAL\\n\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\n\\n\\tvarying vec3 vViewPosition;\\n\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\n\\n\\tvViewPosition = - mvPosition.xyz;\\n\\n#endif\\n\\n}\\n`;\\nvar fragment10 = `\\n#define NORMAL\\n\\nuniform float opacity;\\n\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\n\\n\\tvarying vec3 vViewPosition;\\n\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshphong.glsl.js\\nvar vertex11 = `\\n#define PHONG\\n\\nvarying vec3 vViewPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvViewPosition = - mvPosition.xyz;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment11 = `\\n#define PHONG\\n\\nuniform vec3 diffuse;\\nuniform vec3 emissive;\\nuniform vec3 specular;\\nuniform float shininess;\\nuniform float opacity;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\tvec3 totalEmissiveRadiance = emissive;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// accumulation\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// modulation\\n\\t#include \\n\\n\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshphysical.glsl.js\\nvar vertex12 = `\\n#define STANDARD\\n\\nvarying vec3 vViewPosition;\\n\\n#ifdef USE_TRANSMISSION\\n\\n\\tvarying vec3 vWorldPosition;\\n\\n#endif\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvViewPosition = - mvPosition.xyz;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n#ifdef USE_TRANSMISSION\\n\\n\\tvWorldPosition = worldPosition.xyz;\\n\\n#endif\\n}\\n`;\\nvar fragment12 = `\\n#define STANDARD\\n\\n#ifdef PHYSICAL\\n\\t#define IOR\\n\\t#define SPECULAR\\n#endif\\n\\nuniform vec3 diffuse;\\nuniform vec3 emissive;\\nuniform float roughness;\\nuniform float metalness;\\nuniform float opacity;\\n\\n#ifdef IOR\\n\\tuniform float ior;\\n#endif\\n\\n#ifdef SPECULAR\\n\\tuniform float specularIntensity;\\n\\tuniform vec3 specularTint;\\n\\n\\t#ifdef USE_SPECULARINTENSITYMAP\\n\\t\\tuniform sampler2D specularIntensityMap;\\n\\t#endif\\n\\n\\t#ifdef USE_SPECULARTINTMAP\\n\\t\\tuniform sampler2D specularTintMap;\\n\\t#endif\\n#endif\\n\\n#ifdef USE_CLEARCOAT\\n\\tuniform float clearcoat;\\n\\tuniform float clearcoatRoughness;\\n#endif\\n\\n#ifdef USE_SHEEN\\n\\tuniform vec3 sheenTint;\\n\\tuniform float sheenRoughness;\\n#endif\\n\\nvarying vec3 vViewPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\tvec3 totalEmissiveRadiance = emissive;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// accumulation\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// modulation\\n\\t#include \\n\\n\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\n\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\n\\n\\t#include \\n\\n\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\n\\n\\t#ifdef USE_CLEARCOAT\\n\\n\\t\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\n\\n\\t\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\n\\n\\t\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/meshtoon.glsl.js\\nvar vertex13 = `\\n#define TOON\\n\\nvarying vec3 vViewPosition;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tvViewPosition = - mvPosition.xyz;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment13 = `\\n#define TOON\\n\\nuniform vec3 diffuse;\\nuniform vec3 emissive;\\nuniform float opacity;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\tvec3 totalEmissiveRadiance = emissive;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// accumulation\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t// modulation\\n\\t#include \\n\\n\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/points.glsl.js\\nvar vertex14 = `\\nuniform float size;\\nuniform float scale;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\tgl_PointSize = size;\\n\\n\\t#ifdef USE_SIZEATTENUATION\\n\\n\\t\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\n\\n\\t\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\n\\n\\t#endif\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment14 = `\\nuniform vec3 diffuse;\\nuniform float opacity;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec3 outgoingLight = vec3( 0.0 );\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\toutgoingLight = diffuseColor.rgb;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/shadow.glsl.js\\nvar vertex15 = `\\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment15 = `\\nuniform vec3 color;\\nuniform float opacity;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/sprite.glsl.js\\nvar vertex16 = `\\nuniform float rotation;\\nuniform vec2 center;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\n\\n\\tvec2 scale;\\n\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\n\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\n\\n\\t#ifndef USE_SIZEATTENUATION\\n\\n\\t\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\n\\n\\t\\tif ( isPerspective ) scale *= - mvPosition.z;\\n\\n\\t#endif\\n\\n\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\n\\n\\tvec2 rotatedPosition;\\n\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\n\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\n\\n\\tmvPosition.xy += rotatedPosition;\\n\\n\\tgl_Position = projectionMatrix * mvPosition;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\nvar fragment16 = `\\nuniform vec3 diffuse;\\nuniform float opacity;\\n\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n\\nvoid main() {\\n\\n\\t#include \\n\\n\\tvec3 outgoingLight = vec3( 0.0 );\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n\\toutgoingLight = diffuseColor.rgb;\\n\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderChunk.js\\nvar ShaderChunk = {\\n alphamap_fragment: alphamap_fragment_glsl_default,\\n alphamap_pars_fragment: alphamap_pars_fragment_glsl_default,\\n alphatest_fragment: alphatest_fragment_glsl_default,\\n alphatest_pars_fragment: alphatest_pars_fragment_glsl_default,\\n aomap_fragment: aomap_fragment_glsl_default,\\n aomap_pars_fragment: aomap_pars_fragment_glsl_default,\\n begin_vertex: begin_vertex_glsl_default,\\n beginnormal_vertex: beginnormal_vertex_glsl_default,\\n bsdfs: bsdfs_glsl_default,\\n bumpmap_pars_fragment: bumpmap_pars_fragment_glsl_default,\\n clipping_planes_fragment: clipping_planes_fragment_glsl_default,\\n clipping_planes_pars_fragment: clipping_planes_pars_fragment_glsl_default,\\n clipping_planes_pars_vertex: clipping_planes_pars_vertex_glsl_default,\\n clipping_planes_vertex: clipping_planes_vertex_glsl_default,\\n color_fragment: color_fragment_glsl_default,\\n color_pars_fragment: color_pars_fragment_glsl_default,\\n color_pars_vertex: color_pars_vertex_glsl_default,\\n color_vertex: color_vertex_glsl_default,\\n common: common_glsl_default,\\n cube_uv_reflection_fragment: cube_uv_reflection_fragment_glsl_default,\\n defaultnormal_vertex: defaultnormal_vertex_glsl_default,\\n displacementmap_pars_vertex: displacementmap_pars_vertex_glsl_default,\\n displacementmap_vertex: displacementmap_vertex_glsl_default,\\n emissivemap_fragment: emissivemap_fragment_glsl_default,\\n emissivemap_pars_fragment: emissivemap_pars_fragment_glsl_default,\\n encodings_fragment: encodings_fragment_glsl_default,\\n encodings_pars_fragment: encodings_pars_fragment_glsl_default,\\n envmap_fragment: envmap_fragment_glsl_default,\\n envmap_common_pars_fragment: envmap_common_pars_fragment_glsl_default,\\n envmap_pars_fragment: envmap_pars_fragment_glsl_default,\\n envmap_pars_vertex: envmap_pars_vertex_glsl_default,\\n envmap_physical_pars_fragment: envmap_physical_pars_fragment_glsl_default,\\n envmap_vertex: envmap_vertex_glsl_default,\\n fog_vertex: fog_vertex_glsl_default,\\n fog_pars_vertex: fog_pars_vertex_glsl_default,\\n fog_fragment: fog_fragment_glsl_default,\\n fog_pars_fragment: fog_pars_fragment_glsl_default,\\n gradientmap_pars_fragment: gradientmap_pars_fragment_glsl_default,\\n lightmap_fragment: lightmap_fragment_glsl_default,\\n lightmap_pars_fragment: lightmap_pars_fragment_glsl_default,\\n lights_lambert_vertex: lights_lambert_vertex_glsl_default,\\n lights_pars_begin: lights_pars_begin_glsl_default,\\n lights_toon_fragment: lights_toon_fragment_glsl_default,\\n lights_toon_pars_fragment: lights_toon_pars_fragment_glsl_default,\\n lights_phong_fragment: lights_phong_fragment_glsl_default,\\n lights_phong_pars_fragment: lights_phong_pars_fragment_glsl_default,\\n lights_physical_fragment: lights_physical_fragment_glsl_default,\\n lights_physical_pars_fragment: lights_physical_pars_fragment_glsl_default,\\n lights_fragment_begin: lights_fragment_begin_glsl_default,\\n lights_fragment_maps: lights_fragment_maps_glsl_default,\\n lights_fragment_end: lights_fragment_end_glsl_default,\\n logdepthbuf_fragment: logdepthbuf_fragment_glsl_default,\\n logdepthbuf_pars_fragment: logdepthbuf_pars_fragment_glsl_default,\\n logdepthbuf_pars_vertex: logdepthbuf_pars_vertex_glsl_default,\\n logdepthbuf_vertex: logdepthbuf_vertex_glsl_default,\\n map_fragment: map_fragment_glsl_default,\\n map_pars_fragment: map_pars_fragment_glsl_default,\\n map_particle_fragment: map_particle_fragment_glsl_default,\\n map_particle_pars_fragment: map_particle_pars_fragment_glsl_default,\\n metalnessmap_fragment: metalnessmap_fragment_glsl_default,\\n metalnessmap_pars_fragment: metalnessmap_pars_fragment_glsl_default,\\n morphnormal_vertex: morphnormal_vertex_glsl_default,\\n morphtarget_pars_vertex: morphtarget_pars_vertex_glsl_default,\\n morphtarget_vertex: morphtarget_vertex_glsl_default,\\n normal_fragment_begin: normal_fragment_begin_glsl_default,\\n normal_fragment_maps: normal_fragment_maps_glsl_default,\\n normal_pars_fragment: normal_pars_fragment_glsl_default,\\n normal_pars_vertex: normal_pars_vertex_glsl_default,\\n normal_vertex: normal_vertex_glsl_default,\\n normalmap_pars_fragment: normalmap_pars_fragment_glsl_default,\\n clearcoat_normal_fragment_begin: clearcoat_normal_fragment_begin_glsl_default,\\n clearcoat_normal_fragment_maps: clearcoat_normal_fragment_maps_glsl_default,\\n clearcoat_pars_fragment: clearcoat_pars_fragment_glsl_default,\\n output_fragment: output_fragment_glsl_default,\\n packing: packing_glsl_default,\\n premultiplied_alpha_fragment: premultiplied_alpha_fragment_glsl_default,\\n project_vertex: project_vertex_glsl_default,\\n dithering_fragment: dithering_fragment_glsl_default,\\n dithering_pars_fragment: dithering_pars_fragment_glsl_default,\\n roughnessmap_fragment: roughnessmap_fragment_glsl_default,\\n roughnessmap_pars_fragment: roughnessmap_pars_fragment_glsl_default,\\n shadowmap_pars_fragment: shadowmap_pars_fragment_glsl_default,\\n shadowmap_pars_vertex: shadowmap_pars_vertex_glsl_default,\\n shadowmap_vertex: shadowmap_vertex_glsl_default,\\n shadowmask_pars_fragment: shadowmask_pars_fragment_glsl_default,\\n skinbase_vertex: skinbase_vertex_glsl_default,\\n skinning_pars_vertex: skinning_pars_vertex_glsl_default,\\n skinning_vertex: skinning_vertex_glsl_default,\\n skinnormal_vertex: skinnormal_vertex_glsl_default,\\n specularmap_fragment: specularmap_fragment_glsl_default,\\n specularmap_pars_fragment: specularmap_pars_fragment_glsl_default,\\n tonemapping_fragment: tonemapping_fragment_glsl_default,\\n tonemapping_pars_fragment: tonemapping_pars_fragment_glsl_default,\\n transmission_fragment: transmission_fragment_glsl_default,\\n transmission_pars_fragment: transmission_pars_fragment_glsl_default,\\n uv_pars_fragment: uv_pars_fragment_glsl_default,\\n uv_pars_vertex: uv_pars_vertex_glsl_default,\\n uv_vertex: uv_vertex_glsl_default,\\n uv2_pars_fragment: uv2_pars_fragment_glsl_default,\\n uv2_pars_vertex: uv2_pars_vertex_glsl_default,\\n uv2_vertex: uv2_vertex_glsl_default,\\n worldpos_vertex: worldpos_vertex_glsl_default,\\n background_vert: vertex,\\n background_frag: fragment,\\n cube_vert: vertex2,\\n cube_frag: fragment2,\\n depth_vert: vertex3,\\n depth_frag: fragment3,\\n distanceRGBA_vert: vertex4,\\n distanceRGBA_frag: fragment4,\\n equirect_vert: vertex5,\\n equirect_frag: fragment5,\\n linedashed_vert: vertex6,\\n linedashed_frag: fragment6,\\n meshbasic_vert: vertex7,\\n meshbasic_frag: fragment7,\\n meshlambert_vert: vertex8,\\n meshlambert_frag: fragment8,\\n meshmatcap_vert: vertex9,\\n meshmatcap_frag: fragment9,\\n meshnormal_vert: vertex10,\\n meshnormal_frag: fragment10,\\n meshphong_vert: vertex11,\\n meshphong_frag: fragment11,\\n meshphysical_vert: vertex12,\\n meshphysical_frag: fragment12,\\n meshtoon_vert: vertex13,\\n meshtoon_frag: fragment13,\\n points_vert: vertex14,\\n points_frag: fragment14,\\n shadow_vert: vertex15,\\n shadow_frag: fragment15,\\n sprite_vert: vertex16,\\n sprite_frag: fragment16\\n};\\n\\n// ../../../node_modules/three/src/renderers/shaders/UniformsLib.js\\nvar UniformsLib = {\\n common: {\\n diffuse: {value: new Color(16777215)},\\n opacity: {value: 1},\\n map: {value: null},\\n uvTransform: {value: new Matrix3()},\\n uv2Transform: {value: new Matrix3()},\\n alphaMap: {value: null},\\n alphaTest: {value: 0}\\n },\\n specularmap: {\\n specularMap: {value: null}\\n },\\n envmap: {\\n envMap: {value: null},\\n flipEnvMap: {value: -1},\\n reflectivity: {value: 1},\\n ior: {value: 1.5},\\n refractionRatio: {value: 0.98},\\n maxMipLevel: {value: 0}\\n },\\n aomap: {\\n aoMap: {value: null},\\n aoMapIntensity: {value: 1}\\n },\\n lightmap: {\\n lightMap: {value: null},\\n lightMapIntensity: {value: 1}\\n },\\n emissivemap: {\\n emissiveMap: {value: null}\\n },\\n bumpmap: {\\n bumpMap: {value: null},\\n bumpScale: {value: 1}\\n },\\n normalmap: {\\n normalMap: {value: null},\\n normalScale: {value: new Vector2(1, 1)}\\n },\\n displacementmap: {\\n displacementMap: {value: null},\\n displacementScale: {value: 1},\\n displacementBias: {value: 0}\\n },\\n roughnessmap: {\\n roughnessMap: {value: null}\\n },\\n metalnessmap: {\\n metalnessMap: {value: null}\\n },\\n gradientmap: {\\n gradientMap: {value: null}\\n },\\n fog: {\\n fogDensity: {value: 25e-5},\\n fogNear: {value: 1},\\n fogFar: {value: 2e3},\\n fogColor: {value: new Color(16777215)}\\n },\\n lights: {\\n ambientLightColor: {value: []},\\n lightProbe: {value: []},\\n directionalLights: {value: [], properties: {\\n direction: {},\\n color: {}\\n }},\\n directionalLightShadows: {value: [], properties: {\\n shadowBias: {},\\n shadowNormalBias: {},\\n shadowRadius: {},\\n shadowMapSize: {}\\n }},\\n directionalShadowMap: {value: []},\\n directionalShadowMatrix: {value: []},\\n spotLights: {value: [], properties: {\\n color: {},\\n position: {},\\n direction: {},\\n distance: {},\\n coneCos: {},\\n penumbraCos: {},\\n decay: {}\\n }},\\n spotLightShadows: {value: [], properties: {\\n shadowBias: {},\\n shadowNormalBias: {},\\n shadowRadius: {},\\n shadowMapSize: {}\\n }},\\n spotShadowMap: {value: []},\\n spotShadowMatrix: {value: []},\\n pointLights: {value: [], properties: {\\n color: {},\\n position: {},\\n decay: {},\\n distance: {}\\n }},\\n pointLightShadows: {value: [], properties: {\\n shadowBias: {},\\n shadowNormalBias: {},\\n shadowRadius: {},\\n shadowMapSize: {},\\n shadowCameraNear: {},\\n shadowCameraFar: {}\\n }},\\n pointShadowMap: {value: []},\\n pointShadowMatrix: {value: []},\\n hemisphereLights: {value: [], properties: {\\n direction: {},\\n skyColor: {},\\n groundColor: {}\\n }},\\n rectAreaLights: {value: [], properties: {\\n color: {},\\n position: {},\\n width: {},\\n height: {}\\n }},\\n ltc_1: {value: null},\\n ltc_2: {value: null}\\n },\\n points: {\\n diffuse: {value: new Color(16777215)},\\n opacity: {value: 1},\\n size: {value: 1},\\n scale: {value: 1},\\n map: {value: null},\\n alphaMap: {value: null},\\n alphaTest: {value: 0},\\n uvTransform: {value: new Matrix3()}\\n },\\n sprite: {\\n diffuse: {value: new Color(16777215)},\\n opacity: {value: 1},\\n center: {value: new Vector2(0.5, 0.5)},\\n rotation: {value: 0},\\n map: {value: null},\\n alphaMap: {value: null},\\n alphaTest: {value: 0},\\n uvTransform: {value: new Matrix3()}\\n }\\n};\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib.js\\nvar ShaderLib = {\\n basic: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.specularmap,\\n UniformsLib.envmap,\\n UniformsLib.aomap,\\n UniformsLib.lightmap,\\n UniformsLib.fog\\n ]),\\n vertexShader: ShaderChunk.meshbasic_vert,\\n fragmentShader: ShaderChunk.meshbasic_frag\\n },\\n lambert: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.specularmap,\\n UniformsLib.envmap,\\n UniformsLib.aomap,\\n UniformsLib.lightmap,\\n UniformsLib.emissivemap,\\n UniformsLib.fog,\\n UniformsLib.lights,\\n {\\n emissive: {value: new Color(0)}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshlambert_vert,\\n fragmentShader: ShaderChunk.meshlambert_frag\\n },\\n phong: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.specularmap,\\n UniformsLib.envmap,\\n UniformsLib.aomap,\\n UniformsLib.lightmap,\\n UniformsLib.emissivemap,\\n UniformsLib.bumpmap,\\n UniformsLib.normalmap,\\n UniformsLib.displacementmap,\\n UniformsLib.fog,\\n UniformsLib.lights,\\n {\\n emissive: {value: new Color(0)},\\n specular: {value: new Color(1118481)},\\n shininess: {value: 30}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshphong_vert,\\n fragmentShader: ShaderChunk.meshphong_frag\\n },\\n standard: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.envmap,\\n UniformsLib.aomap,\\n UniformsLib.lightmap,\\n UniformsLib.emissivemap,\\n UniformsLib.bumpmap,\\n UniformsLib.normalmap,\\n UniformsLib.displacementmap,\\n UniformsLib.roughnessmap,\\n UniformsLib.metalnessmap,\\n UniformsLib.fog,\\n UniformsLib.lights,\\n {\\n emissive: {value: new Color(0)},\\n roughness: {value: 1},\\n metalness: {value: 0},\\n envMapIntensity: {value: 1}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshphysical_vert,\\n fragmentShader: ShaderChunk.meshphysical_frag\\n },\\n toon: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.aomap,\\n UniformsLib.lightmap,\\n UniformsLib.emissivemap,\\n UniformsLib.bumpmap,\\n UniformsLib.normalmap,\\n UniformsLib.displacementmap,\\n UniformsLib.gradientmap,\\n UniformsLib.fog,\\n UniformsLib.lights,\\n {\\n emissive: {value: new Color(0)}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshtoon_vert,\\n fragmentShader: ShaderChunk.meshtoon_frag\\n },\\n matcap: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.bumpmap,\\n UniformsLib.normalmap,\\n UniformsLib.displacementmap,\\n UniformsLib.fog,\\n {\\n matcap: {value: null}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshmatcap_vert,\\n fragmentShader: ShaderChunk.meshmatcap_frag\\n },\\n points: {\\n uniforms: mergeUniforms([\\n UniformsLib.points,\\n UniformsLib.fog\\n ]),\\n vertexShader: ShaderChunk.points_vert,\\n fragmentShader: ShaderChunk.points_frag\\n },\\n dashed: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.fog,\\n {\\n scale: {value: 1},\\n dashSize: {value: 1},\\n totalSize: {value: 2}\\n }\\n ]),\\n vertexShader: ShaderChunk.linedashed_vert,\\n fragmentShader: ShaderChunk.linedashed_frag\\n },\\n depth: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.displacementmap\\n ]),\\n vertexShader: ShaderChunk.depth_vert,\\n fragmentShader: ShaderChunk.depth_frag\\n },\\n normal: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.bumpmap,\\n UniformsLib.normalmap,\\n UniformsLib.displacementmap,\\n {\\n opacity: {value: 1}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshnormal_vert,\\n fragmentShader: ShaderChunk.meshnormal_frag\\n },\\n sprite: {\\n uniforms: mergeUniforms([\\n UniformsLib.sprite,\\n UniformsLib.fog\\n ]),\\n vertexShader: ShaderChunk.sprite_vert,\\n fragmentShader: ShaderChunk.sprite_frag\\n },\\n background: {\\n uniforms: {\\n uvTransform: {value: new Matrix3()},\\n t2D: {value: null}\\n },\\n vertexShader: ShaderChunk.background_vert,\\n fragmentShader: ShaderChunk.background_frag\\n },\\n cube: {\\n uniforms: mergeUniforms([\\n UniformsLib.envmap,\\n {\\n opacity: {value: 1}\\n }\\n ]),\\n vertexShader: ShaderChunk.cube_vert,\\n fragmentShader: ShaderChunk.cube_frag\\n },\\n equirect: {\\n uniforms: {\\n tEquirect: {value: null}\\n },\\n vertexShader: ShaderChunk.equirect_vert,\\n fragmentShader: ShaderChunk.equirect_frag\\n },\\n distanceRGBA: {\\n uniforms: mergeUniforms([\\n UniformsLib.common,\\n UniformsLib.displacementmap,\\n {\\n referencePosition: {value: new Vector3()},\\n nearDistance: {value: 1},\\n farDistance: {value: 1e3}\\n }\\n ]),\\n vertexShader: ShaderChunk.distanceRGBA_vert,\\n fragmentShader: ShaderChunk.distanceRGBA_frag\\n },\\n shadow: {\\n uniforms: mergeUniforms([\\n UniformsLib.lights,\\n UniformsLib.fog,\\n {\\n color: {value: new Color(0)},\\n opacity: {value: 1}\\n }\\n ]),\\n vertexShader: ShaderChunk.shadow_vert,\\n fragmentShader: ShaderChunk.shadow_frag\\n }\\n};\\nShaderLib.physical = {\\n uniforms: mergeUniforms([\\n ShaderLib.standard.uniforms,\\n {\\n clearcoat: {value: 0},\\n clearcoatMap: {value: null},\\n clearcoatRoughness: {value: 0},\\n clearcoatRoughnessMap: {value: null},\\n clearcoatNormalScale: {value: new Vector2(1, 1)},\\n clearcoatNormalMap: {value: null},\\n sheen: {value: 0},\\n sheenTint: {value: new Color(0)},\\n sheenRoughness: {value: 0},\\n transmission: {value: 0},\\n transmissionMap: {value: null},\\n transmissionSamplerSize: {value: new Vector2()},\\n transmissionSamplerMap: {value: null},\\n thickness: {value: 0},\\n thicknessMap: {value: null},\\n attenuationDistance: {value: 0},\\n attenuationTint: {value: new Color(0)},\\n specularIntensity: {value: 0},\\n specularIntensityMap: {value: null},\\n specularTint: {value: new Color(1, 1, 1)},\\n specularTintMap: {value: null}\\n }\\n ]),\\n vertexShader: ShaderChunk.meshphysical_vert,\\n fragmentShader: ShaderChunk.meshphysical_frag\\n};\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLBackground.js\\nfunction WebGLBackground(renderer, cubemaps, state, objects, premultipliedAlpha) {\\n const clearColor = new Color(0);\\n let clearAlpha = 0;\\n let planeMesh;\\n let boxMesh;\\n let currentBackground = null;\\n let currentBackgroundVersion = 0;\\n let currentTonemapping = null;\\n function render(renderList, scene) {\\n let forceClear = false;\\n let background2 = scene.isScene === true ? scene.background : null;\\n if (background2 && background2.isTexture) {\\n background2 = cubemaps.get(background2);\\n }\\n const xr = renderer.xr;\\n const session = xr.getSession && xr.getSession();\\n if (session && session.environmentBlendMode === \\\\\\\"additive\\\\\\\") {\\n background2 = null;\\n }\\n if (background2 === null) {\\n setClear(clearColor, clearAlpha);\\n } else if (background2 && background2.isColor) {\\n setClear(background2, 1);\\n forceClear = true;\\n }\\n if (renderer.autoClear || forceClear) {\\n renderer.clear(renderer.autoClearColor, renderer.autoClearDepth, renderer.autoClearStencil);\\n }\\n if (background2 && (background2.isCubeTexture || background2.mapping === CubeUVReflectionMapping)) {\\n if (boxMesh === void 0) {\\n boxMesh = new Mesh(new BoxGeometry(1, 1, 1), new ShaderMaterial({\\n name: \\\\\\\"BackgroundCubeMaterial\\\\\\\",\\n uniforms: cloneUniforms(ShaderLib.cube.uniforms),\\n vertexShader: ShaderLib.cube.vertexShader,\\n fragmentShader: ShaderLib.cube.fragmentShader,\\n side: BackSide,\\n depthTest: false,\\n depthWrite: false,\\n fog: false\\n }));\\n boxMesh.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\");\\n boxMesh.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\");\\n boxMesh.onBeforeRender = function(renderer2, scene2, camera) {\\n this.matrixWorld.copyPosition(camera.matrixWorld);\\n };\\n Object.defineProperty(boxMesh.material, \\\\\\\"envMap\\\\\\\", {\\n get: function() {\\n return this.uniforms.envMap.value;\\n }\\n });\\n objects.update(boxMesh);\\n }\\n boxMesh.material.uniforms.envMap.value = background2;\\n boxMesh.material.uniforms.flipEnvMap.value = background2.isCubeTexture && background2.isRenderTargetTexture === false ? -1 : 1;\\n if (currentBackground !== background2 || currentBackgroundVersion !== background2.version || currentTonemapping !== renderer.toneMapping) {\\n boxMesh.material.needsUpdate = true;\\n currentBackground = background2;\\n currentBackgroundVersion = background2.version;\\n currentTonemapping = renderer.toneMapping;\\n }\\n renderList.unshift(boxMesh, boxMesh.geometry, boxMesh.material, 0, 0, null);\\n } else if (background2 && background2.isTexture) {\\n if (planeMesh === void 0) {\\n planeMesh = new Mesh(new PlaneGeometry(2, 2), new ShaderMaterial({\\n name: \\\\\\\"BackgroundMaterial\\\\\\\",\\n uniforms: cloneUniforms(ShaderLib.background.uniforms),\\n vertexShader: ShaderLib.background.vertexShader,\\n fragmentShader: ShaderLib.background.fragmentShader,\\n side: FrontSide,\\n depthTest: false,\\n depthWrite: false,\\n fog: false\\n }));\\n planeMesh.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\");\\n Object.defineProperty(planeMesh.material, \\\\\\\"map\\\\\\\", {\\n get: function() {\\n return this.uniforms.t2D.value;\\n }\\n });\\n objects.update(planeMesh);\\n }\\n planeMesh.material.uniforms.t2D.value = background2;\\n if (background2.matrixAutoUpdate === true) {\\n background2.updateMatrix();\\n }\\n planeMesh.material.uniforms.uvTransform.value.copy(background2.matrix);\\n if (currentBackground !== background2 || currentBackgroundVersion !== background2.version || currentTonemapping !== renderer.toneMapping) {\\n planeMesh.material.needsUpdate = true;\\n currentBackground = background2;\\n currentBackgroundVersion = background2.version;\\n currentTonemapping = renderer.toneMapping;\\n }\\n renderList.unshift(planeMesh, planeMesh.geometry, planeMesh.material, 0, 0, null);\\n }\\n }\\n function setClear(color, alpha) {\\n state.buffers.color.setClear(color.r, color.g, color.b, alpha, premultipliedAlpha);\\n }\\n return {\\n getClearColor: function() {\\n return clearColor;\\n },\\n setClearColor: function(color, alpha = 1) {\\n clearColor.set(color);\\n clearAlpha = alpha;\\n setClear(clearColor, clearAlpha);\\n },\\n getClearAlpha: function() {\\n return clearAlpha;\\n },\\n setClearAlpha: function(alpha) {\\n clearAlpha = alpha;\\n setClear(clearColor, clearAlpha);\\n },\\n render\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLBindingStates.js\\nfunction WebGLBindingStates(gl, extensions, attributes, capabilities) {\\n const maxVertexAttributes = gl.getParameter(gl.MAX_VERTEX_ATTRIBS);\\n const extension = capabilities.isWebGL2 ? null : extensions.get(\\\\\\\"OES_vertex_array_object\\\\\\\");\\n const vaoAvailable = capabilities.isWebGL2 || extension !== null;\\n const bindingStates = {};\\n const defaultState = createBindingState(null);\\n let currentState = defaultState;\\n function setup(object, material, program, geometry, index) {\\n let updateBuffers = false;\\n if (vaoAvailable) {\\n const state = getBindingState(geometry, program, material);\\n if (currentState !== state) {\\n currentState = state;\\n bindVertexArrayObject(currentState.object);\\n }\\n updateBuffers = needsUpdate(geometry, index);\\n if (updateBuffers)\\n saveCache(geometry, index);\\n } else {\\n const wireframe = material.wireframe === true;\\n if (currentState.geometry !== geometry.id || currentState.program !== program.id || currentState.wireframe !== wireframe) {\\n currentState.geometry = geometry.id;\\n currentState.program = program.id;\\n currentState.wireframe = wireframe;\\n updateBuffers = true;\\n }\\n }\\n if (object.isInstancedMesh === true) {\\n updateBuffers = true;\\n }\\n if (index !== null) {\\n attributes.update(index, gl.ELEMENT_ARRAY_BUFFER);\\n }\\n if (updateBuffers) {\\n setupVertexAttributes(object, material, program, geometry);\\n if (index !== null) {\\n gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, attributes.get(index).buffer);\\n }\\n }\\n }\\n function createVertexArrayObject() {\\n if (capabilities.isWebGL2)\\n return gl.createVertexArray();\\n return extension.createVertexArrayOES();\\n }\\n function bindVertexArrayObject(vao) {\\n if (capabilities.isWebGL2)\\n return gl.bindVertexArray(vao);\\n return extension.bindVertexArrayOES(vao);\\n }\\n function deleteVertexArrayObject(vao) {\\n if (capabilities.isWebGL2)\\n return gl.deleteVertexArray(vao);\\n return extension.deleteVertexArrayOES(vao);\\n }\\n function getBindingState(geometry, program, material) {\\n const wireframe = material.wireframe === true;\\n let programMap = bindingStates[geometry.id];\\n if (programMap === void 0) {\\n programMap = {};\\n bindingStates[geometry.id] = programMap;\\n }\\n let stateMap = programMap[program.id];\\n if (stateMap === void 0) {\\n stateMap = {};\\n programMap[program.id] = stateMap;\\n }\\n let state = stateMap[wireframe];\\n if (state === void 0) {\\n state = createBindingState(createVertexArrayObject());\\n stateMap[wireframe] = state;\\n }\\n return state;\\n }\\n function createBindingState(vao) {\\n const newAttributes = [];\\n const enabledAttributes = [];\\n const attributeDivisors = [];\\n for (let i = 0; i < maxVertexAttributes; i++) {\\n newAttributes[i] = 0;\\n enabledAttributes[i] = 0;\\n attributeDivisors[i] = 0;\\n }\\n return {\\n geometry: null,\\n program: null,\\n wireframe: false,\\n newAttributes,\\n enabledAttributes,\\n attributeDivisors,\\n object: vao,\\n attributes: {},\\n index: null\\n };\\n }\\n function needsUpdate(geometry, index) {\\n const cachedAttributes = currentState.attributes;\\n const geometryAttributes = geometry.attributes;\\n let attributesNum = 0;\\n for (const key in geometryAttributes) {\\n const cachedAttribute = cachedAttributes[key];\\n const geometryAttribute = geometryAttributes[key];\\n if (cachedAttribute === void 0)\\n return true;\\n if (cachedAttribute.attribute !== geometryAttribute)\\n return true;\\n if (cachedAttribute.data !== geometryAttribute.data)\\n return true;\\n attributesNum++;\\n }\\n if (currentState.attributesNum !== attributesNum)\\n return true;\\n if (currentState.index !== index)\\n return true;\\n return false;\\n }\\n function saveCache(geometry, index) {\\n const cache = {};\\n const attributes2 = geometry.attributes;\\n let attributesNum = 0;\\n for (const key in attributes2) {\\n const attribute = attributes2[key];\\n const data = {};\\n data.attribute = attribute;\\n if (attribute.data) {\\n data.data = attribute.data;\\n }\\n cache[key] = data;\\n attributesNum++;\\n }\\n currentState.attributes = cache;\\n currentState.attributesNum = attributesNum;\\n currentState.index = index;\\n }\\n function initAttributes() {\\n const newAttributes = currentState.newAttributes;\\n for (let i = 0, il = newAttributes.length; i < il; i++) {\\n newAttributes[i] = 0;\\n }\\n }\\n function enableAttribute(attribute) {\\n enableAttributeAndDivisor(attribute, 0);\\n }\\n function enableAttributeAndDivisor(attribute, meshPerAttribute) {\\n const newAttributes = currentState.newAttributes;\\n const enabledAttributes = currentState.enabledAttributes;\\n const attributeDivisors = currentState.attributeDivisors;\\n newAttributes[attribute] = 1;\\n if (enabledAttributes[attribute] === 0) {\\n gl.enableVertexAttribArray(attribute);\\n enabledAttributes[attribute] = 1;\\n }\\n if (attributeDivisors[attribute] !== meshPerAttribute) {\\n const extension2 = capabilities.isWebGL2 ? gl : extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\");\\n extension2[capabilities.isWebGL2 ? \\\\\\\"vertexAttribDivisor\\\\\\\" : \\\\\\\"vertexAttribDivisorANGLE\\\\\\\"](attribute, meshPerAttribute);\\n attributeDivisors[attribute] = meshPerAttribute;\\n }\\n }\\n function disableUnusedAttributes() {\\n const newAttributes = currentState.newAttributes;\\n const enabledAttributes = currentState.enabledAttributes;\\n for (let i = 0, il = enabledAttributes.length; i < il; i++) {\\n if (enabledAttributes[i] !== newAttributes[i]) {\\n gl.disableVertexAttribArray(i);\\n enabledAttributes[i] = 0;\\n }\\n }\\n }\\n function vertexAttribPointer(index, size, type, normalized, stride, offset) {\\n if (capabilities.isWebGL2 === true && (type === gl.INT || type === gl.UNSIGNED_INT)) {\\n gl.vertexAttribIPointer(index, size, type, stride, offset);\\n } else {\\n gl.vertexAttribPointer(index, size, type, normalized, stride, offset);\\n }\\n }\\n function setupVertexAttributes(object, material, program, geometry) {\\n if (capabilities.isWebGL2 === false && (object.isInstancedMesh || geometry.isInstancedBufferGeometry)) {\\n if (extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\") === null)\\n return;\\n }\\n initAttributes();\\n const geometryAttributes = geometry.attributes;\\n const programAttributes = program.getAttributes();\\n const materialDefaultAttributeValues = material.defaultAttributeValues;\\n for (const name in programAttributes) {\\n const programAttribute = programAttributes[name];\\n if (programAttribute.location >= 0) {\\n let geometryAttribute = geometryAttributes[name];\\n if (geometryAttribute === void 0) {\\n if (name === \\\\\\\"instanceMatrix\\\\\\\" && object.instanceMatrix)\\n geometryAttribute = object.instanceMatrix;\\n if (name === \\\\\\\"instanceColor\\\\\\\" && object.instanceColor)\\n geometryAttribute = object.instanceColor;\\n }\\n if (geometryAttribute !== void 0) {\\n const normalized = geometryAttribute.normalized;\\n const size = geometryAttribute.itemSize;\\n const attribute = attributes.get(geometryAttribute);\\n if (attribute === void 0)\\n continue;\\n const buffer = attribute.buffer;\\n const type = attribute.type;\\n const bytesPerElement = attribute.bytesPerElement;\\n if (geometryAttribute.isInterleavedBufferAttribute) {\\n const data = geometryAttribute.data;\\n const stride = data.stride;\\n const offset = geometryAttribute.offset;\\n if (data && data.isInstancedInterleavedBuffer) {\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n enableAttributeAndDivisor(programAttribute.location + i, data.meshPerAttribute);\\n }\\n if (object.isInstancedMesh !== true && geometry._maxInstanceCount === void 0) {\\n geometry._maxInstanceCount = data.meshPerAttribute * data.count;\\n }\\n } else {\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n enableAttribute(programAttribute.location + i);\\n }\\n }\\n gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n vertexAttribPointer(programAttribute.location + i, size / programAttribute.locationSize, type, normalized, stride * bytesPerElement, (offset + size / programAttribute.locationSize * i) * bytesPerElement);\\n }\\n } else {\\n if (geometryAttribute.isInstancedBufferAttribute) {\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n enableAttributeAndDivisor(programAttribute.location + i, geometryAttribute.meshPerAttribute);\\n }\\n if (object.isInstancedMesh !== true && geometry._maxInstanceCount === void 0) {\\n geometry._maxInstanceCount = geometryAttribute.meshPerAttribute * geometryAttribute.count;\\n }\\n } else {\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n enableAttribute(programAttribute.location + i);\\n }\\n }\\n gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\\n for (let i = 0; i < programAttribute.locationSize; i++) {\\n vertexAttribPointer(programAttribute.location + i, size / programAttribute.locationSize, type, normalized, size * bytesPerElement, size / programAttribute.locationSize * i * bytesPerElement);\\n }\\n }\\n } else if (materialDefaultAttributeValues !== void 0) {\\n const value = materialDefaultAttributeValues[name];\\n if (value !== void 0) {\\n switch (value.length) {\\n case 2:\\n gl.vertexAttrib2fv(programAttribute.location, value);\\n break;\\n case 3:\\n gl.vertexAttrib3fv(programAttribute.location, value);\\n break;\\n case 4:\\n gl.vertexAttrib4fv(programAttribute.location, value);\\n break;\\n default:\\n gl.vertexAttrib1fv(programAttribute.location, value);\\n }\\n }\\n }\\n }\\n }\\n disableUnusedAttributes();\\n }\\n function dispose() {\\n reset();\\n for (const geometryId in bindingStates) {\\n const programMap = bindingStates[geometryId];\\n for (const programId in programMap) {\\n const stateMap = programMap[programId];\\n for (const wireframe in stateMap) {\\n deleteVertexArrayObject(stateMap[wireframe].object);\\n delete stateMap[wireframe];\\n }\\n delete programMap[programId];\\n }\\n delete bindingStates[geometryId];\\n }\\n }\\n function releaseStatesOfGeometry(geometry) {\\n if (bindingStates[geometry.id] === void 0)\\n return;\\n const programMap = bindingStates[geometry.id];\\n for (const programId in programMap) {\\n const stateMap = programMap[programId];\\n for (const wireframe in stateMap) {\\n deleteVertexArrayObject(stateMap[wireframe].object);\\n delete stateMap[wireframe];\\n }\\n delete programMap[programId];\\n }\\n delete bindingStates[geometry.id];\\n }\\n function releaseStatesOfProgram(program) {\\n for (const geometryId in bindingStates) {\\n const programMap = bindingStates[geometryId];\\n if (programMap[program.id] === void 0)\\n continue;\\n const stateMap = programMap[program.id];\\n for (const wireframe in stateMap) {\\n deleteVertexArrayObject(stateMap[wireframe].object);\\n delete stateMap[wireframe];\\n }\\n delete programMap[program.id];\\n }\\n }\\n function reset() {\\n resetDefaultState();\\n if (currentState === defaultState)\\n return;\\n currentState = defaultState;\\n bindVertexArrayObject(currentState.object);\\n }\\n function resetDefaultState() {\\n defaultState.geometry = null;\\n defaultState.program = null;\\n defaultState.wireframe = false;\\n }\\n return {\\n setup,\\n reset,\\n resetDefaultState,\\n dispose,\\n releaseStatesOfGeometry,\\n releaseStatesOfProgram,\\n initAttributes,\\n enableAttribute,\\n disableUnusedAttributes\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLBufferRenderer.js\\nfunction WebGLBufferRenderer(gl, extensions, info, capabilities) {\\n const isWebGL2 = capabilities.isWebGL2;\\n let mode;\\n function setMode(value) {\\n mode = value;\\n }\\n function render(start, count) {\\n gl.drawArrays(mode, start, count);\\n info.update(count, mode, 1);\\n }\\n function renderInstances(start, count, primcount) {\\n if (primcount === 0)\\n return;\\n let extension, methodName;\\n if (isWebGL2) {\\n extension = gl;\\n methodName = \\\\\\\"drawArraysInstanced\\\\\\\";\\n } else {\\n extension = extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\");\\n methodName = \\\\\\\"drawArraysInstancedANGLE\\\\\\\";\\n if (extension === null) {\\n console.error(\\\\\\\"THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");\\n return;\\n }\\n }\\n extension[methodName](mode, start, count, primcount);\\n info.update(count, mode, primcount);\\n }\\n this.setMode = setMode;\\n this.render = render;\\n this.renderInstances = renderInstances;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLCapabilities.js\\nfunction WebGLCapabilities(gl, extensions, parameters) {\\n let maxAnisotropy;\\n function getMaxAnisotropy() {\\n if (maxAnisotropy !== void 0)\\n return maxAnisotropy;\\n if (extensions.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\") === true) {\\n const extension = extensions.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");\\n maxAnisotropy = gl.getParameter(extension.MAX_TEXTURE_MAX_ANISOTROPY_EXT);\\n } else {\\n maxAnisotropy = 0;\\n }\\n return maxAnisotropy;\\n }\\n function getMaxPrecision(precision2) {\\n if (precision2 === \\\\\\\"highp\\\\\\\") {\\n if (gl.getShaderPrecisionFormat(gl.VERTEX_SHADER, gl.HIGH_FLOAT).precision > 0 && gl.getShaderPrecisionFormat(gl.FRAGMENT_SHADER, gl.HIGH_FLOAT).precision > 0) {\\n return \\\\\\\"highp\\\\\\\";\\n }\\n precision2 = \\\\\\\"mediump\\\\\\\";\\n }\\n if (precision2 === \\\\\\\"mediump\\\\\\\") {\\n if (gl.getShaderPrecisionFormat(gl.VERTEX_SHADER, gl.MEDIUM_FLOAT).precision > 0 && gl.getShaderPrecisionFormat(gl.FRAGMENT_SHADER, gl.MEDIUM_FLOAT).precision > 0) {\\n return \\\\\\\"mediump\\\\\\\";\\n }\\n }\\n return \\\\\\\"lowp\\\\\\\";\\n }\\n const isWebGL2 = typeof WebGL2RenderingContext !== \\\\\\\"undefined\\\\\\\" && gl instanceof WebGL2RenderingContext || typeof WebGL2ComputeRenderingContext !== \\\\\\\"undefined\\\\\\\" && gl instanceof WebGL2ComputeRenderingContext;\\n let precision = parameters.precision !== void 0 ? parameters.precision : \\\\\\\"highp\\\\\\\";\\n const maxPrecision = getMaxPrecision(precision);\\n if (maxPrecision !== precision) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\", precision, \\\\\\\"not supported, using\\\\\\\", maxPrecision, \\\\\\\"instead.\\\\\\\");\\n precision = maxPrecision;\\n }\\n const drawBuffers = isWebGL2 || extensions.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\");\\n const logarithmicDepthBuffer = parameters.logarithmicDepthBuffer === true;\\n const maxTextures = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\\n const maxVertexTextures = gl.getParameter(gl.MAX_VERTEX_TEXTURE_IMAGE_UNITS);\\n const maxTextureSize = gl.getParameter(gl.MAX_TEXTURE_SIZE);\\n const maxCubemapSize = gl.getParameter(gl.MAX_CUBE_MAP_TEXTURE_SIZE);\\n const maxAttributes = gl.getParameter(gl.MAX_VERTEX_ATTRIBS);\\n const maxVertexUniforms = gl.getParameter(gl.MAX_VERTEX_UNIFORM_VECTORS);\\n const maxVaryings = gl.getParameter(gl.MAX_VARYING_VECTORS);\\n const maxFragmentUniforms = gl.getParameter(gl.MAX_FRAGMENT_UNIFORM_VECTORS);\\n const vertexTextures = maxVertexTextures > 0;\\n const floatFragmentTextures = isWebGL2 || extensions.has(\\\\\\\"OES_texture_float\\\\\\\");\\n const floatVertexTextures = vertexTextures && floatFragmentTextures;\\n const maxSamples = isWebGL2 ? gl.getParameter(gl.MAX_SAMPLES) : 0;\\n return {\\n isWebGL2,\\n drawBuffers,\\n getMaxAnisotropy,\\n getMaxPrecision,\\n precision,\\n logarithmicDepthBuffer,\\n maxTextures,\\n maxVertexTextures,\\n maxTextureSize,\\n maxCubemapSize,\\n maxAttributes,\\n maxVertexUniforms,\\n maxVaryings,\\n maxFragmentUniforms,\\n vertexTextures,\\n floatFragmentTextures,\\n floatVertexTextures,\\n maxSamples\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLClipping.js\\nfunction WebGLClipping(properties) {\\n const scope = this;\\n let globalState = null, numGlobalPlanes = 0, localClippingEnabled = false, renderingShadows = false;\\n const plane = new Plane(), viewNormalMatrix = new Matrix3(), uniform = {value: null, needsUpdate: false};\\n this.uniform = uniform;\\n this.numPlanes = 0;\\n this.numIntersection = 0;\\n this.init = function(planes, enableLocalClipping, camera) {\\n const enabled = planes.length !== 0 || enableLocalClipping || numGlobalPlanes !== 0 || localClippingEnabled;\\n localClippingEnabled = enableLocalClipping;\\n globalState = projectPlanes(planes, camera, 0);\\n numGlobalPlanes = planes.length;\\n return enabled;\\n };\\n this.beginShadows = function() {\\n renderingShadows = true;\\n projectPlanes(null);\\n };\\n this.endShadows = function() {\\n renderingShadows = false;\\n resetGlobalState();\\n };\\n this.setState = function(material, camera, useCache) {\\n const planes = material.clippingPlanes, clipIntersection = material.clipIntersection, clipShadows = material.clipShadows;\\n const materialProperties = properties.get(material);\\n if (!localClippingEnabled || planes === null || planes.length === 0 || renderingShadows && !clipShadows) {\\n if (renderingShadows) {\\n projectPlanes(null);\\n } else {\\n resetGlobalState();\\n }\\n } else {\\n const nGlobal = renderingShadows ? 0 : numGlobalPlanes, lGlobal = nGlobal * 4;\\n let dstArray = materialProperties.clippingState || null;\\n uniform.value = dstArray;\\n dstArray = projectPlanes(planes, camera, lGlobal, useCache);\\n for (let i = 0; i !== lGlobal; ++i) {\\n dstArray[i] = globalState[i];\\n }\\n materialProperties.clippingState = dstArray;\\n this.numIntersection = clipIntersection ? this.numPlanes : 0;\\n this.numPlanes += nGlobal;\\n }\\n };\\n function resetGlobalState() {\\n if (uniform.value !== globalState) {\\n uniform.value = globalState;\\n uniform.needsUpdate = numGlobalPlanes > 0;\\n }\\n scope.numPlanes = numGlobalPlanes;\\n scope.numIntersection = 0;\\n }\\n function projectPlanes(planes, camera, dstOffset, skipTransform) {\\n const nPlanes = planes !== null ? planes.length : 0;\\n let dstArray = null;\\n if (nPlanes !== 0) {\\n dstArray = uniform.value;\\n if (skipTransform !== true || dstArray === null) {\\n const flatSize = dstOffset + nPlanes * 4, viewMatrix = camera.matrixWorldInverse;\\n viewNormalMatrix.getNormalMatrix(viewMatrix);\\n if (dstArray === null || dstArray.length < flatSize) {\\n dstArray = new Float32Array(flatSize);\\n }\\n for (let i = 0, i4 = dstOffset; i !== nPlanes; ++i, i4 += 4) {\\n plane.copy(planes[i]).applyMatrix4(viewMatrix, viewNormalMatrix);\\n plane.normal.toArray(dstArray, i4);\\n dstArray[i4 + 3] = plane.constant;\\n }\\n }\\n uniform.value = dstArray;\\n uniform.needsUpdate = true;\\n }\\n scope.numPlanes = nPlanes;\\n scope.numIntersection = 0;\\n return dstArray;\\n }\\n}\\n\\n// ../../../node_modules/three/src/extras/ImageUtils.js\\nvar _canvas;\\nvar ImageUtils = class {\\n static getDataURL(image) {\\n if (/^data:/i.test(image.src)) {\\n return image.src;\\n }\\n if (typeof HTMLCanvasElement == \\\\\\\"undefined\\\\\\\") {\\n return image.src;\\n }\\n let canvas;\\n if (image instanceof HTMLCanvasElement) {\\n canvas = image;\\n } else {\\n if (_canvas === void 0)\\n _canvas = createElementNS(\\\\\\\"canvas\\\\\\\");\\n _canvas.width = image.width;\\n _canvas.height = image.height;\\n const context = _canvas.getContext(\\\\\\\"2d\\\\\\\");\\n if (image instanceof ImageData) {\\n context.putImageData(image, 0, 0);\\n } else {\\n context.drawImage(image, 0, 0, image.width, image.height);\\n }\\n canvas = _canvas;\\n }\\n if (canvas.width > 2048 || canvas.height > 2048) {\\n console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\", image);\\n return canvas.toDataURL(\\\\\\\"image/jpeg\\\\\\\", 0.6);\\n } else {\\n return canvas.toDataURL(\\\\\\\"image/png\\\\\\\");\\n }\\n }\\n};\\n\\n// ../../../node_modules/three/src/textures/Texture.js\\nvar textureId = 0;\\nvar Texture = class extends EventDispatcher {\\n constructor(image = Texture.DEFAULT_IMAGE, mapping = Texture.DEFAULT_MAPPING, wrapS = ClampToEdgeWrapping, wrapT = ClampToEdgeWrapping, magFilter = LinearFilter, minFilter = LinearMipmapLinearFilter, format = RGBAFormat, type = UnsignedByteType, anisotropy = 1, encoding = LinearEncoding) {\\n super();\\n Object.defineProperty(this, \\\\\\\"id\\\\\\\", {value: textureId++});\\n this.uuid = generateUUID();\\n this.name = \\\\\\\"\\\\\\\";\\n this.image = image;\\n this.mipmaps = [];\\n this.mapping = mapping;\\n this.wrapS = wrapS;\\n this.wrapT = wrapT;\\n this.magFilter = magFilter;\\n this.minFilter = minFilter;\\n this.anisotropy = anisotropy;\\n this.format = format;\\n this.internalFormat = null;\\n this.type = type;\\n this.offset = new Vector2(0, 0);\\n this.repeat = new Vector2(1, 1);\\n this.center = new Vector2(0, 0);\\n this.rotation = 0;\\n this.matrixAutoUpdate = true;\\n this.matrix = new Matrix3();\\n this.generateMipmaps = true;\\n this.premultiplyAlpha = false;\\n this.flipY = true;\\n this.unpackAlignment = 4;\\n this.encoding = encoding;\\n this.version = 0;\\n this.onUpdate = null;\\n this.isRenderTargetTexture = false;\\n }\\n updateMatrix() {\\n this.matrix.setUvTransform(this.offset.x, this.offset.y, this.repeat.x, this.repeat.y, this.rotation, this.center.x, this.center.y);\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(source) {\\n this.name = source.name;\\n this.image = source.image;\\n this.mipmaps = source.mipmaps.slice(0);\\n this.mapping = source.mapping;\\n this.wrapS = source.wrapS;\\n this.wrapT = source.wrapT;\\n this.magFilter = source.magFilter;\\n this.minFilter = source.minFilter;\\n this.anisotropy = source.anisotropy;\\n this.format = source.format;\\n this.internalFormat = source.internalFormat;\\n this.type = source.type;\\n this.offset.copy(source.offset);\\n this.repeat.copy(source.repeat);\\n this.center.copy(source.center);\\n this.rotation = source.rotation;\\n this.matrixAutoUpdate = source.matrixAutoUpdate;\\n this.matrix.copy(source.matrix);\\n this.generateMipmaps = source.generateMipmaps;\\n this.premultiplyAlpha = source.premultiplyAlpha;\\n this.flipY = source.flipY;\\n this.unpackAlignment = source.unpackAlignment;\\n this.encoding = source.encoding;\\n return this;\\n }\\n toJSON(meta) {\\n const isRootObject = meta === void 0 || typeof meta === \\\\\\\"string\\\\\\\";\\n if (!isRootObject && meta.textures[this.uuid] !== void 0) {\\n return meta.textures[this.uuid];\\n }\\n const output = {\\n metadata: {\\n version: 4.5,\\n type: \\\\\\\"Texture\\\\\\\",\\n generator: \\\\\\\"Texture.toJSON\\\\\\\"\\n },\\n uuid: this.uuid,\\n name: this.name,\\n mapping: this.mapping,\\n repeat: [this.repeat.x, this.repeat.y],\\n offset: [this.offset.x, this.offset.y],\\n center: [this.center.x, this.center.y],\\n rotation: this.rotation,\\n wrap: [this.wrapS, this.wrapT],\\n format: this.format,\\n type: this.type,\\n encoding: this.encoding,\\n minFilter: this.minFilter,\\n magFilter: this.magFilter,\\n anisotropy: this.anisotropy,\\n flipY: this.flipY,\\n premultiplyAlpha: this.premultiplyAlpha,\\n unpackAlignment: this.unpackAlignment\\n };\\n if (this.image !== void 0) {\\n const image = this.image;\\n if (image.uuid === void 0) {\\n image.uuid = generateUUID();\\n }\\n if (!isRootObject && meta.images[image.uuid] === void 0) {\\n let url;\\n if (Array.isArray(image)) {\\n url = [];\\n for (let i = 0, l = image.length; i < l; i++) {\\n if (image[i].isDataTexture) {\\n url.push(serializeImage(image[i].image));\\n } else {\\n url.push(serializeImage(image[i]));\\n }\\n }\\n } else {\\n url = serializeImage(image);\\n }\\n meta.images[image.uuid] = {\\n uuid: image.uuid,\\n url\\n };\\n }\\n output.image = image.uuid;\\n }\\n if (!isRootObject) {\\n meta.textures[this.uuid] = output;\\n }\\n return output;\\n }\\n dispose() {\\n this.dispatchEvent({type: \\\\\\\"dispose\\\\\\\"});\\n }\\n transformUv(uv) {\\n if (this.mapping !== UVMapping)\\n return uv;\\n uv.applyMatrix3(this.matrix);\\n if (uv.x < 0 || uv.x > 1) {\\n switch (this.wrapS) {\\n case RepeatWrapping:\\n uv.x = uv.x - Math.floor(uv.x);\\n break;\\n case ClampToEdgeWrapping:\\n uv.x = uv.x < 0 ? 0 : 1;\\n break;\\n case MirroredRepeatWrapping:\\n if (Math.abs(Math.floor(uv.x) % 2) === 1) {\\n uv.x = Math.ceil(uv.x) - uv.x;\\n } else {\\n uv.x = uv.x - Math.floor(uv.x);\\n }\\n break;\\n }\\n }\\n if (uv.y < 0 || uv.y > 1) {\\n switch (this.wrapT) {\\n case RepeatWrapping:\\n uv.y = uv.y - Math.floor(uv.y);\\n break;\\n case ClampToEdgeWrapping:\\n uv.y = uv.y < 0 ? 0 : 1;\\n break;\\n case MirroredRepeatWrapping:\\n if (Math.abs(Math.floor(uv.y) % 2) === 1) {\\n uv.y = Math.ceil(uv.y) - uv.y;\\n } else {\\n uv.y = uv.y - Math.floor(uv.y);\\n }\\n break;\\n }\\n }\\n if (this.flipY) {\\n uv.y = 1 - uv.y;\\n }\\n return uv;\\n }\\n set needsUpdate(value) {\\n if (value === true)\\n this.version++;\\n }\\n};\\nTexture.DEFAULT_IMAGE = void 0;\\nTexture.DEFAULT_MAPPING = UVMapping;\\nTexture.prototype.isTexture = true;\\nfunction serializeImage(image) {\\n if (typeof HTMLImageElement !== \\\\\\\"undefined\\\\\\\" && image instanceof HTMLImageElement || typeof HTMLCanvasElement !== \\\\\\\"undefined\\\\\\\" && image instanceof HTMLCanvasElement || typeof ImageBitmap !== \\\\\\\"undefined\\\\\\\" && image instanceof ImageBitmap) {\\n return ImageUtils.getDataURL(image);\\n } else {\\n if (image.data) {\\n return {\\n data: Array.prototype.slice.call(image.data),\\n width: image.width,\\n height: image.height,\\n type: image.data.constructor.name\\n };\\n } else {\\n console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\");\\n return {};\\n }\\n }\\n}\\n\\n// ../../../node_modules/three/src/renderers/WebGLRenderTarget.js\\nvar WebGLRenderTarget = class extends EventDispatcher {\\n constructor(width, height, options = {}) {\\n super();\\n this.width = width;\\n this.height = height;\\n this.depth = 1;\\n this.scissor = new Vector4(0, 0, width, height);\\n this.scissorTest = false;\\n this.viewport = new Vector4(0, 0, width, height);\\n this.texture = new Texture(void 0, options.mapping, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.encoding);\\n this.texture.isRenderTargetTexture = true;\\n this.texture.image = {width, height, depth: 1};\\n this.texture.generateMipmaps = options.generateMipmaps !== void 0 ? options.generateMipmaps : false;\\n this.texture.internalFormat = options.internalFormat !== void 0 ? options.internalFormat : null;\\n this.texture.minFilter = options.minFilter !== void 0 ? options.minFilter : LinearFilter;\\n this.depthBuffer = options.depthBuffer !== void 0 ? options.depthBuffer : true;\\n this.stencilBuffer = options.stencilBuffer !== void 0 ? options.stencilBuffer : false;\\n this.depthTexture = options.depthTexture !== void 0 ? options.depthTexture : null;\\n }\\n setTexture(texture) {\\n texture.image = {\\n width: this.width,\\n height: this.height,\\n depth: this.depth\\n };\\n this.texture = texture;\\n }\\n setSize(width, height, depth2 = 1) {\\n if (this.width !== width || this.height !== height || this.depth !== depth2) {\\n this.width = width;\\n this.height = height;\\n this.depth = depth2;\\n this.texture.image.width = width;\\n this.texture.image.height = height;\\n this.texture.image.depth = depth2;\\n this.dispose();\\n }\\n this.viewport.set(0, 0, width, height);\\n this.scissor.set(0, 0, width, height);\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n copy(source) {\\n this.width = source.width;\\n this.height = source.height;\\n this.depth = source.depth;\\n this.viewport.copy(source.viewport);\\n this.texture = source.texture.clone();\\n this.texture.image = {...this.texture.image};\\n this.depthBuffer = source.depthBuffer;\\n this.stencilBuffer = source.stencilBuffer;\\n this.depthTexture = source.depthTexture;\\n return this;\\n }\\n dispose() {\\n this.dispatchEvent({type: \\\\\\\"dispose\\\\\\\"});\\n }\\n};\\nWebGLRenderTarget.prototype.isWebGLRenderTarget = true;\\n\\n// ../../../node_modules/three/src/cameras/Camera.js\\nvar Camera = class extends Object3D {\\n constructor() {\\n super();\\n this.type = \\\\\\\"Camera\\\\\\\";\\n this.matrixWorldInverse = new Matrix4();\\n this.projectionMatrix = new Matrix4();\\n this.projectionMatrixInverse = new Matrix4();\\n }\\n copy(source, recursive) {\\n super.copy(source, recursive);\\n this.matrixWorldInverse.copy(source.matrixWorldInverse);\\n this.projectionMatrix.copy(source.projectionMatrix);\\n this.projectionMatrixInverse.copy(source.projectionMatrixInverse);\\n return this;\\n }\\n getWorldDirection(target) {\\n this.updateWorldMatrix(true, false);\\n const e = this.matrixWorld.elements;\\n return target.set(-e[8], -e[9], -e[10]).normalize();\\n }\\n updateMatrixWorld(force) {\\n super.updateMatrixWorld(force);\\n this.matrixWorldInverse.copy(this.matrixWorld).invert();\\n }\\n updateWorldMatrix(updateParents, updateChildren) {\\n super.updateWorldMatrix(updateParents, updateChildren);\\n this.matrixWorldInverse.copy(this.matrixWorld).invert();\\n }\\n clone() {\\n return new this.constructor().copy(this);\\n }\\n};\\nCamera.prototype.isCamera = true;\\n\\n// ../../../node_modules/three/src/cameras/PerspectiveCamera.js\\nvar PerspectiveCamera = class extends Camera {\\n constructor(fov2 = 50, aspect2 = 1, near = 0.1, far = 2e3) {\\n super();\\n this.type = \\\\\\\"PerspectiveCamera\\\\\\\";\\n this.fov = fov2;\\n this.zoom = 1;\\n this.near = near;\\n this.far = far;\\n this.focus = 10;\\n this.aspect = aspect2;\\n this.view = null;\\n this.filmGauge = 35;\\n this.filmOffset = 0;\\n this.updateProjectionMatrix();\\n }\\n copy(source, recursive) {\\n super.copy(source, recursive);\\n this.fov = source.fov;\\n this.zoom = source.zoom;\\n this.near = source.near;\\n this.far = source.far;\\n this.focus = source.focus;\\n this.aspect = source.aspect;\\n this.view = source.view === null ? null : Object.assign({}, source.view);\\n this.filmGauge = source.filmGauge;\\n this.filmOffset = source.filmOffset;\\n return this;\\n }\\n setFocalLength(focalLength) {\\n const vExtentSlope = 0.5 * this.getFilmHeight() / focalLength;\\n this.fov = RAD2DEG * 2 * Math.atan(vExtentSlope);\\n this.updateProjectionMatrix();\\n }\\n getFocalLength() {\\n const vExtentSlope = Math.tan(DEG2RAD * 0.5 * this.fov);\\n return 0.5 * this.getFilmHeight() / vExtentSlope;\\n }\\n getEffectiveFOV() {\\n return RAD2DEG * 2 * Math.atan(Math.tan(DEG2RAD * 0.5 * this.fov) / this.zoom);\\n }\\n getFilmWidth() {\\n return this.filmGauge * Math.min(this.aspect, 1);\\n }\\n getFilmHeight() {\\n return this.filmGauge / Math.max(this.aspect, 1);\\n }\\n setViewOffset(fullWidth, fullHeight, x, y, width, height) {\\n this.aspect = fullWidth / fullHeight;\\n if (this.view === null) {\\n this.view = {\\n enabled: true,\\n fullWidth: 1,\\n fullHeight: 1,\\n offsetX: 0,\\n offsetY: 0,\\n width: 1,\\n height: 1\\n };\\n }\\n this.view.enabled = true;\\n this.view.fullWidth = fullWidth;\\n this.view.fullHeight = fullHeight;\\n this.view.offsetX = x;\\n this.view.offsetY = y;\\n this.view.width = width;\\n this.view.height = height;\\n this.updateProjectionMatrix();\\n }\\n clearViewOffset() {\\n if (this.view !== null) {\\n this.view.enabled = false;\\n }\\n this.updateProjectionMatrix();\\n }\\n updateProjectionMatrix() {\\n const near = this.near;\\n let top = near * Math.tan(DEG2RAD * 0.5 * this.fov) / this.zoom;\\n let height = 2 * top;\\n let width = this.aspect * height;\\n let left = -0.5 * width;\\n const view = this.view;\\n if (this.view !== null && this.view.enabled) {\\n const fullWidth = view.fullWidth, fullHeight = view.fullHeight;\\n left += view.offsetX * width / fullWidth;\\n top -= view.offsetY * height / fullHeight;\\n width *= view.width / fullWidth;\\n height *= view.height / fullHeight;\\n }\\n const skew = this.filmOffset;\\n if (skew !== 0)\\n left += near * skew / this.getFilmWidth();\\n this.projectionMatrix.makePerspective(left, left + width, top, top - height, near, this.far);\\n this.projectionMatrixInverse.copy(this.projectionMatrix).invert();\\n }\\n toJSON(meta) {\\n const data = super.toJSON(meta);\\n data.object.fov = this.fov;\\n data.object.zoom = this.zoom;\\n data.object.near = this.near;\\n data.object.far = this.far;\\n data.object.focus = this.focus;\\n data.object.aspect = this.aspect;\\n if (this.view !== null)\\n data.object.view = Object.assign({}, this.view);\\n data.object.filmGauge = this.filmGauge;\\n data.object.filmOffset = this.filmOffset;\\n return data;\\n }\\n};\\nPerspectiveCamera.prototype.isPerspectiveCamera = true;\\n\\n// ../../../node_modules/three/src/cameras/CubeCamera.js\\nvar fov = 90;\\nvar aspect = 1;\\nvar CubeCamera = class extends Object3D {\\n constructor(near, far, renderTarget) {\\n super();\\n this.type = \\\\\\\"CubeCamera\\\\\\\";\\n if (renderTarget.isWebGLCubeRenderTarget !== true) {\\n console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");\\n return;\\n }\\n this.renderTarget = renderTarget;\\n const cameraPX = new PerspectiveCamera(fov, aspect, near, far);\\n cameraPX.layers = this.layers;\\n cameraPX.up.set(0, -1, 0);\\n cameraPX.lookAt(new Vector3(1, 0, 0));\\n this.add(cameraPX);\\n const cameraNX = new PerspectiveCamera(fov, aspect, near, far);\\n cameraNX.layers = this.layers;\\n cameraNX.up.set(0, -1, 0);\\n cameraNX.lookAt(new Vector3(-1, 0, 0));\\n this.add(cameraNX);\\n const cameraPY = new PerspectiveCamera(fov, aspect, near, far);\\n cameraPY.layers = this.layers;\\n cameraPY.up.set(0, 0, 1);\\n cameraPY.lookAt(new Vector3(0, 1, 0));\\n this.add(cameraPY);\\n const cameraNY = new PerspectiveCamera(fov, aspect, near, far);\\n cameraNY.layers = this.layers;\\n cameraNY.up.set(0, 0, -1);\\n cameraNY.lookAt(new Vector3(0, -1, 0));\\n this.add(cameraNY);\\n const cameraPZ = new PerspectiveCamera(fov, aspect, near, far);\\n cameraPZ.layers = this.layers;\\n cameraPZ.up.set(0, -1, 0);\\n cameraPZ.lookAt(new Vector3(0, 0, 1));\\n this.add(cameraPZ);\\n const cameraNZ = new PerspectiveCamera(fov, aspect, near, far);\\n cameraNZ.layers = this.layers;\\n cameraNZ.up.set(0, -1, 0);\\n cameraNZ.lookAt(new Vector3(0, 0, -1));\\n this.add(cameraNZ);\\n }\\n update(renderer, scene) {\\n if (this.parent === null)\\n this.updateMatrixWorld();\\n const renderTarget = this.renderTarget;\\n const [cameraPX, cameraNX, cameraPY, cameraNY, cameraPZ, cameraNZ] = this.children;\\n const currentXrEnabled = renderer.xr.enabled;\\n const currentRenderTarget = renderer.getRenderTarget();\\n renderer.xr.enabled = false;\\n const generateMipmaps = renderTarget.texture.generateMipmaps;\\n renderTarget.texture.generateMipmaps = false;\\n renderer.setRenderTarget(renderTarget, 0);\\n renderer.render(scene, cameraPX);\\n renderer.setRenderTarget(renderTarget, 1);\\n renderer.render(scene, cameraNX);\\n renderer.setRenderTarget(renderTarget, 2);\\n renderer.render(scene, cameraPY);\\n renderer.setRenderTarget(renderTarget, 3);\\n renderer.render(scene, cameraNY);\\n renderer.setRenderTarget(renderTarget, 4);\\n renderer.render(scene, cameraPZ);\\n renderTarget.texture.generateMipmaps = generateMipmaps;\\n renderer.setRenderTarget(renderTarget, 5);\\n renderer.render(scene, cameraNZ);\\n renderer.setRenderTarget(currentRenderTarget);\\n renderer.xr.enabled = currentXrEnabled;\\n }\\n};\\n\\n// ../../../node_modules/three/src/textures/CubeTexture.js\\nvar CubeTexture = class extends Texture {\\n constructor(images, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding) {\\n images = images !== void 0 ? images : [];\\n mapping = mapping !== void 0 ? mapping : CubeReflectionMapping;\\n super(images, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding);\\n this.flipY = false;\\n }\\n get images() {\\n return this.image;\\n }\\n set images(value) {\\n this.image = value;\\n }\\n};\\nCubeTexture.prototype.isCubeTexture = true;\\n\\n// ../../../node_modules/three/src/renderers/WebGLCubeRenderTarget.js\\nvar WebGLCubeRenderTarget = class extends WebGLRenderTarget {\\n constructor(size, options, dummy) {\\n if (Number.isInteger(options)) {\\n console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\");\\n options = dummy;\\n }\\n super(size, size, options);\\n options = options || {};\\n this.texture = new CubeTexture(void 0, options.mapping, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.encoding);\\n this.texture.isRenderTargetTexture = true;\\n this.texture.generateMipmaps = options.generateMipmaps !== void 0 ? options.generateMipmaps : false;\\n this.texture.minFilter = options.minFilter !== void 0 ? options.minFilter : LinearFilter;\\n this.texture._needsFlipEnvMap = false;\\n }\\n fromEquirectangularTexture(renderer, texture) {\\n this.texture.type = texture.type;\\n this.texture.format = RGBAFormat;\\n this.texture.encoding = texture.encoding;\\n this.texture.generateMipmaps = texture.generateMipmaps;\\n this.texture.minFilter = texture.minFilter;\\n this.texture.magFilter = texture.magFilter;\\n const shader = {\\n uniforms: {\\n tEquirect: {value: null}\\n },\\n vertexShader: `\\n\\n\\t\\t\\t\\tvarying vec3 vWorldDirection;\\n\\n\\t\\t\\t\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\n\\n\\t\\t\\t\\t\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\n\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\tvoid main() {\\n\\n\\t\\t\\t\\t\\tvWorldDirection = transformDirection( position, modelMatrix );\\n\\n\\t\\t\\t\\t\\t#include \\n\\t\\t\\t\\t\\t#include \\n\\n\\t\\t\\t\\t}\\n\\t\\t\\t`,\\n fragmentShader: `\\n\\n\\t\\t\\t\\tuniform sampler2D tEquirect;\\n\\n\\t\\t\\t\\tvarying vec3 vWorldDirection;\\n\\n\\t\\t\\t\\t#include \\n\\n\\t\\t\\t\\tvoid main() {\\n\\n\\t\\t\\t\\t\\tvec3 direction = normalize( vWorldDirection );\\n\\n\\t\\t\\t\\t\\tvec2 sampleUV = equirectUv( direction );\\n\\n\\t\\t\\t\\t\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\n\\n\\t\\t\\t\\t}\\n\\t\\t\\t`\\n };\\n const geometry = new BoxGeometry(5, 5, 5);\\n const material = new ShaderMaterial({\\n name: \\\\\\\"CubemapFromEquirect\\\\\\\",\\n uniforms: cloneUniforms(shader.uniforms),\\n vertexShader: shader.vertexShader,\\n fragmentShader: shader.fragmentShader,\\n side: BackSide,\\n blending: NoBlending\\n });\\n material.uniforms.tEquirect.value = texture;\\n const mesh = new Mesh(geometry, material);\\n const currentMinFilter = texture.minFilter;\\n if (texture.minFilter === LinearMipmapLinearFilter)\\n texture.minFilter = LinearFilter;\\n const camera = new CubeCamera(1, 10, this);\\n camera.update(renderer, mesh);\\n texture.minFilter = currentMinFilter;\\n mesh.geometry.dispose();\\n mesh.material.dispose();\\n return this;\\n }\\n clear(renderer, color, depth2, stencil) {\\n const currentRenderTarget = renderer.getRenderTarget();\\n for (let i = 0; i < 6; i++) {\\n renderer.setRenderTarget(this, i);\\n renderer.clear(color, depth2, stencil);\\n }\\n renderer.setRenderTarget(currentRenderTarget);\\n }\\n};\\nWebGLCubeRenderTarget.prototype.isWebGLCubeRenderTarget = true;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLCubeMaps.js\\nfunction WebGLCubeMaps(renderer) {\\n let cubemaps = new WeakMap();\\n function mapTextureMapping(texture, mapping) {\\n if (mapping === EquirectangularReflectionMapping) {\\n texture.mapping = CubeReflectionMapping;\\n } else if (mapping === EquirectangularRefractionMapping) {\\n texture.mapping = CubeRefractionMapping;\\n }\\n return texture;\\n }\\n function get(texture) {\\n if (texture && texture.isTexture && texture.isRenderTargetTexture === false) {\\n const mapping = texture.mapping;\\n if (mapping === EquirectangularReflectionMapping || mapping === EquirectangularRefractionMapping) {\\n if (cubemaps.has(texture)) {\\n const cubemap = cubemaps.get(texture).texture;\\n return mapTextureMapping(cubemap, texture.mapping);\\n } else {\\n const image = texture.image;\\n if (image && image.height > 0) {\\n const currentRenderTarget = renderer.getRenderTarget();\\n const renderTarget = new WebGLCubeRenderTarget(image.height / 2);\\n renderTarget.fromEquirectangularTexture(renderer, texture);\\n cubemaps.set(texture, renderTarget);\\n renderer.setRenderTarget(currentRenderTarget);\\n texture.addEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n return mapTextureMapping(renderTarget.texture, texture.mapping);\\n } else {\\n return null;\\n }\\n }\\n }\\n }\\n return texture;\\n }\\n function onTextureDispose(event) {\\n const texture = event.target;\\n texture.removeEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n const cubemap = cubemaps.get(texture);\\n if (cubemap !== void 0) {\\n cubemaps.delete(texture);\\n cubemap.dispose();\\n }\\n }\\n function dispose() {\\n cubemaps = new WeakMap();\\n }\\n return {\\n get,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/cameras/OrthographicCamera.js\\nvar OrthographicCamera = class extends Camera {\\n constructor(left = -1, right = 1, top = 1, bottom = -1, near = 0.1, far = 2e3) {\\n super();\\n this.type = \\\\\\\"OrthographicCamera\\\\\\\";\\n this.zoom = 1;\\n this.view = null;\\n this.left = left;\\n this.right = right;\\n this.top = top;\\n this.bottom = bottom;\\n this.near = near;\\n this.far = far;\\n this.updateProjectionMatrix();\\n }\\n copy(source, recursive) {\\n super.copy(source, recursive);\\n this.left = source.left;\\n this.right = source.right;\\n this.top = source.top;\\n this.bottom = source.bottom;\\n this.near = source.near;\\n this.far = source.far;\\n this.zoom = source.zoom;\\n this.view = source.view === null ? null : Object.assign({}, source.view);\\n return this;\\n }\\n setViewOffset(fullWidth, fullHeight, x, y, width, height) {\\n if (this.view === null) {\\n this.view = {\\n enabled: true,\\n fullWidth: 1,\\n fullHeight: 1,\\n offsetX: 0,\\n offsetY: 0,\\n width: 1,\\n height: 1\\n };\\n }\\n this.view.enabled = true;\\n this.view.fullWidth = fullWidth;\\n this.view.fullHeight = fullHeight;\\n this.view.offsetX = x;\\n this.view.offsetY = y;\\n this.view.width = width;\\n this.view.height = height;\\n this.updateProjectionMatrix();\\n }\\n clearViewOffset() {\\n if (this.view !== null) {\\n this.view.enabled = false;\\n }\\n this.updateProjectionMatrix();\\n }\\n updateProjectionMatrix() {\\n const dx = (this.right - this.left) / (2 * this.zoom);\\n const dy = (this.top - this.bottom) / (2 * this.zoom);\\n const cx = (this.right + this.left) / 2;\\n const cy = (this.top + this.bottom) / 2;\\n let left = cx - dx;\\n let right = cx + dx;\\n let top = cy + dy;\\n let bottom = cy - dy;\\n if (this.view !== null && this.view.enabled) {\\n const scaleW = (this.right - this.left) / this.view.fullWidth / this.zoom;\\n const scaleH = (this.top - this.bottom) / this.view.fullHeight / this.zoom;\\n left += scaleW * this.view.offsetX;\\n right = left + scaleW * this.view.width;\\n top -= scaleH * this.view.offsetY;\\n bottom = top - scaleH * this.view.height;\\n }\\n this.projectionMatrix.makeOrthographic(left, right, top, bottom, this.near, this.far);\\n this.projectionMatrixInverse.copy(this.projectionMatrix).invert();\\n }\\n toJSON(meta) {\\n const data = super.toJSON(meta);\\n data.object.zoom = this.zoom;\\n data.object.left = this.left;\\n data.object.right = this.right;\\n data.object.top = this.top;\\n data.object.bottom = this.bottom;\\n data.object.near = this.near;\\n data.object.far = this.far;\\n if (this.view !== null)\\n data.object.view = Object.assign({}, this.view);\\n return data;\\n }\\n};\\nOrthographicCamera.prototype.isOrthographicCamera = true;\\n\\n// ../../../node_modules/three/src/materials/RawShaderMaterial.js\\nvar RawShaderMaterial = class extends ShaderMaterial {\\n constructor(parameters) {\\n super(parameters);\\n this.type = \\\\\\\"RawShaderMaterial\\\\\\\";\\n }\\n};\\nRawShaderMaterial.prototype.isRawShaderMaterial = true;\\n\\n// ../../../node_modules/three/src/extras/PMREMGenerator.js\\nvar LOD_MIN = 4;\\nvar LOD_MAX = 8;\\nvar SIZE_MAX = Math.pow(2, LOD_MAX);\\nvar EXTRA_LOD_SIGMA = [0.125, 0.215, 0.35, 0.446, 0.526, 0.582];\\nvar TOTAL_LODS = LOD_MAX - LOD_MIN + 1 + EXTRA_LOD_SIGMA.length;\\nvar MAX_SAMPLES = 20;\\nvar ENCODINGS = {\\n [LinearEncoding]: 0,\\n [sRGBEncoding]: 1,\\n [RGBEEncoding]: 2,\\n [RGBM7Encoding]: 3,\\n [RGBM16Encoding]: 4,\\n [RGBDEncoding]: 5,\\n [GammaEncoding]: 6\\n};\\nvar _flatCamera = /* @__PURE__ */ new OrthographicCamera();\\nvar {_lodPlanes, _sizeLods, _sigmas} = /* @__PURE__ */ _createPlanes();\\nvar _clearColor = /* @__PURE__ */ new Color();\\nvar _oldTarget = null;\\nvar PHI = (1 + Math.sqrt(5)) / 2;\\nvar INV_PHI = 1 / PHI;\\nvar _axisDirections = [\\n /* @__PURE__ */ new Vector3(1, 1, 1),\\n /* @__PURE__ */ new Vector3(-1, 1, 1),\\n /* @__PURE__ */ new Vector3(1, 1, -1),\\n /* @__PURE__ */ new Vector3(-1, 1, -1),\\n /* @__PURE__ */ new Vector3(0, PHI, INV_PHI),\\n /* @__PURE__ */ new Vector3(0, PHI, -INV_PHI),\\n /* @__PURE__ */ new Vector3(INV_PHI, 0, PHI),\\n /* @__PURE__ */ new Vector3(-INV_PHI, 0, PHI),\\n /* @__PURE__ */ new Vector3(PHI, INV_PHI, 0),\\n /* @__PURE__ */ new Vector3(-PHI, INV_PHI, 0)\\n];\\nvar PMREMGenerator = class {\\n constructor(renderer) {\\n this._renderer = renderer;\\n this._pingPongRenderTarget = null;\\n this._blurMaterial = _getBlurShader(MAX_SAMPLES);\\n this._equirectShader = null;\\n this._cubemapShader = null;\\n this._compileMaterial(this._blurMaterial);\\n }\\n fromScene(scene, sigma = 0, near = 0.1, far = 100) {\\n _oldTarget = this._renderer.getRenderTarget();\\n const cubeUVRenderTarget = this._allocateTargets();\\n this._sceneToCubeUV(scene, near, far, cubeUVRenderTarget);\\n if (sigma > 0) {\\n this._blur(cubeUVRenderTarget, 0, 0, sigma);\\n }\\n this._applyPMREM(cubeUVRenderTarget);\\n this._cleanup(cubeUVRenderTarget);\\n return cubeUVRenderTarget;\\n }\\n fromEquirectangular(equirectangular) {\\n return this._fromTexture(equirectangular);\\n }\\n fromCubemap(cubemap) {\\n return this._fromTexture(cubemap);\\n }\\n compileCubemapShader() {\\n if (this._cubemapShader === null) {\\n this._cubemapShader = _getCubemapShader();\\n this._compileMaterial(this._cubemapShader);\\n }\\n }\\n compileEquirectangularShader() {\\n if (this._equirectShader === null) {\\n this._equirectShader = _getEquirectShader();\\n this._compileMaterial(this._equirectShader);\\n }\\n }\\n dispose() {\\n this._blurMaterial.dispose();\\n if (this._cubemapShader !== null)\\n this._cubemapShader.dispose();\\n if (this._equirectShader !== null)\\n this._equirectShader.dispose();\\n for (let i = 0; i < _lodPlanes.length; i++) {\\n _lodPlanes[i].dispose();\\n }\\n }\\n _cleanup(outputTarget) {\\n this._pingPongRenderTarget.dispose();\\n this._renderer.setRenderTarget(_oldTarget);\\n outputTarget.scissorTest = false;\\n _setViewport(outputTarget, 0, 0, outputTarget.width, outputTarget.height);\\n }\\n _fromTexture(texture) {\\n _oldTarget = this._renderer.getRenderTarget();\\n const cubeUVRenderTarget = this._allocateTargets(texture);\\n this._textureToCubeUV(texture, cubeUVRenderTarget);\\n this._applyPMREM(cubeUVRenderTarget);\\n this._cleanup(cubeUVRenderTarget);\\n return cubeUVRenderTarget;\\n }\\n _allocateTargets(texture) {\\n const params = {\\n magFilter: NearestFilter,\\n minFilter: NearestFilter,\\n generateMipmaps: false,\\n type: UnsignedByteType,\\n format: RGBEFormat,\\n encoding: _isLDR(texture) ? texture.encoding : RGBEEncoding,\\n depthBuffer: false\\n };\\n const cubeUVRenderTarget = _createRenderTarget(params);\\n cubeUVRenderTarget.depthBuffer = texture ? false : true;\\n this._pingPongRenderTarget = _createRenderTarget(params);\\n return cubeUVRenderTarget;\\n }\\n _compileMaterial(material) {\\n const tmpMesh = new Mesh(_lodPlanes[0], material);\\n this._renderer.compile(tmpMesh, _flatCamera);\\n }\\n _sceneToCubeUV(scene, near, far, cubeUVRenderTarget) {\\n const fov2 = 90;\\n const aspect2 = 1;\\n const cubeCamera = new PerspectiveCamera(fov2, aspect2, near, far);\\n const upSign = [1, -1, 1, 1, 1, 1];\\n const forwardSign = [1, 1, 1, -1, -1, -1];\\n const renderer = this._renderer;\\n const originalAutoClear = renderer.autoClear;\\n const outputEncoding = renderer.outputEncoding;\\n const toneMapping = renderer.toneMapping;\\n renderer.getClearColor(_clearColor);\\n renderer.toneMapping = NoToneMapping;\\n renderer.outputEncoding = LinearEncoding;\\n renderer.autoClear = false;\\n const backgroundMaterial = new MeshBasicMaterial({\\n name: \\\\\\\"PMREM.Background\\\\\\\",\\n side: BackSide,\\n depthWrite: false,\\n depthTest: false\\n });\\n const backgroundBox = new Mesh(new BoxGeometry(), backgroundMaterial);\\n let useSolidColor = false;\\n const background2 = scene.background;\\n if (background2) {\\n if (background2.isColor) {\\n backgroundMaterial.color.copy(background2);\\n scene.background = null;\\n useSolidColor = true;\\n }\\n } else {\\n backgroundMaterial.color.copy(_clearColor);\\n useSolidColor = true;\\n }\\n for (let i = 0; i < 6; i++) {\\n const col = i % 3;\\n if (col == 0) {\\n cubeCamera.up.set(0, upSign[i], 0);\\n cubeCamera.lookAt(forwardSign[i], 0, 0);\\n } else if (col == 1) {\\n cubeCamera.up.set(0, 0, upSign[i]);\\n cubeCamera.lookAt(0, forwardSign[i], 0);\\n } else {\\n cubeCamera.up.set(0, upSign[i], 0);\\n cubeCamera.lookAt(0, 0, forwardSign[i]);\\n }\\n _setViewport(cubeUVRenderTarget, col * SIZE_MAX, i > 2 ? SIZE_MAX : 0, SIZE_MAX, SIZE_MAX);\\n renderer.setRenderTarget(cubeUVRenderTarget);\\n if (useSolidColor) {\\n renderer.render(backgroundBox, cubeCamera);\\n }\\n renderer.render(scene, cubeCamera);\\n }\\n backgroundBox.geometry.dispose();\\n backgroundBox.material.dispose();\\n renderer.toneMapping = toneMapping;\\n renderer.outputEncoding = outputEncoding;\\n renderer.autoClear = originalAutoClear;\\n scene.background = background2;\\n }\\n _setEncoding(uniform, texture) {\\n if (this._renderer.capabilities.isWebGL2 === true && texture.format === RGBAFormat && texture.type === UnsignedByteType && texture.encoding === sRGBEncoding) {\\n uniform.value = ENCODINGS[LinearEncoding];\\n } else {\\n uniform.value = ENCODINGS[texture.encoding];\\n }\\n }\\n _textureToCubeUV(texture, cubeUVRenderTarget) {\\n const renderer = this._renderer;\\n if (texture.isCubeTexture) {\\n if (this._cubemapShader == null) {\\n this._cubemapShader = _getCubemapShader();\\n }\\n } else {\\n if (this._equirectShader == null) {\\n this._equirectShader = _getEquirectShader();\\n }\\n }\\n const material = texture.isCubeTexture ? this._cubemapShader : this._equirectShader;\\n const mesh = new Mesh(_lodPlanes[0], material);\\n const uniforms = material.uniforms;\\n uniforms[\\\\\\\"envMap\\\\\\\"].value = texture;\\n if (!texture.isCubeTexture) {\\n uniforms[\\\\\\\"texelSize\\\\\\\"].value.set(1 / texture.image.width, 1 / texture.image.height);\\n }\\n this._setEncoding(uniforms[\\\\\\\"inputEncoding\\\\\\\"], texture);\\n this._setEncoding(uniforms[\\\\\\\"outputEncoding\\\\\\\"], cubeUVRenderTarget.texture);\\n _setViewport(cubeUVRenderTarget, 0, 0, 3 * SIZE_MAX, 2 * SIZE_MAX);\\n renderer.setRenderTarget(cubeUVRenderTarget);\\n renderer.render(mesh, _flatCamera);\\n }\\n _applyPMREM(cubeUVRenderTarget) {\\n const renderer = this._renderer;\\n const autoClear = renderer.autoClear;\\n renderer.autoClear = false;\\n for (let i = 1; i < TOTAL_LODS; i++) {\\n const sigma = Math.sqrt(_sigmas[i] * _sigmas[i] - _sigmas[i - 1] * _sigmas[i - 1]);\\n const poleAxis = _axisDirections[(i - 1) % _axisDirections.length];\\n this._blur(cubeUVRenderTarget, i - 1, i, sigma, poleAxis);\\n }\\n renderer.autoClear = autoClear;\\n }\\n _blur(cubeUVRenderTarget, lodIn, lodOut, sigma, poleAxis) {\\n const pingPongRenderTarget = this._pingPongRenderTarget;\\n this._halfBlur(cubeUVRenderTarget, pingPongRenderTarget, lodIn, lodOut, sigma, \\\\\\\"latitudinal\\\\\\\", poleAxis);\\n this._halfBlur(pingPongRenderTarget, cubeUVRenderTarget, lodOut, lodOut, sigma, \\\\\\\"longitudinal\\\\\\\", poleAxis);\\n }\\n _halfBlur(targetIn, targetOut, lodIn, lodOut, sigmaRadians, direction, poleAxis) {\\n const renderer = this._renderer;\\n const blurMaterial = this._blurMaterial;\\n if (direction !== \\\\\\\"latitudinal\\\\\\\" && direction !== \\\\\\\"longitudinal\\\\\\\") {\\n console.error(\\\\\\\"blur direction must be either latitudinal or longitudinal!\\\\\\\");\\n }\\n const STANDARD_DEVIATIONS = 3;\\n const blurMesh = new Mesh(_lodPlanes[lodOut], blurMaterial);\\n const blurUniforms = blurMaterial.uniforms;\\n const pixels = _sizeLods[lodIn] - 1;\\n const radiansPerPixel = isFinite(sigmaRadians) ? Math.PI / (2 * pixels) : 2 * Math.PI / (2 * MAX_SAMPLES - 1);\\n const sigmaPixels = sigmaRadians / radiansPerPixel;\\n const samples = isFinite(sigmaRadians) ? 1 + Math.floor(STANDARD_DEVIATIONS * sigmaPixels) : MAX_SAMPLES;\\n if (samples > MAX_SAMPLES) {\\n console.warn(`sigmaRadians, ${sigmaRadians}, is too large and will clip, as it requested ${samples} samples when the maximum is set to ${MAX_SAMPLES}`);\\n }\\n const weights = [];\\n let sum = 0;\\n for (let i = 0; i < MAX_SAMPLES; ++i) {\\n const x2 = i / sigmaPixels;\\n const weight = Math.exp(-x2 * x2 / 2);\\n weights.push(weight);\\n if (i == 0) {\\n sum += weight;\\n } else if (i < samples) {\\n sum += 2 * weight;\\n }\\n }\\n for (let i = 0; i < weights.length; i++) {\\n weights[i] = weights[i] / sum;\\n }\\n blurUniforms[\\\\\\\"envMap\\\\\\\"].value = targetIn.texture;\\n blurUniforms[\\\\\\\"samples\\\\\\\"].value = samples;\\n blurUniforms[\\\\\\\"weights\\\\\\\"].value = weights;\\n blurUniforms[\\\\\\\"latitudinal\\\\\\\"].value = direction === \\\\\\\"latitudinal\\\\\\\";\\n if (poleAxis) {\\n blurUniforms[\\\\\\\"poleAxis\\\\\\\"].value = poleAxis;\\n }\\n blurUniforms[\\\\\\\"dTheta\\\\\\\"].value = radiansPerPixel;\\n blurUniforms[\\\\\\\"mipInt\\\\\\\"].value = LOD_MAX - lodIn;\\n this._setEncoding(blurUniforms[\\\\\\\"inputEncoding\\\\\\\"], targetIn.texture);\\n this._setEncoding(blurUniforms[\\\\\\\"outputEncoding\\\\\\\"], targetIn.texture);\\n const outputSize = _sizeLods[lodOut];\\n const x = 3 * Math.max(0, SIZE_MAX - 2 * outputSize);\\n const y = (lodOut === 0 ? 0 : 2 * SIZE_MAX) + 2 * outputSize * (lodOut > LOD_MAX - LOD_MIN ? lodOut - LOD_MAX + LOD_MIN : 0);\\n _setViewport(targetOut, x, y, 3 * outputSize, 2 * outputSize);\\n renderer.setRenderTarget(targetOut);\\n renderer.render(blurMesh, _flatCamera);\\n }\\n};\\nfunction _isLDR(texture) {\\n if (texture === void 0 || texture.type !== UnsignedByteType)\\n return false;\\n return texture.encoding === LinearEncoding || texture.encoding === sRGBEncoding || texture.encoding === GammaEncoding;\\n}\\nfunction _createPlanes() {\\n const _lodPlanes2 = [];\\n const _sizeLods2 = [];\\n const _sigmas2 = [];\\n let lod = LOD_MAX;\\n for (let i = 0; i < TOTAL_LODS; i++) {\\n const sizeLod = Math.pow(2, lod);\\n _sizeLods2.push(sizeLod);\\n let sigma = 1 / sizeLod;\\n if (i > LOD_MAX - LOD_MIN) {\\n sigma = EXTRA_LOD_SIGMA[i - LOD_MAX + LOD_MIN - 1];\\n } else if (i == 0) {\\n sigma = 0;\\n }\\n _sigmas2.push(sigma);\\n const texelSize = 1 / (sizeLod - 1);\\n const min = -texelSize / 2;\\n const max = 1 + texelSize / 2;\\n const uv1 = [min, min, max, min, max, max, min, min, max, max, min, max];\\n const cubeFaces = 6;\\n const vertices = 6;\\n const positionSize = 3;\\n const uvSize = 2;\\n const faceIndexSize = 1;\\n const position = new Float32Array(positionSize * vertices * cubeFaces);\\n const uv = new Float32Array(uvSize * vertices * cubeFaces);\\n const faceIndex = new Float32Array(faceIndexSize * vertices * cubeFaces);\\n for (let face = 0; face < cubeFaces; face++) {\\n const x = face % 3 * 2 / 3 - 1;\\n const y = face > 2 ? 0 : -1;\\n const coordinates = [\\n x,\\n y,\\n 0,\\n x + 2 / 3,\\n y,\\n 0,\\n x + 2 / 3,\\n y + 1,\\n 0,\\n x,\\n y,\\n 0,\\n x + 2 / 3,\\n y + 1,\\n 0,\\n x,\\n y + 1,\\n 0\\n ];\\n position.set(coordinates, positionSize * vertices * face);\\n uv.set(uv1, uvSize * vertices * face);\\n const fill = [face, face, face, face, face, face];\\n faceIndex.set(fill, faceIndexSize * vertices * face);\\n }\\n const planes = new BufferGeometry();\\n planes.setAttribute(\\\\\\\"position\\\\\\\", new BufferAttribute(position, positionSize));\\n planes.setAttribute(\\\\\\\"uv\\\\\\\", new BufferAttribute(uv, uvSize));\\n planes.setAttribute(\\\\\\\"faceIndex\\\\\\\", new BufferAttribute(faceIndex, faceIndexSize));\\n _lodPlanes2.push(planes);\\n if (lod > LOD_MIN) {\\n lod--;\\n }\\n }\\n return {_lodPlanes: _lodPlanes2, _sizeLods: _sizeLods2, _sigmas: _sigmas2};\\n}\\nfunction _createRenderTarget(params) {\\n const cubeUVRenderTarget = new WebGLRenderTarget(3 * SIZE_MAX, 3 * SIZE_MAX, params);\\n cubeUVRenderTarget.texture.mapping = CubeUVReflectionMapping;\\n cubeUVRenderTarget.texture.name = \\\\\\\"PMREM.cubeUv\\\\\\\";\\n cubeUVRenderTarget.scissorTest = true;\\n return cubeUVRenderTarget;\\n}\\nfunction _setViewport(target, x, y, width, height) {\\n target.viewport.set(x, y, width, height);\\n target.scissor.set(x, y, width, height);\\n}\\nfunction _getBlurShader(maxSamples) {\\n const weights = new Float32Array(maxSamples);\\n const poleAxis = new Vector3(0, 1, 0);\\n const shaderMaterial = new RawShaderMaterial({\\n name: \\\\\\\"SphericalGaussianBlur\\\\\\\",\\n defines: {n: maxSamples},\\n uniforms: {\\n envMap: {value: null},\\n samples: {value: 1},\\n weights: {value: weights},\\n latitudinal: {value: false},\\n dTheta: {value: 0},\\n mipInt: {value: 0},\\n poleAxis: {value: poleAxis},\\n inputEncoding: {value: ENCODINGS[LinearEncoding]},\\n outputEncoding: {value: ENCODINGS[LinearEncoding]}\\n },\\n vertexShader: _getCommonVertexShader(),\\n fragmentShader: `\\n\\n\\t\\t\\tprecision mediump float;\\n\\t\\t\\tprecision mediump int;\\n\\n\\t\\t\\tvarying vec3 vOutputDirection;\\n\\n\\t\\t\\tuniform sampler2D envMap;\\n\\t\\t\\tuniform int samples;\\n\\t\\t\\tuniform float weights[ n ];\\n\\t\\t\\tuniform bool latitudinal;\\n\\t\\t\\tuniform float dTheta;\\n\\t\\t\\tuniform float mipInt;\\n\\t\\t\\tuniform vec3 poleAxis;\\n\\n\\t\\t\\t${_getEncodings()}\\n\\n\\t\\t\\t#define ENVMAP_TYPE_CUBE_UV\\n\\t\\t\\t#include \\n\\n\\t\\t\\tvec3 getSample( float theta, vec3 axis ) {\\n\\n\\t\\t\\t\\tfloat cosTheta = cos( theta );\\n\\t\\t\\t\\t// Rodrigues' axis-angle rotation\\n\\t\\t\\t\\tvec3 sampleDirection = vOutputDirection * cosTheta\\n\\t\\t\\t\\t\\t+ cross( axis, vOutputDirection ) * sin( theta )\\n\\t\\t\\t\\t\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\n\\n\\t\\t\\t\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\n\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main() {\\n\\n\\t\\t\\t\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\n\\n\\t\\t\\t\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\n\\n\\t\\t\\t\\t\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\n\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\taxis = normalize( axis );\\n\\n\\t\\t\\t\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\n\\t\\t\\t\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\n\\n\\t\\t\\t\\tfor ( int i = 1; i < n; i++ ) {\\n\\n\\t\\t\\t\\t\\tif ( i >= samples ) {\\n\\n\\t\\t\\t\\t\\t\\tbreak;\\n\\n\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\tfloat theta = dTheta * float( i );\\n\\t\\t\\t\\t\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\n\\t\\t\\t\\t\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\n\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\n\\n\\t\\t\\t}\\n\\t\\t`,\\n blending: NoBlending,\\n depthTest: false,\\n depthWrite: false\\n });\\n return shaderMaterial;\\n}\\nfunction _getEquirectShader() {\\n const texelSize = new Vector2(1, 1);\\n const shaderMaterial = new RawShaderMaterial({\\n name: \\\\\\\"EquirectangularToCubeUV\\\\\\\",\\n uniforms: {\\n envMap: {value: null},\\n texelSize: {value: texelSize},\\n inputEncoding: {value: ENCODINGS[LinearEncoding]},\\n outputEncoding: {value: ENCODINGS[LinearEncoding]}\\n },\\n vertexShader: _getCommonVertexShader(),\\n fragmentShader: `\\n\\n\\t\\t\\tprecision mediump float;\\n\\t\\t\\tprecision mediump int;\\n\\n\\t\\t\\tvarying vec3 vOutputDirection;\\n\\n\\t\\t\\tuniform sampler2D envMap;\\n\\t\\t\\tuniform vec2 texelSize;\\n\\n\\t\\t\\t${_getEncodings()}\\n\\n\\t\\t\\t#include \\n\\n\\t\\t\\tvoid main() {\\n\\n\\t\\t\\t\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\n\\n\\t\\t\\t\\tvec3 outputDirection = normalize( vOutputDirection );\\n\\t\\t\\t\\tvec2 uv = equirectUv( outputDirection );\\n\\n\\t\\t\\t\\tvec2 f = fract( uv / texelSize - 0.5 );\\n\\t\\t\\t\\tuv -= f * texelSize;\\n\\t\\t\\t\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\n\\t\\t\\t\\tuv.x += texelSize.x;\\n\\t\\t\\t\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\n\\t\\t\\t\\tuv.y += texelSize.y;\\n\\t\\t\\t\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\n\\t\\t\\t\\tuv.x -= texelSize.x;\\n\\t\\t\\t\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\n\\n\\t\\t\\t\\tvec3 tm = mix( tl, tr, f.x );\\n\\t\\t\\t\\tvec3 bm = mix( bl, br, f.x );\\n\\t\\t\\t\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\n\\n\\t\\t\\t\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\n\\n\\t\\t\\t}\\n\\t\\t`,\\n blending: NoBlending,\\n depthTest: false,\\n depthWrite: false\\n });\\n return shaderMaterial;\\n}\\nfunction _getCubemapShader() {\\n const shaderMaterial = new RawShaderMaterial({\\n name: \\\\\\\"CubemapToCubeUV\\\\\\\",\\n uniforms: {\\n envMap: {value: null},\\n inputEncoding: {value: ENCODINGS[LinearEncoding]},\\n outputEncoding: {value: ENCODINGS[LinearEncoding]}\\n },\\n vertexShader: _getCommonVertexShader(),\\n fragmentShader: `\\n\\n\\t\\t\\tprecision mediump float;\\n\\t\\t\\tprecision mediump int;\\n\\n\\t\\t\\tvarying vec3 vOutputDirection;\\n\\n\\t\\t\\tuniform samplerCube envMap;\\n\\n\\t\\t\\t${_getEncodings()}\\n\\n\\t\\t\\tvoid main() {\\n\\n\\t\\t\\t\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\n\\t\\t\\t\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\n\\t\\t\\t\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\n\\n\\t\\t\\t}\\n\\t\\t`,\\n blending: NoBlending,\\n depthTest: false,\\n depthWrite: false\\n });\\n return shaderMaterial;\\n}\\nfunction _getCommonVertexShader() {\\n return `\\n\\n\\t\\tprecision mediump float;\\n\\t\\tprecision mediump int;\\n\\n\\t\\tattribute vec3 position;\\n\\t\\tattribute vec2 uv;\\n\\t\\tattribute float faceIndex;\\n\\n\\t\\tvarying vec3 vOutputDirection;\\n\\n\\t\\t// RH coordinate system; PMREM face-indexing convention\\n\\t\\tvec3 getDirection( vec2 uv, float face ) {\\n\\n\\t\\t\\tuv = 2.0 * uv - 1.0;\\n\\n\\t\\t\\tvec3 direction = vec3( uv, 1.0 );\\n\\n\\t\\t\\tif ( face == 0.0 ) {\\n\\n\\t\\t\\t\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\n\\n\\t\\t\\t} else if ( face == 1.0 ) {\\n\\n\\t\\t\\t\\tdirection = direction.xzy;\\n\\t\\t\\t\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\n\\n\\t\\t\\t} else if ( face == 2.0 ) {\\n\\n\\t\\t\\t\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\n\\n\\t\\t\\t} else if ( face == 3.0 ) {\\n\\n\\t\\t\\t\\tdirection = direction.zyx;\\n\\t\\t\\t\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\n\\n\\t\\t\\t} else if ( face == 4.0 ) {\\n\\n\\t\\t\\t\\tdirection = direction.xzy;\\n\\t\\t\\t\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\n\\n\\t\\t\\t} else if ( face == 5.0 ) {\\n\\n\\t\\t\\t\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\n\\n\\t\\t\\t}\\n\\n\\t\\t\\treturn direction;\\n\\n\\t\\t}\\n\\n\\t\\tvoid main() {\\n\\n\\t\\t\\tvOutputDirection = getDirection( uv, faceIndex );\\n\\t\\t\\tgl_Position = vec4( position, 1.0 );\\n\\n\\t\\t}\\n\\t`;\\n}\\nfunction _getEncodings() {\\n return `\\n\\n\\t\\tuniform int inputEncoding;\\n\\t\\tuniform int outputEncoding;\\n\\n\\t\\t#include \\n\\n\\t\\tvec4 inputTexelToLinear( vec4 value ) {\\n\\n\\t\\t\\tif ( inputEncoding == 0 ) {\\n\\n\\t\\t\\t\\treturn value;\\n\\n\\t\\t\\t} else if ( inputEncoding == 1 ) {\\n\\n\\t\\t\\t\\treturn sRGBToLinear( value );\\n\\n\\t\\t\\t} else if ( inputEncoding == 2 ) {\\n\\n\\t\\t\\t\\treturn RGBEToLinear( value );\\n\\n\\t\\t\\t} else if ( inputEncoding == 3 ) {\\n\\n\\t\\t\\t\\treturn RGBMToLinear( value, 7.0 );\\n\\n\\t\\t\\t} else if ( inputEncoding == 4 ) {\\n\\n\\t\\t\\t\\treturn RGBMToLinear( value, 16.0 );\\n\\n\\t\\t\\t} else if ( inputEncoding == 5 ) {\\n\\n\\t\\t\\t\\treturn RGBDToLinear( value, 256.0 );\\n\\n\\t\\t\\t} else {\\n\\n\\t\\t\\t\\treturn GammaToLinear( value, 2.2 );\\n\\n\\t\\t\\t}\\n\\n\\t\\t}\\n\\n\\t\\tvec4 linearToOutputTexel( vec4 value ) {\\n\\n\\t\\t\\tif ( outputEncoding == 0 ) {\\n\\n\\t\\t\\t\\treturn value;\\n\\n\\t\\t\\t} else if ( outputEncoding == 1 ) {\\n\\n\\t\\t\\t\\treturn LinearTosRGB( value );\\n\\n\\t\\t\\t} else if ( outputEncoding == 2 ) {\\n\\n\\t\\t\\t\\treturn LinearToRGBE( value );\\n\\n\\t\\t\\t} else if ( outputEncoding == 3 ) {\\n\\n\\t\\t\\t\\treturn LinearToRGBM( value, 7.0 );\\n\\n\\t\\t\\t} else if ( outputEncoding == 4 ) {\\n\\n\\t\\t\\t\\treturn LinearToRGBM( value, 16.0 );\\n\\n\\t\\t\\t} else if ( outputEncoding == 5 ) {\\n\\n\\t\\t\\t\\treturn LinearToRGBD( value, 256.0 );\\n\\n\\t\\t\\t} else {\\n\\n\\t\\t\\t\\treturn LinearToGamma( value, 2.2 );\\n\\n\\t\\t\\t}\\n\\n\\t\\t}\\n\\n\\t\\tvec4 envMapTexelToLinear( vec4 color ) {\\n\\n\\t\\t\\treturn inputTexelToLinear( color );\\n\\n\\t\\t}\\n\\t`;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLCubeUVMaps.js\\nfunction WebGLCubeUVMaps(renderer) {\\n let cubeUVmaps = new WeakMap();\\n let pmremGenerator = null;\\n function get(texture) {\\n if (texture && texture.isTexture && texture.isRenderTargetTexture === false) {\\n const mapping = texture.mapping;\\n const isEquirectMap = mapping === EquirectangularReflectionMapping || mapping === EquirectangularRefractionMapping;\\n const isCubeMap = mapping === CubeReflectionMapping || mapping === CubeRefractionMapping;\\n if (isEquirectMap || isCubeMap) {\\n if (cubeUVmaps.has(texture)) {\\n return cubeUVmaps.get(texture).texture;\\n } else {\\n const image = texture.image;\\n if (isEquirectMap && image && image.height > 0 || isCubeMap && image && isCubeTextureComplete(image)) {\\n const currentRenderTarget = renderer.getRenderTarget();\\n if (pmremGenerator === null)\\n pmremGenerator = new PMREMGenerator(renderer);\\n const renderTarget = isEquirectMap ? pmremGenerator.fromEquirectangular(texture) : pmremGenerator.fromCubemap(texture);\\n cubeUVmaps.set(texture, renderTarget);\\n renderer.setRenderTarget(currentRenderTarget);\\n texture.addEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n return renderTarget.texture;\\n } else {\\n return null;\\n }\\n }\\n }\\n }\\n return texture;\\n }\\n function isCubeTextureComplete(image) {\\n let count = 0;\\n const length = 6;\\n for (let i = 0; i < length; i++) {\\n if (image[i] !== void 0)\\n count++;\\n }\\n return count === length;\\n }\\n function onTextureDispose(event) {\\n const texture = event.target;\\n texture.removeEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n const cubemapUV = cubeUVmaps.get(texture);\\n if (cubemapUV !== void 0) {\\n cubeUVmaps.delete(texture);\\n cubemapUV.dispose();\\n }\\n }\\n function dispose() {\\n cubeUVmaps = new WeakMap();\\n if (pmremGenerator !== null) {\\n pmremGenerator.dispose();\\n pmremGenerator = null;\\n }\\n }\\n return {\\n get,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLExtensions.js\\nfunction WebGLExtensions(gl) {\\n const extensions = {};\\n function getExtension(name) {\\n if (extensions[name] !== void 0) {\\n return extensions[name];\\n }\\n let extension;\\n switch (name) {\\n case \\\\\\\"WEBGL_depth_texture\\\\\\\":\\n extension = gl.getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\") || gl.getExtension(\\\\\\\"MOZ_WEBGL_depth_texture\\\\\\\") || gl.getExtension(\\\\\\\"WEBKIT_WEBGL_depth_texture\\\\\\\");\\n break;\\n case \\\\\\\"EXT_texture_filter_anisotropic\\\\\\\":\\n extension = gl.getExtension(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\") || gl.getExtension(\\\\\\\"MOZ_EXT_texture_filter_anisotropic\\\\\\\") || gl.getExtension(\\\\\\\"WEBKIT_EXT_texture_filter_anisotropic\\\\\\\");\\n break;\\n case \\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\":\\n extension = gl.getExtension(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\") || gl.getExtension(\\\\\\\"MOZ_WEBGL_compressed_texture_s3tc\\\\\\\") || gl.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_s3tc\\\\\\\");\\n break;\\n case \\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\":\\n extension = gl.getExtension(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\") || gl.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_pvrtc\\\\\\\");\\n break;\\n default:\\n extension = gl.getExtension(name);\\n }\\n extensions[name] = extension;\\n return extension;\\n }\\n return {\\n has: function(name) {\\n return getExtension(name) !== null;\\n },\\n init: function(capabilities) {\\n if (capabilities.isWebGL2) {\\n getExtension(\\\\\\\"EXT_color_buffer_float\\\\\\\");\\n } else {\\n getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\");\\n getExtension(\\\\\\\"OES_texture_float\\\\\\\");\\n getExtension(\\\\\\\"OES_texture_half_float\\\\\\\");\\n getExtension(\\\\\\\"OES_texture_half_float_linear\\\\\\\");\\n getExtension(\\\\\\\"OES_standard_derivatives\\\\\\\");\\n getExtension(\\\\\\\"OES_element_index_uint\\\\\\\");\\n getExtension(\\\\\\\"OES_vertex_array_object\\\\\\\");\\n getExtension(\\\\\\\"ANGLE_instanced_arrays\\\\\\\");\\n }\\n getExtension(\\\\\\\"OES_texture_float_linear\\\\\\\");\\n getExtension(\\\\\\\"EXT_color_buffer_half_float\\\\\\\");\\n },\\n get: function(name) {\\n const extension = getExtension(name);\\n if (extension === null) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: \\\\\\\" + name + \\\\\\\" extension not supported.\\\\\\\");\\n }\\n return extension;\\n }\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLGeometries.js\\nfunction WebGLGeometries(gl, attributes, info, bindingStates) {\\n const geometries = {};\\n const wireframeAttributes = new WeakMap();\\n function onGeometryDispose(event) {\\n const geometry = event.target;\\n if (geometry.index !== null) {\\n attributes.remove(geometry.index);\\n }\\n for (const name in geometry.attributes) {\\n attributes.remove(geometry.attributes[name]);\\n }\\n geometry.removeEventListener(\\\\\\\"dispose\\\\\\\", onGeometryDispose);\\n delete geometries[geometry.id];\\n const attribute = wireframeAttributes.get(geometry);\\n if (attribute) {\\n attributes.remove(attribute);\\n wireframeAttributes.delete(geometry);\\n }\\n bindingStates.releaseStatesOfGeometry(geometry);\\n if (geometry.isInstancedBufferGeometry === true) {\\n delete geometry._maxInstanceCount;\\n }\\n info.memory.geometries--;\\n }\\n function get(object, geometry) {\\n if (geometries[geometry.id] === true)\\n return geometry;\\n geometry.addEventListener(\\\\\\\"dispose\\\\\\\", onGeometryDispose);\\n geometries[geometry.id] = true;\\n info.memory.geometries++;\\n return geometry;\\n }\\n function update(geometry) {\\n const geometryAttributes = geometry.attributes;\\n for (const name in geometryAttributes) {\\n attributes.update(geometryAttributes[name], gl.ARRAY_BUFFER);\\n }\\n const morphAttributes = geometry.morphAttributes;\\n for (const name in morphAttributes) {\\n const array = morphAttributes[name];\\n for (let i = 0, l = array.length; i < l; i++) {\\n attributes.update(array[i], gl.ARRAY_BUFFER);\\n }\\n }\\n }\\n function updateWireframeAttribute(geometry) {\\n const indices = [];\\n const geometryIndex = geometry.index;\\n const geometryPosition = geometry.attributes.position;\\n let version = 0;\\n if (geometryIndex !== null) {\\n const array = geometryIndex.array;\\n version = geometryIndex.version;\\n for (let i = 0, l = array.length; i < l; i += 3) {\\n const a = array[i + 0];\\n const b = array[i + 1];\\n const c = array[i + 2];\\n indices.push(a, b, b, c, c, a);\\n }\\n } else {\\n const array = geometryPosition.array;\\n version = geometryPosition.version;\\n for (let i = 0, l = array.length / 3 - 1; i < l; i += 3) {\\n const a = i + 0;\\n const b = i + 1;\\n const c = i + 2;\\n indices.push(a, b, b, c, c, a);\\n }\\n }\\n const attribute = new (arrayMax(indices) > 65535 ? Uint32BufferAttribute : Uint16BufferAttribute)(indices, 1);\\n attribute.version = version;\\n const previousAttribute = wireframeAttributes.get(geometry);\\n if (previousAttribute)\\n attributes.remove(previousAttribute);\\n wireframeAttributes.set(geometry, attribute);\\n }\\n function getWireframeAttribute(geometry) {\\n const currentAttribute = wireframeAttributes.get(geometry);\\n if (currentAttribute) {\\n const geometryIndex = geometry.index;\\n if (geometryIndex !== null) {\\n if (currentAttribute.version < geometryIndex.version) {\\n updateWireframeAttribute(geometry);\\n }\\n }\\n } else {\\n updateWireframeAttribute(geometry);\\n }\\n return wireframeAttributes.get(geometry);\\n }\\n return {\\n get,\\n update,\\n getWireframeAttribute\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLIndexedBufferRenderer.js\\nfunction WebGLIndexedBufferRenderer(gl, extensions, info, capabilities) {\\n const isWebGL2 = capabilities.isWebGL2;\\n let mode;\\n function setMode(value) {\\n mode = value;\\n }\\n let type, bytesPerElement;\\n function setIndex(value) {\\n type = value.type;\\n bytesPerElement = value.bytesPerElement;\\n }\\n function render(start, count) {\\n gl.drawElements(mode, count, type, start * bytesPerElement);\\n info.update(count, mode, 1);\\n }\\n function renderInstances(start, count, primcount) {\\n if (primcount === 0)\\n return;\\n let extension, methodName;\\n if (isWebGL2) {\\n extension = gl;\\n methodName = \\\\\\\"drawElementsInstanced\\\\\\\";\\n } else {\\n extension = extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\");\\n methodName = \\\\\\\"drawElementsInstancedANGLE\\\\\\\";\\n if (extension === null) {\\n console.error(\\\\\\\"THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");\\n return;\\n }\\n }\\n extension[methodName](mode, count, type, start * bytesPerElement, primcount);\\n info.update(count, mode, primcount);\\n }\\n this.setMode = setMode;\\n this.setIndex = setIndex;\\n this.render = render;\\n this.renderInstances = renderInstances;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLInfo.js\\nfunction WebGLInfo(gl) {\\n const memory = {\\n geometries: 0,\\n textures: 0\\n };\\n const render = {\\n frame: 0,\\n calls: 0,\\n triangles: 0,\\n points: 0,\\n lines: 0\\n };\\n function update(count, mode, instanceCount) {\\n render.calls++;\\n switch (mode) {\\n case gl.TRIANGLES:\\n render.triangles += instanceCount * (count / 3);\\n break;\\n case gl.LINES:\\n render.lines += instanceCount * (count / 2);\\n break;\\n case gl.LINE_STRIP:\\n render.lines += instanceCount * (count - 1);\\n break;\\n case gl.LINE_LOOP:\\n render.lines += instanceCount * count;\\n break;\\n case gl.POINTS:\\n render.points += instanceCount * count;\\n break;\\n default:\\n console.error(\\\\\\\"THREE.WebGLInfo: Unknown draw mode:\\\\\\\", mode);\\n break;\\n }\\n }\\n function reset() {\\n render.frame++;\\n render.calls = 0;\\n render.triangles = 0;\\n render.points = 0;\\n render.lines = 0;\\n }\\n return {\\n memory,\\n render,\\n programs: null,\\n autoReset: true,\\n reset,\\n update\\n };\\n}\\n\\n// ../../../node_modules/three/src/textures/DataTexture2DArray.js\\nvar DataTexture2DArray = class extends Texture {\\n constructor(data = null, width = 1, height = 1, depth2 = 1) {\\n super(null);\\n this.image = {data, width, height, depth: depth2};\\n this.magFilter = NearestFilter;\\n this.minFilter = NearestFilter;\\n this.wrapR = ClampToEdgeWrapping;\\n this.generateMipmaps = false;\\n this.flipY = false;\\n this.unpackAlignment = 1;\\n this.needsUpdate = true;\\n }\\n};\\nDataTexture2DArray.prototype.isDataTexture2DArray = true;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLMorphtargets.js\\nfunction numericalSort(a, b) {\\n return a[0] - b[0];\\n}\\nfunction absNumericalSort(a, b) {\\n return Math.abs(b[1]) - Math.abs(a[1]);\\n}\\nfunction denormalize(morph, attribute) {\\n let denominator = 1;\\n const array = attribute.isInterleavedBufferAttribute ? attribute.data.array : attribute.array;\\n if (array instanceof Int8Array)\\n denominator = 127;\\n else if (array instanceof Int16Array)\\n denominator = 32767;\\n else if (array instanceof Int32Array)\\n denominator = 2147483647;\\n else\\n console.error(\\\\\\\"THREE.WebGLMorphtargets: Unsupported morph attribute data type: \\\\\\\", array);\\n morph.divideScalar(denominator);\\n}\\nfunction WebGLMorphtargets(gl, capabilities, textures) {\\n const influencesList = {};\\n const morphInfluences = new Float32Array(8);\\n const morphTextures = new WeakMap();\\n const morph = new Vector3();\\n const workInfluences = [];\\n for (let i = 0; i < 8; i++) {\\n workInfluences[i] = [i, 0];\\n }\\n function update(object, geometry, material, program) {\\n const objectInfluences = object.morphTargetInfluences;\\n if (capabilities.isWebGL2 === true) {\\n const numberOfMorphTargets = geometry.morphAttributes.position.length;\\n let entry = morphTextures.get(geometry);\\n if (entry === void 0 || entry.count !== numberOfMorphTargets) {\\n if (entry !== void 0)\\n entry.texture.dispose();\\n const hasMorphNormals = geometry.morphAttributes.normal !== void 0;\\n const morphTargets = geometry.morphAttributes.position;\\n const morphNormals = geometry.morphAttributes.normal || [];\\n const numberOfVertices = geometry.attributes.position.count;\\n const numberOfVertexData = hasMorphNormals === true ? 2 : 1;\\n let width = numberOfVertices * numberOfVertexData;\\n let height = 1;\\n if (width > capabilities.maxTextureSize) {\\n height = Math.ceil(width / capabilities.maxTextureSize);\\n width = capabilities.maxTextureSize;\\n }\\n const buffer = new Float32Array(width * height * 4 * numberOfMorphTargets);\\n const texture = new DataTexture2DArray(buffer, width, height, numberOfMorphTargets);\\n texture.format = RGBAFormat;\\n texture.type = FloatType;\\n const vertexDataStride = numberOfVertexData * 4;\\n for (let i = 0; i < numberOfMorphTargets; i++) {\\n const morphTarget = morphTargets[i];\\n const morphNormal = morphNormals[i];\\n const offset = width * height * 4 * i;\\n for (let j = 0; j < morphTarget.count; j++) {\\n morph.fromBufferAttribute(morphTarget, j);\\n if (morphTarget.normalized === true)\\n denormalize(morph, morphTarget);\\n const stride = j * vertexDataStride;\\n buffer[offset + stride + 0] = morph.x;\\n buffer[offset + stride + 1] = morph.y;\\n buffer[offset + stride + 2] = morph.z;\\n buffer[offset + stride + 3] = 0;\\n if (hasMorphNormals === true) {\\n morph.fromBufferAttribute(morphNormal, j);\\n if (morphNormal.normalized === true)\\n denormalize(morph, morphNormal);\\n buffer[offset + stride + 4] = morph.x;\\n buffer[offset + stride + 5] = morph.y;\\n buffer[offset + stride + 6] = morph.z;\\n buffer[offset + stride + 7] = 0;\\n }\\n }\\n }\\n entry = {\\n count: numberOfMorphTargets,\\n texture,\\n size: new Vector2(width, height)\\n };\\n morphTextures.set(geometry, entry);\\n }\\n let morphInfluencesSum = 0;\\n for (let i = 0; i < objectInfluences.length; i++) {\\n morphInfluencesSum += objectInfluences[i];\\n }\\n const morphBaseInfluence = geometry.morphTargetsRelative ? 1 : 1 - morphInfluencesSum;\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetBaseInfluence\\\\\\\", morphBaseInfluence);\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetInfluences\\\\\\\", objectInfluences);\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetsTexture\\\\\\\", entry.texture, textures);\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetsTextureSize\\\\\\\", entry.size);\\n } else {\\n const length = objectInfluences === void 0 ? 0 : objectInfluences.length;\\n let influences = influencesList[geometry.id];\\n if (influences === void 0 || influences.length !== length) {\\n influences = [];\\n for (let i = 0; i < length; i++) {\\n influences[i] = [i, 0];\\n }\\n influencesList[geometry.id] = influences;\\n }\\n for (let i = 0; i < length; i++) {\\n const influence = influences[i];\\n influence[0] = i;\\n influence[1] = objectInfluences[i];\\n }\\n influences.sort(absNumericalSort);\\n for (let i = 0; i < 8; i++) {\\n if (i < length && influences[i][1]) {\\n workInfluences[i][0] = influences[i][0];\\n workInfluences[i][1] = influences[i][1];\\n } else {\\n workInfluences[i][0] = Number.MAX_SAFE_INTEGER;\\n workInfluences[i][1] = 0;\\n }\\n }\\n workInfluences.sort(numericalSort);\\n const morphTargets = geometry.morphAttributes.position;\\n const morphNormals = geometry.morphAttributes.normal;\\n let morphInfluencesSum = 0;\\n for (let i = 0; i < 8; i++) {\\n const influence = workInfluences[i];\\n const index = influence[0];\\n const value = influence[1];\\n if (index !== Number.MAX_SAFE_INTEGER && value) {\\n if (morphTargets && geometry.getAttribute(\\\\\\\"morphTarget\\\\\\\" + i) !== morphTargets[index]) {\\n geometry.setAttribute(\\\\\\\"morphTarget\\\\\\\" + i, morphTargets[index]);\\n }\\n if (morphNormals && geometry.getAttribute(\\\\\\\"morphNormal\\\\\\\" + i) !== morphNormals[index]) {\\n geometry.setAttribute(\\\\\\\"morphNormal\\\\\\\" + i, morphNormals[index]);\\n }\\n morphInfluences[i] = value;\\n morphInfluencesSum += value;\\n } else {\\n if (morphTargets && geometry.hasAttribute(\\\\\\\"morphTarget\\\\\\\" + i) === true) {\\n geometry.deleteAttribute(\\\\\\\"morphTarget\\\\\\\" + i);\\n }\\n if (morphNormals && geometry.hasAttribute(\\\\\\\"morphNormal\\\\\\\" + i) === true) {\\n geometry.deleteAttribute(\\\\\\\"morphNormal\\\\\\\" + i);\\n }\\n morphInfluences[i] = 0;\\n }\\n }\\n const morphBaseInfluence = geometry.morphTargetsRelative ? 1 : 1 - morphInfluencesSum;\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetBaseInfluence\\\\\\\", morphBaseInfluence);\\n program.getUniforms().setValue(gl, \\\\\\\"morphTargetInfluences\\\\\\\", morphInfluences);\\n }\\n }\\n return {\\n update\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/WebGLMultisampleRenderTarget.js\\nvar WebGLMultisampleRenderTarget = class extends WebGLRenderTarget {\\n constructor(width, height, options) {\\n super(width, height, options);\\n this.samples = 4;\\n }\\n copy(source) {\\n super.copy.call(this, source);\\n this.samples = source.samples;\\n return this;\\n }\\n};\\nWebGLMultisampleRenderTarget.prototype.isWebGLMultisampleRenderTarget = true;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLObjects.js\\nfunction WebGLObjects(gl, geometries, attributes, info) {\\n let updateMap = new WeakMap();\\n function update(object) {\\n const frame = info.render.frame;\\n const geometry = object.geometry;\\n const buffergeometry = geometries.get(object, geometry);\\n if (updateMap.get(buffergeometry) !== frame) {\\n geometries.update(buffergeometry);\\n updateMap.set(buffergeometry, frame);\\n }\\n if (object.isInstancedMesh) {\\n if (object.hasEventListener(\\\\\\\"dispose\\\\\\\", onInstancedMeshDispose) === false) {\\n object.addEventListener(\\\\\\\"dispose\\\\\\\", onInstancedMeshDispose);\\n }\\n attributes.update(object.instanceMatrix, gl.ARRAY_BUFFER);\\n if (object.instanceColor !== null) {\\n attributes.update(object.instanceColor, gl.ARRAY_BUFFER);\\n }\\n }\\n return buffergeometry;\\n }\\n function dispose() {\\n updateMap = new WeakMap();\\n }\\n function onInstancedMeshDispose(event) {\\n const instancedMesh = event.target;\\n instancedMesh.removeEventListener(\\\\\\\"dispose\\\\\\\", onInstancedMeshDispose);\\n attributes.remove(instancedMesh.instanceMatrix);\\n if (instancedMesh.instanceColor !== null)\\n attributes.remove(instancedMesh.instanceColor);\\n }\\n return {\\n update,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/textures/DataTexture3D.js\\nvar DataTexture3D = class extends Texture {\\n constructor(data = null, width = 1, height = 1, depth2 = 1) {\\n super(null);\\n this.image = {data, width, height, depth: depth2};\\n this.magFilter = NearestFilter;\\n this.minFilter = NearestFilter;\\n this.wrapR = ClampToEdgeWrapping;\\n this.generateMipmaps = false;\\n this.flipY = false;\\n this.unpackAlignment = 1;\\n this.needsUpdate = true;\\n }\\n};\\nDataTexture3D.prototype.isDataTexture3D = true;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLUniforms.js\\nvar emptyTexture = new Texture();\\nvar emptyTexture2dArray = new DataTexture2DArray();\\nvar emptyTexture3d = new DataTexture3D();\\nvar emptyCubeTexture = new CubeTexture();\\nvar arrayCacheF32 = [];\\nvar arrayCacheI32 = [];\\nvar mat4array = new Float32Array(16);\\nvar mat3array = new Float32Array(9);\\nvar mat2array = new Float32Array(4);\\nfunction flatten(array, nBlocks, blockSize) {\\n const firstElem = array[0];\\n if (firstElem <= 0 || firstElem > 0)\\n return array;\\n const n = nBlocks * blockSize;\\n let r = arrayCacheF32[n];\\n if (r === void 0) {\\n r = new Float32Array(n);\\n arrayCacheF32[n] = r;\\n }\\n if (nBlocks !== 0) {\\n firstElem.toArray(r, 0);\\n for (let i = 1, offset = 0; i !== nBlocks; ++i) {\\n offset += blockSize;\\n array[i].toArray(r, offset);\\n }\\n }\\n return r;\\n}\\nfunction arraysEqual(a, b) {\\n if (a.length !== b.length)\\n return false;\\n for (let i = 0, l = a.length; i < l; i++) {\\n if (a[i] !== b[i])\\n return false;\\n }\\n return true;\\n}\\nfunction copyArray(a, b) {\\n for (let i = 0, l = b.length; i < l; i++) {\\n a[i] = b[i];\\n }\\n}\\nfunction allocTexUnits(textures, n) {\\n let r = arrayCacheI32[n];\\n if (r === void 0) {\\n r = new Int32Array(n);\\n arrayCacheI32[n] = r;\\n }\\n for (let i = 0; i !== n; ++i) {\\n r[i] = textures.allocateTextureUnit();\\n }\\n return r;\\n}\\nfunction setValueV1f(gl, v) {\\n const cache = this.cache;\\n if (cache[0] === v)\\n return;\\n gl.uniform1f(this.addr, v);\\n cache[0] = v;\\n}\\nfunction setValueV2f(gl, v) {\\n const cache = this.cache;\\n if (v.x !== void 0) {\\n if (cache[0] !== v.x || cache[1] !== v.y) {\\n gl.uniform2f(this.addr, v.x, v.y);\\n cache[0] = v.x;\\n cache[1] = v.y;\\n }\\n } else {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform2fv(this.addr, v);\\n copyArray(cache, v);\\n }\\n}\\nfunction setValueV3f(gl, v) {\\n const cache = this.cache;\\n if (v.x !== void 0) {\\n if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z) {\\n gl.uniform3f(this.addr, v.x, v.y, v.z);\\n cache[0] = v.x;\\n cache[1] = v.y;\\n cache[2] = v.z;\\n }\\n } else if (v.r !== void 0) {\\n if (cache[0] !== v.r || cache[1] !== v.g || cache[2] !== v.b) {\\n gl.uniform3f(this.addr, v.r, v.g, v.b);\\n cache[0] = v.r;\\n cache[1] = v.g;\\n cache[2] = v.b;\\n }\\n } else {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform3fv(this.addr, v);\\n copyArray(cache, v);\\n }\\n}\\nfunction setValueV4f(gl, v) {\\n const cache = this.cache;\\n if (v.x !== void 0) {\\n if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z || cache[3] !== v.w) {\\n gl.uniform4f(this.addr, v.x, v.y, v.z, v.w);\\n cache[0] = v.x;\\n cache[1] = v.y;\\n cache[2] = v.z;\\n cache[3] = v.w;\\n }\\n } else {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform4fv(this.addr, v);\\n copyArray(cache, v);\\n }\\n}\\nfunction setValueM2(gl, v) {\\n const cache = this.cache;\\n const elements = v.elements;\\n if (elements === void 0) {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniformMatrix2fv(this.addr, false, v);\\n copyArray(cache, v);\\n } else {\\n if (arraysEqual(cache, elements))\\n return;\\n mat2array.set(elements);\\n gl.uniformMatrix2fv(this.addr, false, mat2array);\\n copyArray(cache, elements);\\n }\\n}\\nfunction setValueM3(gl, v) {\\n const cache = this.cache;\\n const elements = v.elements;\\n if (elements === void 0) {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniformMatrix3fv(this.addr, false, v);\\n copyArray(cache, v);\\n } else {\\n if (arraysEqual(cache, elements))\\n return;\\n mat3array.set(elements);\\n gl.uniformMatrix3fv(this.addr, false, mat3array);\\n copyArray(cache, elements);\\n }\\n}\\nfunction setValueM4(gl, v) {\\n const cache = this.cache;\\n const elements = v.elements;\\n if (elements === void 0) {\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniformMatrix4fv(this.addr, false, v);\\n copyArray(cache, v);\\n } else {\\n if (arraysEqual(cache, elements))\\n return;\\n mat4array.set(elements);\\n gl.uniformMatrix4fv(this.addr, false, mat4array);\\n copyArray(cache, elements);\\n }\\n}\\nfunction setValueV1i(gl, v) {\\n const cache = this.cache;\\n if (cache[0] === v)\\n return;\\n gl.uniform1i(this.addr, v);\\n cache[0] = v;\\n}\\nfunction setValueV2i(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform2iv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueV3i(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform3iv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueV4i(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform4iv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueV1ui(gl, v) {\\n const cache = this.cache;\\n if (cache[0] === v)\\n return;\\n gl.uniform1ui(this.addr, v);\\n cache[0] = v;\\n}\\nfunction setValueV2ui(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform2uiv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueV3ui(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform3uiv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueV4ui(gl, v) {\\n const cache = this.cache;\\n if (arraysEqual(cache, v))\\n return;\\n gl.uniform4uiv(this.addr, v);\\n copyArray(cache, v);\\n}\\nfunction setValueT1(gl, v, textures) {\\n const cache = this.cache;\\n const unit = textures.allocateTextureUnit();\\n if (cache[0] !== unit) {\\n gl.uniform1i(this.addr, unit);\\n cache[0] = unit;\\n }\\n textures.safeSetTexture2D(v || emptyTexture, unit);\\n}\\nfunction setValueT3D1(gl, v, textures) {\\n const cache = this.cache;\\n const unit = textures.allocateTextureUnit();\\n if (cache[0] !== unit) {\\n gl.uniform1i(this.addr, unit);\\n cache[0] = unit;\\n }\\n textures.setTexture3D(v || emptyTexture3d, unit);\\n}\\nfunction setValueT6(gl, v, textures) {\\n const cache = this.cache;\\n const unit = textures.allocateTextureUnit();\\n if (cache[0] !== unit) {\\n gl.uniform1i(this.addr, unit);\\n cache[0] = unit;\\n }\\n textures.safeSetTextureCube(v || emptyCubeTexture, unit);\\n}\\nfunction setValueT2DArray1(gl, v, textures) {\\n const cache = this.cache;\\n const unit = textures.allocateTextureUnit();\\n if (cache[0] !== unit) {\\n gl.uniform1i(this.addr, unit);\\n cache[0] = unit;\\n }\\n textures.setTexture2DArray(v || emptyTexture2dArray, unit);\\n}\\nfunction getSingularSetter(type) {\\n switch (type) {\\n case 5126:\\n return setValueV1f;\\n case 35664:\\n return setValueV2f;\\n case 35665:\\n return setValueV3f;\\n case 35666:\\n return setValueV4f;\\n case 35674:\\n return setValueM2;\\n case 35675:\\n return setValueM3;\\n case 35676:\\n return setValueM4;\\n case 5124:\\n case 35670:\\n return setValueV1i;\\n case 35667:\\n case 35671:\\n return setValueV2i;\\n case 35668:\\n case 35672:\\n return setValueV3i;\\n case 35669:\\n case 35673:\\n return setValueV4i;\\n case 5125:\\n return setValueV1ui;\\n case 36294:\\n return setValueV2ui;\\n case 36295:\\n return setValueV3ui;\\n case 36296:\\n return setValueV4ui;\\n case 35678:\\n case 36198:\\n case 36298:\\n case 36306:\\n case 35682:\\n return setValueT1;\\n case 35679:\\n case 36299:\\n case 36307:\\n return setValueT3D1;\\n case 35680:\\n case 36300:\\n case 36308:\\n case 36293:\\n return setValueT6;\\n case 36289:\\n case 36303:\\n case 36311:\\n case 36292:\\n return setValueT2DArray1;\\n }\\n}\\nfunction setValueV1fArray(gl, v) {\\n gl.uniform1fv(this.addr, v);\\n}\\nfunction setValueV2fArray(gl, v) {\\n const data = flatten(v, this.size, 2);\\n gl.uniform2fv(this.addr, data);\\n}\\nfunction setValueV3fArray(gl, v) {\\n const data = flatten(v, this.size, 3);\\n gl.uniform3fv(this.addr, data);\\n}\\nfunction setValueV4fArray(gl, v) {\\n const data = flatten(v, this.size, 4);\\n gl.uniform4fv(this.addr, data);\\n}\\nfunction setValueM2Array(gl, v) {\\n const data = flatten(v, this.size, 4);\\n gl.uniformMatrix2fv(this.addr, false, data);\\n}\\nfunction setValueM3Array(gl, v) {\\n const data = flatten(v, this.size, 9);\\n gl.uniformMatrix3fv(this.addr, false, data);\\n}\\nfunction setValueM4Array(gl, v) {\\n const data = flatten(v, this.size, 16);\\n gl.uniformMatrix4fv(this.addr, false, data);\\n}\\nfunction setValueV1iArray(gl, v) {\\n gl.uniform1iv(this.addr, v);\\n}\\nfunction setValueV2iArray(gl, v) {\\n gl.uniform2iv(this.addr, v);\\n}\\nfunction setValueV3iArray(gl, v) {\\n gl.uniform3iv(this.addr, v);\\n}\\nfunction setValueV4iArray(gl, v) {\\n gl.uniform4iv(this.addr, v);\\n}\\nfunction setValueV1uiArray(gl, v) {\\n gl.uniform1uiv(this.addr, v);\\n}\\nfunction setValueV2uiArray(gl, v) {\\n gl.uniform2uiv(this.addr, v);\\n}\\nfunction setValueV3uiArray(gl, v) {\\n gl.uniform3uiv(this.addr, v);\\n}\\nfunction setValueV4uiArray(gl, v) {\\n gl.uniform4uiv(this.addr, v);\\n}\\nfunction setValueT1Array(gl, v, textures) {\\n const n = v.length;\\n const units = allocTexUnits(textures, n);\\n gl.uniform1iv(this.addr, units);\\n for (let i = 0; i !== n; ++i) {\\n textures.safeSetTexture2D(v[i] || emptyTexture, units[i]);\\n }\\n}\\nfunction setValueT6Array(gl, v, textures) {\\n const n = v.length;\\n const units = allocTexUnits(textures, n);\\n gl.uniform1iv(this.addr, units);\\n for (let i = 0; i !== n; ++i) {\\n textures.safeSetTextureCube(v[i] || emptyCubeTexture, units[i]);\\n }\\n}\\nfunction getPureArraySetter(type) {\\n switch (type) {\\n case 5126:\\n return setValueV1fArray;\\n case 35664:\\n return setValueV2fArray;\\n case 35665:\\n return setValueV3fArray;\\n case 35666:\\n return setValueV4fArray;\\n case 35674:\\n return setValueM2Array;\\n case 35675:\\n return setValueM3Array;\\n case 35676:\\n return setValueM4Array;\\n case 5124:\\n case 35670:\\n return setValueV1iArray;\\n case 35667:\\n case 35671:\\n return setValueV2iArray;\\n case 35668:\\n case 35672:\\n return setValueV3iArray;\\n case 35669:\\n case 35673:\\n return setValueV4iArray;\\n case 5125:\\n return setValueV1uiArray;\\n case 36294:\\n return setValueV2uiArray;\\n case 36295:\\n return setValueV3uiArray;\\n case 36296:\\n return setValueV4uiArray;\\n case 35678:\\n case 36198:\\n case 36298:\\n case 36306:\\n case 35682:\\n return setValueT1Array;\\n case 35680:\\n case 36300:\\n case 36308:\\n case 36293:\\n return setValueT6Array;\\n }\\n}\\nfunction SingleUniform(id, activeInfo, addr) {\\n this.id = id;\\n this.addr = addr;\\n this.cache = [];\\n this.setValue = getSingularSetter(activeInfo.type);\\n}\\nfunction PureArrayUniform(id, activeInfo, addr) {\\n this.id = id;\\n this.addr = addr;\\n this.cache = [];\\n this.size = activeInfo.size;\\n this.setValue = getPureArraySetter(activeInfo.type);\\n}\\nPureArrayUniform.prototype.updateCache = function(data) {\\n const cache = this.cache;\\n if (data instanceof Float32Array && cache.length !== data.length) {\\n this.cache = new Float32Array(data.length);\\n }\\n copyArray(cache, data);\\n};\\nfunction StructuredUniform(id) {\\n this.id = id;\\n this.seq = [];\\n this.map = {};\\n}\\nStructuredUniform.prototype.setValue = function(gl, value, textures) {\\n const seq = this.seq;\\n for (let i = 0, n = seq.length; i !== n; ++i) {\\n const u = seq[i];\\n u.setValue(gl, value[u.id], textures);\\n }\\n};\\nvar RePathPart = /(\\\\w+)(\\\\])?(\\\\[|\\\\.)?/g;\\nfunction addUniform(container, uniformObject) {\\n container.seq.push(uniformObject);\\n container.map[uniformObject.id] = uniformObject;\\n}\\nfunction parseUniform(activeInfo, addr, container) {\\n const path = activeInfo.name, pathLength = path.length;\\n RePathPart.lastIndex = 0;\\n while (true) {\\n const match = RePathPart.exec(path), matchEnd = RePathPart.lastIndex;\\n let id = match[1];\\n const idIsIndex = match[2] === \\\\\\\"]\\\\\\\", subscript = match[3];\\n if (idIsIndex)\\n id = id | 0;\\n if (subscript === void 0 || subscript === \\\\\\\"[\\\\\\\" && matchEnd + 2 === pathLength) {\\n addUniform(container, subscript === void 0 ? new SingleUniform(id, activeInfo, addr) : new PureArrayUniform(id, activeInfo, addr));\\n break;\\n } else {\\n const map = container.map;\\n let next = map[id];\\n if (next === void 0) {\\n next = new StructuredUniform(id);\\n addUniform(container, next);\\n }\\n container = next;\\n }\\n }\\n}\\nfunction WebGLUniforms(gl, program) {\\n this.seq = [];\\n this.map = {};\\n const n = gl.getProgramParameter(program, gl.ACTIVE_UNIFORMS);\\n for (let i = 0; i < n; ++i) {\\n const info = gl.getActiveUniform(program, i), addr = gl.getUniformLocation(program, info.name);\\n parseUniform(info, addr, this);\\n }\\n}\\nWebGLUniforms.prototype.setValue = function(gl, name, value, textures) {\\n const u = this.map[name];\\n if (u !== void 0)\\n u.setValue(gl, value, textures);\\n};\\nWebGLUniforms.prototype.setOptional = function(gl, object, name) {\\n const v = object[name];\\n if (v !== void 0)\\n this.setValue(gl, name, v);\\n};\\nWebGLUniforms.upload = function(gl, seq, values, textures) {\\n for (let i = 0, n = seq.length; i !== n; ++i) {\\n const u = seq[i], v = values[u.id];\\n if (v.needsUpdate !== false) {\\n u.setValue(gl, v.value, textures);\\n }\\n }\\n};\\nWebGLUniforms.seqWithValue = function(seq, values) {\\n const r = [];\\n for (let i = 0, n = seq.length; i !== n; ++i) {\\n const u = seq[i];\\n if (u.id in values)\\n r.push(u);\\n }\\n return r;\\n};\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLShader.js\\nfunction WebGLShader(gl, type, string) {\\n const shader = gl.createShader(type);\\n gl.shaderSource(shader, string);\\n gl.compileShader(shader);\\n return shader;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLProgram.js\\nvar programIdCount = 0;\\nfunction addLineNumbers(string) {\\n const lines = string.split(\\\\\\\"\\\\n\\\\\\\");\\n for (let i = 0; i < lines.length; i++) {\\n lines[i] = i + 1 + \\\\\\\": \\\\\\\" + lines[i];\\n }\\n return lines.join(\\\\\\\"\\\\n\\\\\\\");\\n}\\nfunction getEncodingComponents(encoding) {\\n switch (encoding) {\\n case LinearEncoding:\\n return [\\\\\\\"Linear\\\\\\\", \\\\\\\"( value )\\\\\\\"];\\n case sRGBEncoding:\\n return [\\\\\\\"sRGB\\\\\\\", \\\\\\\"( value )\\\\\\\"];\\n case RGBEEncoding:\\n return [\\\\\\\"RGBE\\\\\\\", \\\\\\\"( value )\\\\\\\"];\\n case RGBM7Encoding:\\n return [\\\\\\\"RGBM\\\\\\\", \\\\\\\"( value, 7.0 )\\\\\\\"];\\n case RGBM16Encoding:\\n return [\\\\\\\"RGBM\\\\\\\", \\\\\\\"( value, 16.0 )\\\\\\\"];\\n case RGBDEncoding:\\n return [\\\\\\\"RGBD\\\\\\\", \\\\\\\"( value, 256.0 )\\\\\\\"];\\n case GammaEncoding:\\n return [\\\\\\\"Gamma\\\\\\\", \\\\\\\"( value, float( GAMMA_FACTOR ) )\\\\\\\"];\\n case LogLuvEncoding:\\n return [\\\\\\\"LogLuv\\\\\\\", \\\\\\\"( value )\\\\\\\"];\\n default:\\n console.warn(\\\\\\\"THREE.WebGLProgram: Unsupported encoding:\\\\\\\", encoding);\\n return [\\\\\\\"Linear\\\\\\\", \\\\\\\"( value )\\\\\\\"];\\n }\\n}\\nfunction getShaderErrors(gl, shader, type) {\\n const status = gl.getShaderParameter(shader, gl.COMPILE_STATUS);\\n const errors = gl.getShaderInfoLog(shader).trim();\\n if (status && errors === \\\\\\\"\\\\\\\")\\n return \\\\\\\"\\\\\\\";\\n return type.toUpperCase() + \\\\\\\"\\\\n\\\\n\\\\\\\" + errors + \\\\\\\"\\\\n\\\\n\\\\\\\" + addLineNumbers(gl.getShaderSource(shader));\\n}\\nfunction getTexelDecodingFunction(functionName, encoding) {\\n const components = getEncodingComponents(encoding);\\n return \\\\\\\"vec4 \\\\\\\" + functionName + \\\\\\\"( vec4 value ) { return \\\\\\\" + components[0] + \\\\\\\"ToLinear\\\\\\\" + components[1] + \\\\\\\"; }\\\\\\\";\\n}\\nfunction getTexelEncodingFunction(functionName, encoding) {\\n const components = getEncodingComponents(encoding);\\n return \\\\\\\"vec4 \\\\\\\" + functionName + \\\\\\\"( vec4 value ) { return LinearTo\\\\\\\" + components[0] + components[1] + \\\\\\\"; }\\\\\\\";\\n}\\nfunction getToneMappingFunction(functionName, toneMapping) {\\n let toneMappingName;\\n switch (toneMapping) {\\n case LinearToneMapping:\\n toneMappingName = \\\\\\\"Linear\\\\\\\";\\n break;\\n case ReinhardToneMapping:\\n toneMappingName = \\\\\\\"Reinhard\\\\\\\";\\n break;\\n case CineonToneMapping:\\n toneMappingName = \\\\\\\"OptimizedCineon\\\\\\\";\\n break;\\n case ACESFilmicToneMapping:\\n toneMappingName = \\\\\\\"ACESFilmic\\\\\\\";\\n break;\\n case CustomToneMapping:\\n toneMappingName = \\\\\\\"Custom\\\\\\\";\\n break;\\n default:\\n console.warn(\\\\\\\"THREE.WebGLProgram: Unsupported toneMapping:\\\\\\\", toneMapping);\\n toneMappingName = \\\\\\\"Linear\\\\\\\";\\n }\\n return \\\\\\\"vec3 \\\\\\\" + functionName + \\\\\\\"( vec3 color ) { return \\\\\\\" + toneMappingName + \\\\\\\"ToneMapping( color ); }\\\\\\\";\\n}\\nfunction generateExtensions(parameters) {\\n const chunks = [\\n parameters.extensionDerivatives || parameters.envMapCubeUV || parameters.bumpMap || parameters.tangentSpaceNormalMap || parameters.clearcoatNormalMap || parameters.flatShading || parameters.shaderID === \\\\\\\"physical\\\\\\\" ? \\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\" : \\\\\\\"\\\\\\\",\\n (parameters.extensionFragDepth || parameters.logarithmicDepthBuffer) && parameters.rendererExtensionFragDepth ? \\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.extensionDrawBuffers && parameters.rendererExtensionDrawBuffers ? \\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\" : \\\\\\\"\\\\\\\",\\n (parameters.extensionShaderTextureLOD || parameters.envMap || parameters.transmission) && parameters.rendererExtensionShaderTextureLod ? \\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\" : \\\\\\\"\\\\\\\"\\n ];\\n return chunks.filter(filterEmptyLine).join(\\\\\\\"\\\\n\\\\\\\");\\n}\\nfunction generateDefines(defines) {\\n const chunks = [];\\n for (const name in defines) {\\n const value = defines[name];\\n if (value === false)\\n continue;\\n chunks.push(\\\\\\\"#define \\\\\\\" + name + \\\\\\\" \\\\\\\" + value);\\n }\\n return chunks.join(\\\\\\\"\\\\n\\\\\\\");\\n}\\nfunction fetchAttributeLocations(gl, program) {\\n const attributes = {};\\n const n = gl.getProgramParameter(program, gl.ACTIVE_ATTRIBUTES);\\n for (let i = 0; i < n; i++) {\\n const info = gl.getActiveAttrib(program, i);\\n const name = info.name;\\n let locationSize = 1;\\n if (info.type === gl.FLOAT_MAT2)\\n locationSize = 2;\\n if (info.type === gl.FLOAT_MAT3)\\n locationSize = 3;\\n if (info.type === gl.FLOAT_MAT4)\\n locationSize = 4;\\n attributes[name] = {\\n type: info.type,\\n location: gl.getAttribLocation(program, name),\\n locationSize\\n };\\n }\\n return attributes;\\n}\\nfunction filterEmptyLine(string) {\\n return string !== \\\\\\\"\\\\\\\";\\n}\\nfunction replaceLightNums(string, parameters) {\\n return string.replace(/NUM_DIR_LIGHTS/g, parameters.numDirLights).replace(/NUM_SPOT_LIGHTS/g, parameters.numSpotLights).replace(/NUM_RECT_AREA_LIGHTS/g, parameters.numRectAreaLights).replace(/NUM_POINT_LIGHTS/g, parameters.numPointLights).replace(/NUM_HEMI_LIGHTS/g, parameters.numHemiLights).replace(/NUM_DIR_LIGHT_SHADOWS/g, parameters.numDirLightShadows).replace(/NUM_SPOT_LIGHT_SHADOWS/g, parameters.numSpotLightShadows).replace(/NUM_POINT_LIGHT_SHADOWS/g, parameters.numPointLightShadows);\\n}\\nfunction replaceClippingPlaneNums(string, parameters) {\\n return string.replace(/NUM_CLIPPING_PLANES/g, parameters.numClippingPlanes).replace(/UNION_CLIPPING_PLANES/g, parameters.numClippingPlanes - parameters.numClipIntersection);\\n}\\nvar includePattern = /^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm;\\nfunction resolveIncludes(string) {\\n return string.replace(includePattern, includeReplacer);\\n}\\nfunction includeReplacer(match, include) {\\n const string = ShaderChunk[include];\\n if (string === void 0) {\\n throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\" + include + \\\\\\\">\\\\\\\");\\n }\\n return resolveIncludes(string);\\n}\\nvar deprecatedUnrollLoopPattern = /#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g;\\nvar unrollLoopPattern = /#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;\\nfunction unrollLoops(string) {\\n return string.replace(unrollLoopPattern, loopReplacer).replace(deprecatedUnrollLoopPattern, deprecatedLoopReplacer);\\n}\\nfunction deprecatedLoopReplacer(match, start, end, snippet) {\\n console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\");\\n return loopReplacer(match, start, end, snippet);\\n}\\nfunction loopReplacer(match, start, end, snippet) {\\n let string = \\\\\\\"\\\\\\\";\\n for (let i = parseInt(start); i < parseInt(end); i++) {\\n string += snippet.replace(/\\\\[\\\\s*i\\\\s*\\\\]/g, \\\\\\\"[ \\\\\\\" + i + \\\\\\\" ]\\\\\\\").replace(/UNROLLED_LOOP_INDEX/g, i);\\n }\\n return string;\\n}\\nfunction generatePrecision(parameters) {\\n let precisionstring = \\\\\\\"precision \\\\\\\" + parameters.precision + \\\\\\\" float;\\\\nprecision \\\\\\\" + parameters.precision + \\\\\\\" int;\\\\\\\";\\n if (parameters.precision === \\\\\\\"highp\\\\\\\") {\\n precisionstring += \\\\\\\"\\\\n#define HIGH_PRECISION\\\\\\\";\\n } else if (parameters.precision === \\\\\\\"mediump\\\\\\\") {\\n precisionstring += \\\\\\\"\\\\n#define MEDIUM_PRECISION\\\\\\\";\\n } else if (parameters.precision === \\\\\\\"lowp\\\\\\\") {\\n precisionstring += \\\\\\\"\\\\n#define LOW_PRECISION\\\\\\\";\\n }\\n return precisionstring;\\n}\\nfunction generateShadowMapTypeDefine(parameters) {\\n let shadowMapTypeDefine = \\\\\\\"SHADOWMAP_TYPE_BASIC\\\\\\\";\\n if (parameters.shadowMapType === PCFShadowMap) {\\n shadowMapTypeDefine = \\\\\\\"SHADOWMAP_TYPE_PCF\\\\\\\";\\n } else if (parameters.shadowMapType === PCFSoftShadowMap) {\\n shadowMapTypeDefine = \\\\\\\"SHADOWMAP_TYPE_PCF_SOFT\\\\\\\";\\n } else if (parameters.shadowMapType === VSMShadowMap) {\\n shadowMapTypeDefine = \\\\\\\"SHADOWMAP_TYPE_VSM\\\\\\\";\\n }\\n return shadowMapTypeDefine;\\n}\\nfunction generateEnvMapTypeDefine(parameters) {\\n let envMapTypeDefine = \\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";\\n if (parameters.envMap) {\\n switch (parameters.envMapMode) {\\n case CubeReflectionMapping:\\n case CubeRefractionMapping:\\n envMapTypeDefine = \\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";\\n break;\\n case CubeUVReflectionMapping:\\n case CubeUVRefractionMapping:\\n envMapTypeDefine = \\\\\\\"ENVMAP_TYPE_CUBE_UV\\\\\\\";\\n break;\\n }\\n }\\n return envMapTypeDefine;\\n}\\nfunction generateEnvMapModeDefine(parameters) {\\n let envMapModeDefine = \\\\\\\"ENVMAP_MODE_REFLECTION\\\\\\\";\\n if (parameters.envMap) {\\n switch (parameters.envMapMode) {\\n case CubeRefractionMapping:\\n case CubeUVRefractionMapping:\\n envMapModeDefine = \\\\\\\"ENVMAP_MODE_REFRACTION\\\\\\\";\\n break;\\n }\\n }\\n return envMapModeDefine;\\n}\\nfunction generateEnvMapBlendingDefine(parameters) {\\n let envMapBlendingDefine = \\\\\\\"ENVMAP_BLENDING_NONE\\\\\\\";\\n if (parameters.envMap) {\\n switch (parameters.combine) {\\n case MultiplyOperation:\\n envMapBlendingDefine = \\\\\\\"ENVMAP_BLENDING_MULTIPLY\\\\\\\";\\n break;\\n case MixOperation:\\n envMapBlendingDefine = \\\\\\\"ENVMAP_BLENDING_MIX\\\\\\\";\\n break;\\n case AddOperation:\\n envMapBlendingDefine = \\\\\\\"ENVMAP_BLENDING_ADD\\\\\\\";\\n break;\\n }\\n }\\n return envMapBlendingDefine;\\n}\\nfunction WebGLProgram(renderer, cacheKey, parameters, bindingStates) {\\n const gl = renderer.getContext();\\n const defines = parameters.defines;\\n let vertexShader = parameters.vertexShader;\\n let fragmentShader = parameters.fragmentShader;\\n const shadowMapTypeDefine = generateShadowMapTypeDefine(parameters);\\n const envMapTypeDefine = generateEnvMapTypeDefine(parameters);\\n const envMapModeDefine = generateEnvMapModeDefine(parameters);\\n const envMapBlendingDefine = generateEnvMapBlendingDefine(parameters);\\n const gammaFactorDefine = renderer.gammaFactor > 0 ? renderer.gammaFactor : 1;\\n const customExtensions = parameters.isWebGL2 ? \\\\\\\"\\\\\\\" : generateExtensions(parameters);\\n const customDefines = generateDefines(defines);\\n const program = gl.createProgram();\\n let prefixVertex, prefixFragment;\\n let versionString = parameters.glslVersion ? \\\\\\\"#version \\\\\\\" + parameters.glslVersion + \\\\\\\"\\\\n\\\\\\\" : \\\\\\\"\\\\\\\";\\n if (parameters.isRawShaderMaterial) {\\n prefixVertex = [\\n customDefines\\n ].filter(filterEmptyLine).join(\\\\\\\"\\\\n\\\\\\\");\\n if (prefixVertex.length > 0) {\\n prefixVertex += \\\\\\\"\\\\n\\\\\\\";\\n }\\n prefixFragment = [\\n customExtensions,\\n customDefines\\n ].filter(filterEmptyLine).join(\\\\\\\"\\\\n\\\\\\\");\\n if (prefixFragment.length > 0) {\\n prefixFragment += \\\\\\\"\\\\n\\\\\\\";\\n }\\n } else {\\n prefixVertex = [\\n generatePrecision(parameters),\\n \\\\\\\"#define SHADER_NAME \\\\\\\" + parameters.shaderName,\\n customDefines,\\n parameters.instancing ? \\\\\\\"#define USE_INSTANCING\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.instancingColor ? \\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.supportsVertexTextures ? \\\\\\\"#define VERTEX_TEXTURES\\\\\\\" : \\\\\\\"\\\\\\\",\\n \\\\\\\"#define GAMMA_FACTOR \\\\\\\" + gammaFactorDefine,\\n \\\\\\\"#define MAX_BONES \\\\\\\" + parameters.maxBones,\\n parameters.useFog && parameters.fog ? \\\\\\\"#define USE_FOG\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.useFog && parameters.fogExp2 ? \\\\\\\"#define FOG_EXP2\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.map ? \\\\\\\"#define USE_MAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define USE_ENVMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define \\\\\\\" + envMapModeDefine : \\\\\\\"\\\\\\\",\\n parameters.lightMap ? \\\\\\\"#define USE_LIGHTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.aoMap ? \\\\\\\"#define USE_AOMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.emissiveMap ? \\\\\\\"#define USE_EMISSIVEMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.bumpMap ? \\\\\\\"#define USE_BUMPMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap ? \\\\\\\"#define USE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap && parameters.objectSpaceNormalMap ? \\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap && parameters.tangentSpaceNormalMap ? \\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatMap ? \\\\\\\"#define USE_CLEARCOATMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatRoughnessMap ? \\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatNormalMap ? \\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.displacementMap && parameters.supportsVertexTextures ? \\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularMap ? \\\\\\\"#define USE_SPECULARMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularIntensityMap ? \\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularTintMap ? \\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.roughnessMap ? \\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.metalnessMap ? \\\\\\\"#define USE_METALNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.alphaMap ? \\\\\\\"#define USE_ALPHAMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.transmission ? \\\\\\\"#define USE_TRANSMISSION\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.transmissionMap ? \\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.thicknessMap ? \\\\\\\"#define USE_THICKNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexTangents ? \\\\\\\"#define USE_TANGENT\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexColors ? \\\\\\\"#define USE_COLOR\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexAlphas ? \\\\\\\"#define USE_COLOR_ALPHA\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexUvs ? \\\\\\\"#define USE_UV\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.uvsVertexOnly ? \\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.flatShading ? \\\\\\\"#define FLAT_SHADED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.skinning ? \\\\\\\"#define USE_SKINNING\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.useVertexTexture ? \\\\\\\"#define BONE_TEXTURE\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.morphTargets ? \\\\\\\"#define USE_MORPHTARGETS\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.morphNormals && parameters.flatShading === false ? \\\\\\\"#define USE_MORPHNORMALS\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.morphTargets && parameters.isWebGL2 ? \\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.morphTargets && parameters.isWebGL2 ? \\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\" + parameters.morphTargetsCount : \\\\\\\"\\\\\\\",\\n parameters.doubleSided ? \\\\\\\"#define DOUBLE_SIDED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.flipSided ? \\\\\\\"#define FLIP_SIDED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.shadowMapEnabled ? \\\\\\\"#define USE_SHADOWMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.shadowMapEnabled ? \\\\\\\"#define \\\\\\\" + shadowMapTypeDefine : \\\\\\\"\\\\\\\",\\n parameters.sizeAttenuation ? \\\\\\\"#define USE_SIZEATTENUATION\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.logarithmicDepthBuffer ? \\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.logarithmicDepthBuffer && parameters.rendererExtensionFragDepth ? \\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\" : \\\\\\\"\\\\\\\",\\n \\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\n \\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\n \\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\n \\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\n \\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\n \\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\n \\\\\\\"uniform bool isOrthographic;\\\\\\\",\\n \\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\n \\\\\\\"\\tattribute mat4 instanceMatrix;\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\n \\\\\\\"\\tattribute vec3 instanceColor;\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"attribute vec3 position;\\\\\\\",\\n \\\\\\\"attribute vec3 normal;\\\\\\\",\\n \\\\\\\"attribute vec2 uv;\\\\\\\",\\n \\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\n \\\\\\\"\\tattribute vec4 tangent;\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\n \\\\\\\"\\tattribute vec4 color;\\\\\\\",\\n \\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\n \\\\\\\"\\tattribute vec3 color;\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\n \\\\\\\"\\tattribute vec3 morphTarget0;\\\\\\\",\\n \\\\\\\"\\tattribute vec3 morphTarget1;\\\\\\\",\\n \\\\\\\"\\tattribute vec3 morphTarget2;\\\\\\\",\\n \\\\\\\"\\tattribute vec3 morphTarget3;\\\\\\\",\\n \\\\\\\"\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphNormal0;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphNormal1;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphNormal2;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphNormal3;\\\\\\\",\\n \\\\\\\"\\t#else\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphTarget4;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphTarget5;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphTarget6;\\\\\\\",\\n \\\\\\\"\\t\\tattribute vec3 morphTarget7;\\\\\\\",\\n \\\\\\\"\\t#endif\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\n \\\\\\\"\\tattribute vec4 skinIndex;\\\\\\\",\\n \\\\\\\"\\tattribute vec4 skinWeight;\\\\\\\",\\n \\\\\\\"#endif\\\\\\\",\\n \\\\\\\"\\\\n\\\\\\\"\\n ].filter(filterEmptyLine).join(\\\\\\\"\\\\n\\\\\\\");\\n prefixFragment = [\\n customExtensions,\\n generatePrecision(parameters),\\n \\\\\\\"#define SHADER_NAME \\\\\\\" + parameters.shaderName,\\n customDefines,\\n \\\\\\\"#define GAMMA_FACTOR \\\\\\\" + gammaFactorDefine,\\n parameters.useFog && parameters.fog ? \\\\\\\"#define USE_FOG\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.useFog && parameters.fogExp2 ? \\\\\\\"#define FOG_EXP2\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.map ? \\\\\\\"#define USE_MAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.matcap ? \\\\\\\"#define USE_MATCAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define USE_ENVMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define \\\\\\\" + envMapTypeDefine : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define \\\\\\\" + envMapModeDefine : \\\\\\\"\\\\\\\",\\n parameters.envMap ? \\\\\\\"#define \\\\\\\" + envMapBlendingDefine : \\\\\\\"\\\\\\\",\\n parameters.lightMap ? \\\\\\\"#define USE_LIGHTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.aoMap ? \\\\\\\"#define USE_AOMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.emissiveMap ? \\\\\\\"#define USE_EMISSIVEMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.bumpMap ? \\\\\\\"#define USE_BUMPMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap ? \\\\\\\"#define USE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap && parameters.objectSpaceNormalMap ? \\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.normalMap && parameters.tangentSpaceNormalMap ? \\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoat ? \\\\\\\"#define USE_CLEARCOAT\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatMap ? \\\\\\\"#define USE_CLEARCOATMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatRoughnessMap ? \\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.clearcoatNormalMap ? \\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularMap ? \\\\\\\"#define USE_SPECULARMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularIntensityMap ? \\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.specularTintMap ? \\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.roughnessMap ? \\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.metalnessMap ? \\\\\\\"#define USE_METALNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.alphaMap ? \\\\\\\"#define USE_ALPHAMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.alphaTest ? \\\\\\\"#define USE_ALPHATEST\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.sheen ? \\\\\\\"#define USE_SHEEN\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.transmission ? \\\\\\\"#define USE_TRANSMISSION\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.transmissionMap ? \\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.thicknessMap ? \\\\\\\"#define USE_THICKNESSMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexTangents ? \\\\\\\"#define USE_TANGENT\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexColors || parameters.instancingColor ? \\\\\\\"#define USE_COLOR\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexAlphas ? \\\\\\\"#define USE_COLOR_ALPHA\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.vertexUvs ? \\\\\\\"#define USE_UV\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.uvsVertexOnly ? \\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.gradientMap ? \\\\\\\"#define USE_GRADIENTMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.flatShading ? \\\\\\\"#define FLAT_SHADED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.doubleSided ? \\\\\\\"#define DOUBLE_SIDED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.flipSided ? \\\\\\\"#define FLIP_SIDED\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.shadowMapEnabled ? \\\\\\\"#define USE_SHADOWMAP\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.shadowMapEnabled ? \\\\\\\"#define \\\\\\\" + shadowMapTypeDefine : \\\\\\\"\\\\\\\",\\n parameters.premultipliedAlpha ? \\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.physicallyCorrectLights ? \\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.logarithmicDepthBuffer ? \\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.logarithmicDepthBuffer && parameters.rendererExtensionFragDepth ? \\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\" : \\\\\\\"\\\\\\\",\\n (parameters.extensionShaderTextureLOD || parameters.envMap) && parameters.rendererExtensionShaderTextureLod ? \\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\" : \\\\\\\"\\\\\\\",\\n \\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\n \\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\n \\\\\\\"uniform bool isOrthographic;\\\\\\\",\\n parameters.toneMapping !== NoToneMapping ? \\\\\\\"#define TONE_MAPPING\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.toneMapping !== NoToneMapping ? ShaderChunk[\\\\\\\"tonemapping_pars_fragment\\\\\\\"] : \\\\\\\"\\\\\\\",\\n parameters.toneMapping !== NoToneMapping ? getToneMappingFunction(\\\\\\\"toneMapping\\\\\\\", parameters.toneMapping) : \\\\\\\"\\\\\\\",\\n parameters.dithering ? \\\\\\\"#define DITHERING\\\\\\\" : \\\\\\\"\\\\\\\",\\n parameters.format === RGBFormat ? \\\\\\\"#define OPAQUE\\\\\\\" : \\\\\\\"\\\\\\\",\\n ShaderChunk[\\\\\\\"encodings_pars_fragment\\\\\\\"],\\n parameters.map ? getTexelDecodingFunction(\\\\\\\"mapTexelToLinear\\\\\\\", parameters.mapEncoding) : \\\\\\\"\\\\\\\",\\n parameters.matcap ? getTexelDecodingFunction(\\\\\\\"matcapTexelToLinear\\\\\\\", parameters.matcapEncoding) : \\\\\\\"\\\\\\\",\\n parameters.envMap ? getTexelDecodingFunction(\\\\\\\"envMapTexelToLinear\\\\\\\", parameters.envMapEncoding) : \\\\\\\"\\\\\\\",\\n parameters.emissiveMap ? getTexelDecodingFunction(\\\\\\\"emissiveMapTexelToLinear\\\\\\\", parameters.emissiveMapEncoding) : \\\\\\\"\\\\\\\",\\n parameters.specularTintMap ? getTexelDecodingFunction(\\\\\\\"specularTintMapTexelToLinear\\\\\\\", parameters.specularTintMapEncoding) : \\\\\\\"\\\\\\\",\\n parameters.lightMap ? getTexelDecodingFunction(\\\\\\\"lightMapTexelToLinear\\\\\\\", parameters.lightMapEncoding) : \\\\\\\"\\\\\\\",\\n getTexelEncodingFunction(\\\\\\\"linearToOutputTexel\\\\\\\", parameters.outputEncoding),\\n parameters.depthPacking ? \\\\\\\"#define DEPTH_PACKING \\\\\\\" + parameters.depthPacking : \\\\\\\"\\\\\\\",\\n \\\\\\\"\\\\n\\\\\\\"\\n ].filter(filterEmptyLine).join(\\\\\\\"\\\\n\\\\\\\");\\n }\\n vertexShader = resolveIncludes(vertexShader);\\n vertexShader = replaceLightNums(vertexShader, parameters);\\n vertexShader = replaceClippingPlaneNums(vertexShader, parameters);\\n fragmentShader = resolveIncludes(fragmentShader);\\n fragmentShader = replaceLightNums(fragmentShader, parameters);\\n fragmentShader = replaceClippingPlaneNums(fragmentShader, parameters);\\n vertexShader = unrollLoops(vertexShader);\\n fragmentShader = unrollLoops(fragmentShader);\\n if (parameters.isWebGL2 && parameters.isRawShaderMaterial !== true) {\\n versionString = \\\\\\\"#version 300 es\\\\n\\\\\\\";\\n prefixVertex = [\\n \\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\n \\\\\\\"#define attribute in\\\\\\\",\\n \\\\\\\"#define varying out\\\\\\\",\\n \\\\\\\"#define texture2D texture\\\\\\\"\\n ].join(\\\\\\\"\\\\n\\\\\\\") + \\\\\\\"\\\\n\\\\\\\" + prefixVertex;\\n prefixFragment = [\\n \\\\\\\"#define varying in\\\\\\\",\\n parameters.glslVersion === GLSL3 ? \\\\\\\"\\\\\\\" : \\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",\\n parameters.glslVersion === GLSL3 ? \\\\\\\"\\\\\\\" : \\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\n \\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\n \\\\\\\"#define texture2D texture\\\\\\\",\\n \\\\\\\"#define textureCube texture\\\\\\\",\\n \\\\\\\"#define texture2DProj textureProj\\\\\\\",\\n \\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\n \\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\n \\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\n \\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\n \\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\n \\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"\\n ].join(\\\\\\\"\\\\n\\\\\\\") + \\\\\\\"\\\\n\\\\\\\" + prefixFragment;\\n }\\n const vertexGlsl = versionString + prefixVertex + vertexShader;\\n const fragmentGlsl = versionString + prefixFragment + fragmentShader;\\n const glVertexShader = WebGLShader(gl, gl.VERTEX_SHADER, vertexGlsl);\\n const glFragmentShader = WebGLShader(gl, gl.FRAGMENT_SHADER, fragmentGlsl);\\n gl.attachShader(program, glVertexShader);\\n gl.attachShader(program, glFragmentShader);\\n if (parameters.index0AttributeName !== void 0) {\\n gl.bindAttribLocation(program, 0, parameters.index0AttributeName);\\n } else if (parameters.morphTargets === true) {\\n gl.bindAttribLocation(program, 0, \\\\\\\"position\\\\\\\");\\n }\\n gl.linkProgram(program);\\n if (renderer.debug.checkShaderErrors) {\\n const programLog = gl.getProgramInfoLog(program).trim();\\n const vertexLog = gl.getShaderInfoLog(glVertexShader).trim();\\n const fragmentLog = gl.getShaderInfoLog(glFragmentShader).trim();\\n let runnable = true;\\n let haveDiagnostics = true;\\n if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\\n runnable = false;\\n const vertexErrors = getShaderErrors(gl, glVertexShader, \\\\\\\"vertex\\\\\\\");\\n const fragmentErrors = getShaderErrors(gl, glFragmentShader, \\\\\\\"fragment\\\\\\\");\\n console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\" + gl.getError() + \\\\\\\" - VALIDATE_STATUS \\\\\\\" + gl.getProgramParameter(program, gl.VALIDATE_STATUS) + \\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\" + programLog + \\\\\\\"\\\\n\\\\\\\" + vertexErrors + \\\\\\\"\\\\n\\\\\\\" + fragmentErrors);\\n } else if (programLog !== \\\\\\\"\\\\\\\") {\\n console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\", programLog);\\n } else if (vertexLog === \\\\\\\"\\\\\\\" || fragmentLog === \\\\\\\"\\\\\\\") {\\n haveDiagnostics = false;\\n }\\n if (haveDiagnostics) {\\n this.diagnostics = {\\n runnable,\\n programLog,\\n vertexShader: {\\n log: vertexLog,\\n prefix: prefixVertex\\n },\\n fragmentShader: {\\n log: fragmentLog,\\n prefix: prefixFragment\\n }\\n };\\n }\\n }\\n gl.deleteShader(glVertexShader);\\n gl.deleteShader(glFragmentShader);\\n let cachedUniforms;\\n this.getUniforms = function() {\\n if (cachedUniforms === void 0) {\\n cachedUniforms = new WebGLUniforms(gl, program);\\n }\\n return cachedUniforms;\\n };\\n let cachedAttributes;\\n this.getAttributes = function() {\\n if (cachedAttributes === void 0) {\\n cachedAttributes = fetchAttributeLocations(gl, program);\\n }\\n return cachedAttributes;\\n };\\n this.destroy = function() {\\n bindingStates.releaseStatesOfProgram(this);\\n gl.deleteProgram(program);\\n this.program = void 0;\\n };\\n this.name = parameters.shaderName;\\n this.id = programIdCount++;\\n this.cacheKey = cacheKey;\\n this.usedTimes = 1;\\n this.program = program;\\n this.vertexShader = glVertexShader;\\n this.fragmentShader = glFragmentShader;\\n return this;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLPrograms.js\\nfunction WebGLPrograms(renderer, cubemaps, cubeuvmaps, extensions, capabilities, bindingStates, clipping) {\\n const programs = [];\\n const isWebGL2 = capabilities.isWebGL2;\\n const logarithmicDepthBuffer = capabilities.logarithmicDepthBuffer;\\n const floatVertexTextures = capabilities.floatVertexTextures;\\n const maxVertexUniforms = capabilities.maxVertexUniforms;\\n const vertexTextures = capabilities.vertexTextures;\\n let precision = capabilities.precision;\\n const shaderIDs = {\\n MeshDepthMaterial: \\\\\\\"depth\\\\\\\",\\n MeshDistanceMaterial: \\\\\\\"distanceRGBA\\\\\\\",\\n MeshNormalMaterial: \\\\\\\"normal\\\\\\\",\\n MeshBasicMaterial: \\\\\\\"basic\\\\\\\",\\n MeshLambertMaterial: \\\\\\\"lambert\\\\\\\",\\n MeshPhongMaterial: \\\\\\\"phong\\\\\\\",\\n MeshToonMaterial: \\\\\\\"toon\\\\\\\",\\n MeshStandardMaterial: \\\\\\\"physical\\\\\\\",\\n MeshPhysicalMaterial: \\\\\\\"physical\\\\\\\",\\n MeshMatcapMaterial: \\\\\\\"matcap\\\\\\\",\\n LineBasicMaterial: \\\\\\\"basic\\\\\\\",\\n LineDashedMaterial: \\\\\\\"dashed\\\\\\\",\\n PointsMaterial: \\\\\\\"points\\\\\\\",\\n ShadowMaterial: \\\\\\\"shadow\\\\\\\",\\n SpriteMaterial: \\\\\\\"sprite\\\\\\\"\\n };\\n const parameterNames = [\\n \\\\\\\"precision\\\\\\\",\\n \\\\\\\"isWebGL2\\\\\\\",\\n \\\\\\\"supportsVertexTextures\\\\\\\",\\n \\\\\\\"outputEncoding\\\\\\\",\\n \\\\\\\"instancing\\\\\\\",\\n \\\\\\\"instancingColor\\\\\\\",\\n \\\\\\\"map\\\\\\\",\\n \\\\\\\"mapEncoding\\\\\\\",\\n \\\\\\\"matcap\\\\\\\",\\n \\\\\\\"matcapEncoding\\\\\\\",\\n \\\\\\\"envMap\\\\\\\",\\n \\\\\\\"envMapMode\\\\\\\",\\n \\\\\\\"envMapEncoding\\\\\\\",\\n \\\\\\\"envMapCubeUV\\\\\\\",\\n \\\\\\\"lightMap\\\\\\\",\\n \\\\\\\"lightMapEncoding\\\\\\\",\\n \\\\\\\"aoMap\\\\\\\",\\n \\\\\\\"emissiveMap\\\\\\\",\\n \\\\\\\"emissiveMapEncoding\\\\\\\",\\n \\\\\\\"bumpMap\\\\\\\",\\n \\\\\\\"normalMap\\\\\\\",\\n \\\\\\\"objectSpaceNormalMap\\\\\\\",\\n \\\\\\\"tangentSpaceNormalMap\\\\\\\",\\n \\\\\\\"clearcoat\\\\\\\",\\n \\\\\\\"clearcoatMap\\\\\\\",\\n \\\\\\\"clearcoatRoughnessMap\\\\\\\",\\n \\\\\\\"clearcoatNormalMap\\\\\\\",\\n \\\\\\\"displacementMap\\\\\\\",\\n \\\\\\\"specularMap\\\\\\\",\\n \\\\\\\"specularIntensityMap\\\\\\\",\\n \\\\\\\"specularTintMap\\\\\\\",\\n \\\\\\\"specularTintMapEncoding\\\\\\\",\\n \\\\\\\"roughnessMap\\\\\\\",\\n \\\\\\\"metalnessMap\\\\\\\",\\n \\\\\\\"gradientMap\\\\\\\",\\n \\\\\\\"alphaMap\\\\\\\",\\n \\\\\\\"alphaTest\\\\\\\",\\n \\\\\\\"combine\\\\\\\",\\n \\\\\\\"vertexColors\\\\\\\",\\n \\\\\\\"vertexAlphas\\\\\\\",\\n \\\\\\\"vertexTangents\\\\\\\",\\n \\\\\\\"vertexUvs\\\\\\\",\\n \\\\\\\"uvsVertexOnly\\\\\\\",\\n \\\\\\\"fog\\\\\\\",\\n \\\\\\\"useFog\\\\\\\",\\n \\\\\\\"fogExp2\\\\\\\",\\n \\\\\\\"flatShading\\\\\\\",\\n \\\\\\\"sizeAttenuation\\\\\\\",\\n \\\\\\\"logarithmicDepthBuffer\\\\\\\",\\n \\\\\\\"skinning\\\\\\\",\\n \\\\\\\"maxBones\\\\\\\",\\n \\\\\\\"useVertexTexture\\\\\\\",\\n \\\\\\\"morphTargets\\\\\\\",\\n \\\\\\\"morphNormals\\\\\\\",\\n \\\\\\\"morphTargetsCount\\\\\\\",\\n \\\\\\\"premultipliedAlpha\\\\\\\",\\n \\\\\\\"numDirLights\\\\\\\",\\n \\\\\\\"numPointLights\\\\\\\",\\n \\\\\\\"numSpotLights\\\\\\\",\\n \\\\\\\"numHemiLights\\\\\\\",\\n \\\\\\\"numRectAreaLights\\\\\\\",\\n \\\\\\\"numDirLightShadows\\\\\\\",\\n \\\\\\\"numPointLightShadows\\\\\\\",\\n \\\\\\\"numSpotLightShadows\\\\\\\",\\n \\\\\\\"shadowMapEnabled\\\\\\\",\\n \\\\\\\"shadowMapType\\\\\\\",\\n \\\\\\\"toneMapping\\\\\\\",\\n \\\\\\\"physicallyCorrectLights\\\\\\\",\\n \\\\\\\"doubleSided\\\\\\\",\\n \\\\\\\"flipSided\\\\\\\",\\n \\\\\\\"numClippingPlanes\\\\\\\",\\n \\\\\\\"numClipIntersection\\\\\\\",\\n \\\\\\\"depthPacking\\\\\\\",\\n \\\\\\\"dithering\\\\\\\",\\n \\\\\\\"format\\\\\\\",\\n \\\\\\\"sheen\\\\\\\",\\n \\\\\\\"transmission\\\\\\\",\\n \\\\\\\"transmissionMap\\\\\\\",\\n \\\\\\\"thicknessMap\\\\\\\"\\n ];\\n function getMaxBones(object) {\\n const skeleton = object.skeleton;\\n const bones = skeleton.bones;\\n if (floatVertexTextures) {\\n return 1024;\\n } else {\\n const nVertexUniforms = maxVertexUniforms;\\n const nVertexMatrices = Math.floor((nVertexUniforms - 20) / 4);\\n const maxBones = Math.min(nVertexMatrices, bones.length);\\n if (maxBones < bones.length) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Skeleton has \\\\\\\" + bones.length + \\\\\\\" bones. This GPU supports \\\\\\\" + maxBones + \\\\\\\".\\\\\\\");\\n return 0;\\n }\\n return maxBones;\\n }\\n }\\n function getTextureEncodingFromMap(map) {\\n let encoding;\\n if (map && map.isTexture) {\\n encoding = map.encoding;\\n } else if (map && map.isWebGLRenderTarget) {\\n console.warn(\\\\\\\"THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. Use their .texture property instead.\\\\\\\");\\n encoding = map.texture.encoding;\\n } else {\\n encoding = LinearEncoding;\\n }\\n if (isWebGL2 && map && map.isTexture && map.format === RGBAFormat && map.type === UnsignedByteType && map.encoding === sRGBEncoding) {\\n encoding = LinearEncoding;\\n }\\n return encoding;\\n }\\n function getParameters(material, lights, shadows, scene, object) {\\n const fog = scene.fog;\\n const environment = material.isMeshStandardMaterial ? scene.environment : null;\\n const envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || environment);\\n const shaderID = shaderIDs[material.type];\\n const maxBones = object.isSkinnedMesh ? getMaxBones(object) : 0;\\n if (material.precision !== null) {\\n precision = capabilities.getMaxPrecision(material.precision);\\n if (precision !== material.precision) {\\n console.warn(\\\\\\\"THREE.WebGLProgram.getParameters:\\\\\\\", material.precision, \\\\\\\"not supported, using\\\\\\\", precision, \\\\\\\"instead.\\\\\\\");\\n }\\n }\\n let vertexShader, fragmentShader;\\n if (shaderID) {\\n const shader = ShaderLib[shaderID];\\n vertexShader = shader.vertexShader;\\n fragmentShader = shader.fragmentShader;\\n } else {\\n vertexShader = material.vertexShader;\\n fragmentShader = material.fragmentShader;\\n }\\n const currentRenderTarget = renderer.getRenderTarget();\\n const useAlphaTest = material.alphaTest > 0;\\n const useClearcoat = material.clearcoat > 0;\\n const parameters = {\\n isWebGL2,\\n shaderID,\\n shaderName: material.type,\\n vertexShader,\\n fragmentShader,\\n defines: material.defines,\\n isRawShaderMaterial: material.isRawShaderMaterial === true,\\n glslVersion: material.glslVersion,\\n precision,\\n instancing: object.isInstancedMesh === true,\\n instancingColor: object.isInstancedMesh === true && object.instanceColor !== null,\\n supportsVertexTextures: vertexTextures,\\n outputEncoding: currentRenderTarget !== null ? getTextureEncodingFromMap(currentRenderTarget.texture) : renderer.outputEncoding,\\n map: !!material.map,\\n mapEncoding: getTextureEncodingFromMap(material.map),\\n matcap: !!material.matcap,\\n matcapEncoding: getTextureEncodingFromMap(material.matcap),\\n envMap: !!envMap,\\n envMapMode: envMap && envMap.mapping,\\n envMapEncoding: getTextureEncodingFromMap(envMap),\\n envMapCubeUV: !!envMap && (envMap.mapping === CubeUVReflectionMapping || envMap.mapping === CubeUVRefractionMapping),\\n lightMap: !!material.lightMap,\\n lightMapEncoding: getTextureEncodingFromMap(material.lightMap),\\n aoMap: !!material.aoMap,\\n emissiveMap: !!material.emissiveMap,\\n emissiveMapEncoding: getTextureEncodingFromMap(material.emissiveMap),\\n bumpMap: !!material.bumpMap,\\n normalMap: !!material.normalMap,\\n objectSpaceNormalMap: material.normalMapType === ObjectSpaceNormalMap,\\n tangentSpaceNormalMap: material.normalMapType === TangentSpaceNormalMap,\\n clearcoat: useClearcoat,\\n clearcoatMap: useClearcoat && !!material.clearcoatMap,\\n clearcoatRoughnessMap: useClearcoat && !!material.clearcoatRoughnessMap,\\n clearcoatNormalMap: useClearcoat && !!material.clearcoatNormalMap,\\n displacementMap: !!material.displacementMap,\\n roughnessMap: !!material.roughnessMap,\\n metalnessMap: !!material.metalnessMap,\\n specularMap: !!material.specularMap,\\n specularIntensityMap: !!material.specularIntensityMap,\\n specularTintMap: !!material.specularTintMap,\\n specularTintMapEncoding: getTextureEncodingFromMap(material.specularTintMap),\\n alphaMap: !!material.alphaMap,\\n alphaTest: useAlphaTest,\\n gradientMap: !!material.gradientMap,\\n sheen: material.sheen > 0,\\n transmission: material.transmission > 0,\\n transmissionMap: !!material.transmissionMap,\\n thicknessMap: !!material.thicknessMap,\\n combine: material.combine,\\n vertexTangents: !!material.normalMap && !!object.geometry && !!object.geometry.attributes.tangent,\\n vertexColors: material.vertexColors,\\n vertexAlphas: material.vertexColors === true && !!object.geometry && !!object.geometry.attributes.color && object.geometry.attributes.color.itemSize === 4,\\n vertexUvs: !!material.map || !!material.bumpMap || !!material.normalMap || !!material.specularMap || !!material.alphaMap || !!material.emissiveMap || !!material.roughnessMap || !!material.metalnessMap || !!material.clearcoatMap || !!material.clearcoatRoughnessMap || !!material.clearcoatNormalMap || !!material.displacementMap || !!material.transmissionMap || !!material.thicknessMap || !!material.specularIntensityMap || !!material.specularTintMap,\\n uvsVertexOnly: !(!!material.map || !!material.bumpMap || !!material.normalMap || !!material.specularMap || !!material.alphaMap || !!material.emissiveMap || !!material.roughnessMap || !!material.metalnessMap || !!material.clearcoatNormalMap || material.transmission > 0 || !!material.transmissionMap || !!material.thicknessMap || !!material.specularIntensityMap || !!material.specularTintMap) && !!material.displacementMap,\\n fog: !!fog,\\n useFog: material.fog,\\n fogExp2: fog && fog.isFogExp2,\\n flatShading: !!material.flatShading,\\n sizeAttenuation: material.sizeAttenuation,\\n logarithmicDepthBuffer,\\n skinning: object.isSkinnedMesh === true && maxBones > 0,\\n maxBones,\\n useVertexTexture: floatVertexTextures,\\n morphTargets: !!object.geometry && !!object.geometry.morphAttributes.position,\\n morphNormals: !!object.geometry && !!object.geometry.morphAttributes.normal,\\n morphTargetsCount: !!object.geometry && !!object.geometry.morphAttributes.position ? object.geometry.morphAttributes.position.length : 0,\\n numDirLights: lights.directional.length,\\n numPointLights: lights.point.length,\\n numSpotLights: lights.spot.length,\\n numRectAreaLights: lights.rectArea.length,\\n numHemiLights: lights.hemi.length,\\n numDirLightShadows: lights.directionalShadowMap.length,\\n numPointLightShadows: lights.pointShadowMap.length,\\n numSpotLightShadows: lights.spotShadowMap.length,\\n numClippingPlanes: clipping.numPlanes,\\n numClipIntersection: clipping.numIntersection,\\n format: material.format,\\n dithering: material.dithering,\\n shadowMapEnabled: renderer.shadowMap.enabled && shadows.length > 0,\\n shadowMapType: renderer.shadowMap.type,\\n toneMapping: material.toneMapped ? renderer.toneMapping : NoToneMapping,\\n physicallyCorrectLights: renderer.physicallyCorrectLights,\\n premultipliedAlpha: material.premultipliedAlpha,\\n doubleSided: material.side === DoubleSide,\\n flipSided: material.side === BackSide,\\n depthPacking: material.depthPacking !== void 0 ? material.depthPacking : false,\\n index0AttributeName: material.index0AttributeName,\\n extensionDerivatives: material.extensions && material.extensions.derivatives,\\n extensionFragDepth: material.extensions && material.extensions.fragDepth,\\n extensionDrawBuffers: material.extensions && material.extensions.drawBuffers,\\n extensionShaderTextureLOD: material.extensions && material.extensions.shaderTextureLOD,\\n rendererExtensionFragDepth: isWebGL2 || extensions.has(\\\\\\\"EXT_frag_depth\\\\\\\"),\\n rendererExtensionDrawBuffers: isWebGL2 || extensions.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),\\n rendererExtensionShaderTextureLod: isWebGL2 || extensions.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),\\n customProgramCacheKey: material.customProgramCacheKey()\\n };\\n return parameters;\\n }\\n function getProgramCacheKey(parameters) {\\n const array = [];\\n if (parameters.shaderID) {\\n array.push(parameters.shaderID);\\n } else {\\n array.push(parameters.fragmentShader);\\n array.push(parameters.vertexShader);\\n }\\n if (parameters.defines !== void 0) {\\n for (const name in parameters.defines) {\\n array.push(name);\\n array.push(parameters.defines[name]);\\n }\\n }\\n if (parameters.isRawShaderMaterial === false) {\\n for (let i = 0; i < parameterNames.length; i++) {\\n array.push(parameters[parameterNames[i]]);\\n }\\n array.push(renderer.outputEncoding);\\n array.push(renderer.gammaFactor);\\n }\\n array.push(parameters.customProgramCacheKey);\\n return array.join();\\n }\\n function getUniforms(material) {\\n const shaderID = shaderIDs[material.type];\\n let uniforms;\\n if (shaderID) {\\n const shader = ShaderLib[shaderID];\\n uniforms = UniformsUtils.clone(shader.uniforms);\\n } else {\\n uniforms = material.uniforms;\\n }\\n return uniforms;\\n }\\n function acquireProgram(parameters, cacheKey) {\\n let program;\\n for (let p = 0, pl = programs.length; p < pl; p++) {\\n const preexistingProgram = programs[p];\\n if (preexistingProgram.cacheKey === cacheKey) {\\n program = preexistingProgram;\\n ++program.usedTimes;\\n break;\\n }\\n }\\n if (program === void 0) {\\n program = new WebGLProgram(renderer, cacheKey, parameters, bindingStates);\\n programs.push(program);\\n }\\n return program;\\n }\\n function releaseProgram(program) {\\n if (--program.usedTimes === 0) {\\n const i = programs.indexOf(program);\\n programs[i] = programs[programs.length - 1];\\n programs.pop();\\n program.destroy();\\n }\\n }\\n return {\\n getParameters,\\n getProgramCacheKey,\\n getUniforms,\\n acquireProgram,\\n releaseProgram,\\n programs\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLProperties.js\\nfunction WebGLProperties() {\\n let properties = new WeakMap();\\n function get(object) {\\n let map = properties.get(object);\\n if (map === void 0) {\\n map = {};\\n properties.set(object, map);\\n }\\n return map;\\n }\\n function remove(object) {\\n properties.delete(object);\\n }\\n function update(object, key, value) {\\n properties.get(object)[key] = value;\\n }\\n function dispose() {\\n properties = new WeakMap();\\n }\\n return {\\n get,\\n remove,\\n update,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLRenderLists.js\\nfunction painterSortStable(a, b) {\\n if (a.groupOrder !== b.groupOrder) {\\n return a.groupOrder - b.groupOrder;\\n } else if (a.renderOrder !== b.renderOrder) {\\n return a.renderOrder - b.renderOrder;\\n } else if (a.program !== b.program) {\\n return a.program.id - b.program.id;\\n } else if (a.material.id !== b.material.id) {\\n return a.material.id - b.material.id;\\n } else if (a.z !== b.z) {\\n return a.z - b.z;\\n } else {\\n return a.id - b.id;\\n }\\n}\\nfunction reversePainterSortStable(a, b) {\\n if (a.groupOrder !== b.groupOrder) {\\n return a.groupOrder - b.groupOrder;\\n } else if (a.renderOrder !== b.renderOrder) {\\n return a.renderOrder - b.renderOrder;\\n } else if (a.z !== b.z) {\\n return b.z - a.z;\\n } else {\\n return a.id - b.id;\\n }\\n}\\nfunction WebGLRenderList(properties) {\\n const renderItems = [];\\n let renderItemsIndex = 0;\\n const opaque = [];\\n const transmissive = [];\\n const transparent = [];\\n const defaultProgram = {id: -1};\\n function init() {\\n renderItemsIndex = 0;\\n opaque.length = 0;\\n transmissive.length = 0;\\n transparent.length = 0;\\n }\\n function getNextRenderItem(object, geometry, material, groupOrder, z, group) {\\n let renderItem = renderItems[renderItemsIndex];\\n const materialProperties = properties.get(material);\\n if (renderItem === void 0) {\\n renderItem = {\\n id: object.id,\\n object,\\n geometry,\\n material,\\n program: materialProperties.program || defaultProgram,\\n groupOrder,\\n renderOrder: object.renderOrder,\\n z,\\n group\\n };\\n renderItems[renderItemsIndex] = renderItem;\\n } else {\\n renderItem.id = object.id;\\n renderItem.object = object;\\n renderItem.geometry = geometry;\\n renderItem.material = material;\\n renderItem.program = materialProperties.program || defaultProgram;\\n renderItem.groupOrder = groupOrder;\\n renderItem.renderOrder = object.renderOrder;\\n renderItem.z = z;\\n renderItem.group = group;\\n }\\n renderItemsIndex++;\\n return renderItem;\\n }\\n function push(object, geometry, material, groupOrder, z, group) {\\n const renderItem = getNextRenderItem(object, geometry, material, groupOrder, z, group);\\n if (material.transmission > 0) {\\n transmissive.push(renderItem);\\n } else if (material.transparent === true) {\\n transparent.push(renderItem);\\n } else {\\n opaque.push(renderItem);\\n }\\n }\\n function unshift(object, geometry, material, groupOrder, z, group) {\\n const renderItem = getNextRenderItem(object, geometry, material, groupOrder, z, group);\\n if (material.transmission > 0) {\\n transmissive.unshift(renderItem);\\n } else if (material.transparent === true) {\\n transparent.unshift(renderItem);\\n } else {\\n opaque.unshift(renderItem);\\n }\\n }\\n function sort(customOpaqueSort, customTransparentSort) {\\n if (opaque.length > 1)\\n opaque.sort(customOpaqueSort || painterSortStable);\\n if (transmissive.length > 1)\\n transmissive.sort(customTransparentSort || reversePainterSortStable);\\n if (transparent.length > 1)\\n transparent.sort(customTransparentSort || reversePainterSortStable);\\n }\\n function finish() {\\n for (let i = renderItemsIndex, il = renderItems.length; i < il; i++) {\\n const renderItem = renderItems[i];\\n if (renderItem.id === null)\\n break;\\n renderItem.id = null;\\n renderItem.object = null;\\n renderItem.geometry = null;\\n renderItem.material = null;\\n renderItem.program = null;\\n renderItem.group = null;\\n }\\n }\\n return {\\n opaque,\\n transmissive,\\n transparent,\\n init,\\n push,\\n unshift,\\n finish,\\n sort\\n };\\n}\\nfunction WebGLRenderLists(properties) {\\n let lists = new WeakMap();\\n function get(scene, renderCallDepth) {\\n let list;\\n if (lists.has(scene) === false) {\\n list = new WebGLRenderList(properties);\\n lists.set(scene, [list]);\\n } else {\\n if (renderCallDepth >= lists.get(scene).length) {\\n list = new WebGLRenderList(properties);\\n lists.get(scene).push(list);\\n } else {\\n list = lists.get(scene)[renderCallDepth];\\n }\\n }\\n return list;\\n }\\n function dispose() {\\n lists = new WeakMap();\\n }\\n return {\\n get,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLLights.js\\nfunction UniformsCache() {\\n const lights = {};\\n return {\\n get: function(light) {\\n if (lights[light.id] !== void 0) {\\n return lights[light.id];\\n }\\n let uniforms;\\n switch (light.type) {\\n case \\\\\\\"DirectionalLight\\\\\\\":\\n uniforms = {\\n direction: new Vector3(),\\n color: new Color()\\n };\\n break;\\n case \\\\\\\"SpotLight\\\\\\\":\\n uniforms = {\\n position: new Vector3(),\\n direction: new Vector3(),\\n color: new Color(),\\n distance: 0,\\n coneCos: 0,\\n penumbraCos: 0,\\n decay: 0\\n };\\n break;\\n case \\\\\\\"PointLight\\\\\\\":\\n uniforms = {\\n position: new Vector3(),\\n color: new Color(),\\n distance: 0,\\n decay: 0\\n };\\n break;\\n case \\\\\\\"HemisphereLight\\\\\\\":\\n uniforms = {\\n direction: new Vector3(),\\n skyColor: new Color(),\\n groundColor: new Color()\\n };\\n break;\\n case \\\\\\\"RectAreaLight\\\\\\\":\\n uniforms = {\\n color: new Color(),\\n position: new Vector3(),\\n halfWidth: new Vector3(),\\n halfHeight: new Vector3()\\n };\\n break;\\n }\\n lights[light.id] = uniforms;\\n return uniforms;\\n }\\n };\\n}\\nfunction ShadowUniformsCache() {\\n const lights = {};\\n return {\\n get: function(light) {\\n if (lights[light.id] !== void 0) {\\n return lights[light.id];\\n }\\n let uniforms;\\n switch (light.type) {\\n case \\\\\\\"DirectionalLight\\\\\\\":\\n uniforms = {\\n shadowBias: 0,\\n shadowNormalBias: 0,\\n shadowRadius: 1,\\n shadowMapSize: new Vector2()\\n };\\n break;\\n case \\\\\\\"SpotLight\\\\\\\":\\n uniforms = {\\n shadowBias: 0,\\n shadowNormalBias: 0,\\n shadowRadius: 1,\\n shadowMapSize: new Vector2()\\n };\\n break;\\n case \\\\\\\"PointLight\\\\\\\":\\n uniforms = {\\n shadowBias: 0,\\n shadowNormalBias: 0,\\n shadowRadius: 1,\\n shadowMapSize: new Vector2(),\\n shadowCameraNear: 1,\\n shadowCameraFar: 1e3\\n };\\n break;\\n }\\n lights[light.id] = uniforms;\\n return uniforms;\\n }\\n };\\n}\\nvar nextVersion = 0;\\nfunction shadowCastingLightsFirst(lightA, lightB) {\\n return (lightB.castShadow ? 1 : 0) - (lightA.castShadow ? 1 : 0);\\n}\\nfunction WebGLLights(extensions, capabilities) {\\n const cache = new UniformsCache();\\n const shadowCache = ShadowUniformsCache();\\n const state = {\\n version: 0,\\n hash: {\\n directionalLength: -1,\\n pointLength: -1,\\n spotLength: -1,\\n rectAreaLength: -1,\\n hemiLength: -1,\\n numDirectionalShadows: -1,\\n numPointShadows: -1,\\n numSpotShadows: -1\\n },\\n ambient: [0, 0, 0],\\n probe: [],\\n directional: [],\\n directionalShadow: [],\\n directionalShadowMap: [],\\n directionalShadowMatrix: [],\\n spot: [],\\n spotShadow: [],\\n spotShadowMap: [],\\n spotShadowMatrix: [],\\n rectArea: [],\\n rectAreaLTC1: null,\\n rectAreaLTC2: null,\\n point: [],\\n pointShadow: [],\\n pointShadowMap: [],\\n pointShadowMatrix: [],\\n hemi: []\\n };\\n for (let i = 0; i < 9; i++)\\n state.probe.push(new Vector3());\\n const vector3 = new Vector3();\\n const matrix4 = new Matrix4();\\n const matrix42 = new Matrix4();\\n function setup(lights, physicallyCorrectLights) {\\n let r = 0, g = 0, b = 0;\\n for (let i = 0; i < 9; i++)\\n state.probe[i].set(0, 0, 0);\\n let directionalLength = 0;\\n let pointLength = 0;\\n let spotLength = 0;\\n let rectAreaLength = 0;\\n let hemiLength = 0;\\n let numDirectionalShadows = 0;\\n let numPointShadows = 0;\\n let numSpotShadows = 0;\\n lights.sort(shadowCastingLightsFirst);\\n const scaleFactor = physicallyCorrectLights !== true ? Math.PI : 1;\\n for (let i = 0, l = lights.length; i < l; i++) {\\n const light = lights[i];\\n const color = light.color;\\n const intensity = light.intensity;\\n const distance = light.distance;\\n const shadowMap = light.shadow && light.shadow.map ? light.shadow.map.texture : null;\\n if (light.isAmbientLight) {\\n r += color.r * intensity * scaleFactor;\\n g += color.g * intensity * scaleFactor;\\n b += color.b * intensity * scaleFactor;\\n } else if (light.isLightProbe) {\\n for (let j = 0; j < 9; j++) {\\n state.probe[j].addScaledVector(light.sh.coefficients[j], intensity);\\n }\\n } else if (light.isDirectionalLight) {\\n const uniforms = cache.get(light);\\n uniforms.color.copy(light.color).multiplyScalar(light.intensity * scaleFactor);\\n if (light.castShadow) {\\n const shadow2 = light.shadow;\\n const shadowUniforms = shadowCache.get(light);\\n shadowUniforms.shadowBias = shadow2.bias;\\n shadowUniforms.shadowNormalBias = shadow2.normalBias;\\n shadowUniforms.shadowRadius = shadow2.radius;\\n shadowUniforms.shadowMapSize = shadow2.mapSize;\\n state.directionalShadow[directionalLength] = shadowUniforms;\\n state.directionalShadowMap[directionalLength] = shadowMap;\\n state.directionalShadowMatrix[directionalLength] = light.shadow.matrix;\\n numDirectionalShadows++;\\n }\\n state.directional[directionalLength] = uniforms;\\n directionalLength++;\\n } else if (light.isSpotLight) {\\n const uniforms = cache.get(light);\\n uniforms.position.setFromMatrixPosition(light.matrixWorld);\\n uniforms.color.copy(color).multiplyScalar(intensity * scaleFactor);\\n uniforms.distance = distance;\\n uniforms.coneCos = Math.cos(light.angle);\\n uniforms.penumbraCos = Math.cos(light.angle * (1 - light.penumbra));\\n uniforms.decay = light.decay;\\n if (light.castShadow) {\\n const shadow2 = light.shadow;\\n const shadowUniforms = shadowCache.get(light);\\n shadowUniforms.shadowBias = shadow2.bias;\\n shadowUniforms.shadowNormalBias = shadow2.normalBias;\\n shadowUniforms.shadowRadius = shadow2.radius;\\n shadowUniforms.shadowMapSize = shadow2.mapSize;\\n state.spotShadow[spotLength] = shadowUniforms;\\n state.spotShadowMap[spotLength] = shadowMap;\\n state.spotShadowMatrix[spotLength] = light.shadow.matrix;\\n numSpotShadows++;\\n }\\n state.spot[spotLength] = uniforms;\\n spotLength++;\\n } else if (light.isRectAreaLight) {\\n const uniforms = cache.get(light);\\n uniforms.color.copy(color).multiplyScalar(intensity);\\n uniforms.halfWidth.set(light.width * 0.5, 0, 0);\\n uniforms.halfHeight.set(0, light.height * 0.5, 0);\\n state.rectArea[rectAreaLength] = uniforms;\\n rectAreaLength++;\\n } else if (light.isPointLight) {\\n const uniforms = cache.get(light);\\n uniforms.color.copy(light.color).multiplyScalar(light.intensity * scaleFactor);\\n uniforms.distance = light.distance;\\n uniforms.decay = light.decay;\\n if (light.castShadow) {\\n const shadow2 = light.shadow;\\n const shadowUniforms = shadowCache.get(light);\\n shadowUniforms.shadowBias = shadow2.bias;\\n shadowUniforms.shadowNormalBias = shadow2.normalBias;\\n shadowUniforms.shadowRadius = shadow2.radius;\\n shadowUniforms.shadowMapSize = shadow2.mapSize;\\n shadowUniforms.shadowCameraNear = shadow2.camera.near;\\n shadowUniforms.shadowCameraFar = shadow2.camera.far;\\n state.pointShadow[pointLength] = shadowUniforms;\\n state.pointShadowMap[pointLength] = shadowMap;\\n state.pointShadowMatrix[pointLength] = light.shadow.matrix;\\n numPointShadows++;\\n }\\n state.point[pointLength] = uniforms;\\n pointLength++;\\n } else if (light.isHemisphereLight) {\\n const uniforms = cache.get(light);\\n uniforms.skyColor.copy(light.color).multiplyScalar(intensity * scaleFactor);\\n uniforms.groundColor.copy(light.groundColor).multiplyScalar(intensity * scaleFactor);\\n state.hemi[hemiLength] = uniforms;\\n hemiLength++;\\n }\\n }\\n if (rectAreaLength > 0) {\\n if (capabilities.isWebGL2) {\\n state.rectAreaLTC1 = UniformsLib.LTC_FLOAT_1;\\n state.rectAreaLTC2 = UniformsLib.LTC_FLOAT_2;\\n } else {\\n if (extensions.has(\\\\\\\"OES_texture_float_linear\\\\\\\") === true) {\\n state.rectAreaLTC1 = UniformsLib.LTC_FLOAT_1;\\n state.rectAreaLTC2 = UniformsLib.LTC_FLOAT_2;\\n } else if (extensions.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\") === true) {\\n state.rectAreaLTC1 = UniformsLib.LTC_HALF_1;\\n state.rectAreaLTC2 = UniformsLib.LTC_HALF_2;\\n } else {\\n console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\");\\n }\\n }\\n }\\n state.ambient[0] = r;\\n state.ambient[1] = g;\\n state.ambient[2] = b;\\n const hash = state.hash;\\n if (hash.directionalLength !== directionalLength || hash.pointLength !== pointLength || hash.spotLength !== spotLength || hash.rectAreaLength !== rectAreaLength || hash.hemiLength !== hemiLength || hash.numDirectionalShadows !== numDirectionalShadows || hash.numPointShadows !== numPointShadows || hash.numSpotShadows !== numSpotShadows) {\\n state.directional.length = directionalLength;\\n state.spot.length = spotLength;\\n state.rectArea.length = rectAreaLength;\\n state.point.length = pointLength;\\n state.hemi.length = hemiLength;\\n state.directionalShadow.length = numDirectionalShadows;\\n state.directionalShadowMap.length = numDirectionalShadows;\\n state.pointShadow.length = numPointShadows;\\n state.pointShadowMap.length = numPointShadows;\\n state.spotShadow.length = numSpotShadows;\\n state.spotShadowMap.length = numSpotShadows;\\n state.directionalShadowMatrix.length = numDirectionalShadows;\\n state.pointShadowMatrix.length = numPointShadows;\\n state.spotShadowMatrix.length = numSpotShadows;\\n hash.directionalLength = directionalLength;\\n hash.pointLength = pointLength;\\n hash.spotLength = spotLength;\\n hash.rectAreaLength = rectAreaLength;\\n hash.hemiLength = hemiLength;\\n hash.numDirectionalShadows = numDirectionalShadows;\\n hash.numPointShadows = numPointShadows;\\n hash.numSpotShadows = numSpotShadows;\\n state.version = nextVersion++;\\n }\\n }\\n function setupView(lights, camera) {\\n let directionalLength = 0;\\n let pointLength = 0;\\n let spotLength = 0;\\n let rectAreaLength = 0;\\n let hemiLength = 0;\\n const viewMatrix = camera.matrixWorldInverse;\\n for (let i = 0, l = lights.length; i < l; i++) {\\n const light = lights[i];\\n if (light.isDirectionalLight) {\\n const uniforms = state.directional[directionalLength];\\n uniforms.direction.setFromMatrixPosition(light.matrixWorld);\\n vector3.setFromMatrixPosition(light.target.matrixWorld);\\n uniforms.direction.sub(vector3);\\n uniforms.direction.transformDirection(viewMatrix);\\n directionalLength++;\\n } else if (light.isSpotLight) {\\n const uniforms = state.spot[spotLength];\\n uniforms.position.setFromMatrixPosition(light.matrixWorld);\\n uniforms.position.applyMatrix4(viewMatrix);\\n uniforms.direction.setFromMatrixPosition(light.matrixWorld);\\n vector3.setFromMatrixPosition(light.target.matrixWorld);\\n uniforms.direction.sub(vector3);\\n uniforms.direction.transformDirection(viewMatrix);\\n spotLength++;\\n } else if (light.isRectAreaLight) {\\n const uniforms = state.rectArea[rectAreaLength];\\n uniforms.position.setFromMatrixPosition(light.matrixWorld);\\n uniforms.position.applyMatrix4(viewMatrix);\\n matrix42.identity();\\n matrix4.copy(light.matrixWorld);\\n matrix4.premultiply(viewMatrix);\\n matrix42.extractRotation(matrix4);\\n uniforms.halfWidth.set(light.width * 0.5, 0, 0);\\n uniforms.halfHeight.set(0, light.height * 0.5, 0);\\n uniforms.halfWidth.applyMatrix4(matrix42);\\n uniforms.halfHeight.applyMatrix4(matrix42);\\n rectAreaLength++;\\n } else if (light.isPointLight) {\\n const uniforms = state.point[pointLength];\\n uniforms.position.setFromMatrixPosition(light.matrixWorld);\\n uniforms.position.applyMatrix4(viewMatrix);\\n pointLength++;\\n } else if (light.isHemisphereLight) {\\n const uniforms = state.hemi[hemiLength];\\n uniforms.direction.setFromMatrixPosition(light.matrixWorld);\\n uniforms.direction.transformDirection(viewMatrix);\\n uniforms.direction.normalize();\\n hemiLength++;\\n }\\n }\\n }\\n return {\\n setup,\\n setupView,\\n state\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLRenderStates.js\\nfunction WebGLRenderState(extensions, capabilities) {\\n const lights = new WebGLLights(extensions, capabilities);\\n const lightsArray = [];\\n const shadowsArray = [];\\n function init() {\\n lightsArray.length = 0;\\n shadowsArray.length = 0;\\n }\\n function pushLight(light) {\\n lightsArray.push(light);\\n }\\n function pushShadow(shadowLight) {\\n shadowsArray.push(shadowLight);\\n }\\n function setupLights(physicallyCorrectLights) {\\n lights.setup(lightsArray, physicallyCorrectLights);\\n }\\n function setupLightsView(camera) {\\n lights.setupView(lightsArray, camera);\\n }\\n const state = {\\n lightsArray,\\n shadowsArray,\\n lights\\n };\\n return {\\n init,\\n state,\\n setupLights,\\n setupLightsView,\\n pushLight,\\n pushShadow\\n };\\n}\\nfunction WebGLRenderStates(extensions, capabilities) {\\n let renderStates = new WeakMap();\\n function get(scene, renderCallDepth = 0) {\\n let renderState;\\n if (renderStates.has(scene) === false) {\\n renderState = new WebGLRenderState(extensions, capabilities);\\n renderStates.set(scene, [renderState]);\\n } else {\\n if (renderCallDepth >= renderStates.get(scene).length) {\\n renderState = new WebGLRenderState(extensions, capabilities);\\n renderStates.get(scene).push(renderState);\\n } else {\\n renderState = renderStates.get(scene)[renderCallDepth];\\n }\\n }\\n return renderState;\\n }\\n function dispose() {\\n renderStates = new WeakMap();\\n }\\n return {\\n get,\\n dispose\\n };\\n}\\n\\n// ../../../node_modules/three/src/materials/MeshDepthMaterial.js\\nvar MeshDepthMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"MeshDepthMaterial\\\\\\\";\\n this.depthPacking = BasicDepthPacking;\\n this.map = null;\\n this.alphaMap = null;\\n this.displacementMap = null;\\n this.displacementScale = 1;\\n this.displacementBias = 0;\\n this.wireframe = false;\\n this.wireframeLinewidth = 1;\\n this.fog = false;\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.depthPacking = source.depthPacking;\\n this.map = source.map;\\n this.alphaMap = source.alphaMap;\\n this.displacementMap = source.displacementMap;\\n this.displacementScale = source.displacementScale;\\n this.displacementBias = source.displacementBias;\\n this.wireframe = source.wireframe;\\n this.wireframeLinewidth = source.wireframeLinewidth;\\n return this;\\n }\\n};\\nMeshDepthMaterial.prototype.isMeshDepthMaterial = true;\\n\\n// ../../../node_modules/three/src/materials/MeshDistanceMaterial.js\\nvar MeshDistanceMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"MeshDistanceMaterial\\\\\\\";\\n this.referencePosition = new Vector3();\\n this.nearDistance = 1;\\n this.farDistance = 1e3;\\n this.map = null;\\n this.alphaMap = null;\\n this.displacementMap = null;\\n this.displacementScale = 1;\\n this.displacementBias = 0;\\n this.fog = false;\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.referencePosition.copy(source.referencePosition);\\n this.nearDistance = source.nearDistance;\\n this.farDistance = source.farDistance;\\n this.map = source.map;\\n this.alphaMap = source.alphaMap;\\n this.displacementMap = source.displacementMap;\\n this.displacementScale = source.displacementScale;\\n this.displacementBias = source.displacementBias;\\n return this;\\n }\\n};\\nMeshDistanceMaterial.prototype.isMeshDistanceMaterial = true;\\n\\n// ../../../node_modules/three/src/renderers/shaders/ShaderLib/vsm.glsl.js\\nvar vertex17 = `\\nvoid main() {\\n\\n\\tgl_Position = vec4( position, 1.0 );\\n\\n}\\n`;\\nvar fragment17 = `\\nuniform sampler2D shadow_pass;\\nuniform vec2 resolution;\\nuniform float radius;\\nuniform float samples;\\n\\n#include \\n\\nvoid main() {\\n\\n\\tfloat mean = 0.0;\\n\\tfloat squared_mean = 0.0;\\n\\n\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\n\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\n\\n\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\n\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\n\\tfor ( float i = 0.0; i < samples; i ++ ) {\\n\\n\\t\\tfloat uvOffset = uvStart + i * uvStride;\\n\\n\\t\\t#ifdef HORIZONTAL_PASS\\n\\n\\t\\t\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\n\\t\\t\\tmean += distribution.x;\\n\\t\\t\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\n\\n\\t\\t#else\\n\\n\\t\\t\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\n\\t\\t\\tmean += depth;\\n\\t\\t\\tsquared_mean += depth * depth;\\n\\n\\t\\t#endif\\n\\n\\t}\\n\\n\\tmean = mean / samples;\\n\\tsquared_mean = squared_mean / samples;\\n\\n\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\n\\n\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\n\\n}\\n`;\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLShadowMap.js\\nfunction WebGLShadowMap(_renderer, _objects, _capabilities) {\\n let _frustum = new Frustum();\\n const _shadowMapSize = new Vector2(), _viewportSize = new Vector2(), _viewport = new Vector4(), _depthMaterial = new MeshDepthMaterial({depthPacking: RGBADepthPacking}), _distanceMaterial = new MeshDistanceMaterial(), _materialCache = {}, _maxTextureSize = _capabilities.maxTextureSize;\\n const shadowSide = {0: BackSide, 1: FrontSide, 2: DoubleSide};\\n const shadowMaterialVertical = new ShaderMaterial({\\n uniforms: {\\n shadow_pass: {value: null},\\n resolution: {value: new Vector2()},\\n radius: {value: 4},\\n samples: {value: 8}\\n },\\n vertexShader: vertex17,\\n fragmentShader: fragment17\\n });\\n const shadowMaterialHorizontal = shadowMaterialVertical.clone();\\n shadowMaterialHorizontal.defines.HORIZONTAL_PASS = 1;\\n const fullScreenTri = new BufferGeometry();\\n fullScreenTri.setAttribute(\\\\\\\"position\\\\\\\", new BufferAttribute(new Float32Array([-1, -1, 0.5, 3, -1, 0.5, -1, 3, 0.5]), 3));\\n const fullScreenMesh = new Mesh(fullScreenTri, shadowMaterialVertical);\\n const scope = this;\\n this.enabled = false;\\n this.autoUpdate = true;\\n this.needsUpdate = false;\\n this.type = PCFShadowMap;\\n this.render = function(lights, scene, camera) {\\n if (scope.enabled === false)\\n return;\\n if (scope.autoUpdate === false && scope.needsUpdate === false)\\n return;\\n if (lights.length === 0)\\n return;\\n const currentRenderTarget = _renderer.getRenderTarget();\\n const activeCubeFace = _renderer.getActiveCubeFace();\\n const activeMipmapLevel = _renderer.getActiveMipmapLevel();\\n const _state = _renderer.state;\\n _state.setBlending(NoBlending);\\n _state.buffers.color.setClear(1, 1, 1, 1);\\n _state.buffers.depth.setTest(true);\\n _state.setScissorTest(false);\\n for (let i = 0, il = lights.length; i < il; i++) {\\n const light = lights[i];\\n const shadow2 = light.shadow;\\n if (shadow2 === void 0) {\\n console.warn(\\\\\\\"THREE.WebGLShadowMap:\\\\\\\", light, \\\\\\\"has no shadow.\\\\\\\");\\n continue;\\n }\\n if (shadow2.autoUpdate === false && shadow2.needsUpdate === false)\\n continue;\\n _shadowMapSize.copy(shadow2.mapSize);\\n const shadowFrameExtents = shadow2.getFrameExtents();\\n _shadowMapSize.multiply(shadowFrameExtents);\\n _viewportSize.copy(shadow2.mapSize);\\n if (_shadowMapSize.x > _maxTextureSize || _shadowMapSize.y > _maxTextureSize) {\\n if (_shadowMapSize.x > _maxTextureSize) {\\n _viewportSize.x = Math.floor(_maxTextureSize / shadowFrameExtents.x);\\n _shadowMapSize.x = _viewportSize.x * shadowFrameExtents.x;\\n shadow2.mapSize.x = _viewportSize.x;\\n }\\n if (_shadowMapSize.y > _maxTextureSize) {\\n _viewportSize.y = Math.floor(_maxTextureSize / shadowFrameExtents.y);\\n _shadowMapSize.y = _viewportSize.y * shadowFrameExtents.y;\\n shadow2.mapSize.y = _viewportSize.y;\\n }\\n }\\n if (shadow2.map === null && !shadow2.isPointLightShadow && this.type === VSMShadowMap) {\\n const pars = {minFilter: LinearFilter, magFilter: LinearFilter, format: RGBAFormat};\\n shadow2.map = new WebGLRenderTarget(_shadowMapSize.x, _shadowMapSize.y, pars);\\n shadow2.map.texture.name = light.name + \\\\\\\".shadowMap\\\\\\\";\\n shadow2.mapPass = new WebGLRenderTarget(_shadowMapSize.x, _shadowMapSize.y, pars);\\n shadow2.camera.updateProjectionMatrix();\\n }\\n if (shadow2.map === null) {\\n const pars = {minFilter: NearestFilter, magFilter: NearestFilter, format: RGBAFormat};\\n shadow2.map = new WebGLRenderTarget(_shadowMapSize.x, _shadowMapSize.y, pars);\\n shadow2.map.texture.name = light.name + \\\\\\\".shadowMap\\\\\\\";\\n shadow2.camera.updateProjectionMatrix();\\n }\\n _renderer.setRenderTarget(shadow2.map);\\n _renderer.clear();\\n const viewportCount = shadow2.getViewportCount();\\n for (let vp = 0; vp < viewportCount; vp++) {\\n const viewport = shadow2.getViewport(vp);\\n _viewport.set(_viewportSize.x * viewport.x, _viewportSize.y * viewport.y, _viewportSize.x * viewport.z, _viewportSize.y * viewport.w);\\n _state.viewport(_viewport);\\n shadow2.updateMatrices(light, vp);\\n _frustum = shadow2.getFrustum();\\n renderObject(scene, camera, shadow2.camera, light, this.type);\\n }\\n if (!shadow2.isPointLightShadow && this.type === VSMShadowMap) {\\n VSMPass(shadow2, camera);\\n }\\n shadow2.needsUpdate = false;\\n }\\n scope.needsUpdate = false;\\n _renderer.setRenderTarget(currentRenderTarget, activeCubeFace, activeMipmapLevel);\\n };\\n function VSMPass(shadow2, camera) {\\n const geometry = _objects.update(fullScreenMesh);\\n shadowMaterialVertical.uniforms.shadow_pass.value = shadow2.map.texture;\\n shadowMaterialVertical.uniforms.resolution.value = shadow2.mapSize;\\n shadowMaterialVertical.uniforms.radius.value = shadow2.radius;\\n shadowMaterialVertical.uniforms.samples.value = shadow2.blurSamples;\\n _renderer.setRenderTarget(shadow2.mapPass);\\n _renderer.clear();\\n _renderer.renderBufferDirect(camera, null, geometry, shadowMaterialVertical, fullScreenMesh, null);\\n shadowMaterialHorizontal.uniforms.shadow_pass.value = shadow2.mapPass.texture;\\n shadowMaterialHorizontal.uniforms.resolution.value = shadow2.mapSize;\\n shadowMaterialHorizontal.uniforms.radius.value = shadow2.radius;\\n shadowMaterialHorizontal.uniforms.samples.value = shadow2.blurSamples;\\n _renderer.setRenderTarget(shadow2.map);\\n _renderer.clear();\\n _renderer.renderBufferDirect(camera, null, geometry, shadowMaterialHorizontal, fullScreenMesh, null);\\n }\\n function getDepthMaterial(object, geometry, material, light, shadowCameraNear, shadowCameraFar, type) {\\n let result = null;\\n const customMaterial = light.isPointLight === true ? object.customDistanceMaterial : object.customDepthMaterial;\\n if (customMaterial !== void 0) {\\n result = customMaterial;\\n } else {\\n result = light.isPointLight === true ? _distanceMaterial : _depthMaterial;\\n }\\n if (_renderer.localClippingEnabled && material.clipShadows === true && material.clippingPlanes.length !== 0 || material.displacementMap && material.displacementScale !== 0 || material.alphaMap && material.alphaTest > 0) {\\n const keyA = result.uuid, keyB = material.uuid;\\n let materialsForVariant = _materialCache[keyA];\\n if (materialsForVariant === void 0) {\\n materialsForVariant = {};\\n _materialCache[keyA] = materialsForVariant;\\n }\\n let cachedMaterial = materialsForVariant[keyB];\\n if (cachedMaterial === void 0) {\\n cachedMaterial = result.clone();\\n materialsForVariant[keyB] = cachedMaterial;\\n }\\n result = cachedMaterial;\\n }\\n result.visible = material.visible;\\n result.wireframe = material.wireframe;\\n if (type === VSMShadowMap) {\\n result.side = material.shadowSide !== null ? material.shadowSide : material.side;\\n } else {\\n result.side = material.shadowSide !== null ? material.shadowSide : shadowSide[material.side];\\n }\\n result.alphaMap = material.alphaMap;\\n result.alphaTest = material.alphaTest;\\n result.clipShadows = material.clipShadows;\\n result.clippingPlanes = material.clippingPlanes;\\n result.clipIntersection = material.clipIntersection;\\n result.displacementMap = material.displacementMap;\\n result.displacementScale = material.displacementScale;\\n result.displacementBias = material.displacementBias;\\n result.wireframeLinewidth = material.wireframeLinewidth;\\n result.linewidth = material.linewidth;\\n if (light.isPointLight === true && result.isMeshDistanceMaterial === true) {\\n result.referencePosition.setFromMatrixPosition(light.matrixWorld);\\n result.nearDistance = shadowCameraNear;\\n result.farDistance = shadowCameraFar;\\n }\\n return result;\\n }\\n function renderObject(object, camera, shadowCamera, light, type) {\\n if (object.visible === false)\\n return;\\n const visible = object.layers.test(camera.layers);\\n if (visible && (object.isMesh || object.isLine || object.isPoints)) {\\n if ((object.castShadow || object.receiveShadow && type === VSMShadowMap) && (!object.frustumCulled || _frustum.intersectsObject(object))) {\\n object.modelViewMatrix.multiplyMatrices(shadowCamera.matrixWorldInverse, object.matrixWorld);\\n const geometry = _objects.update(object);\\n const material = object.material;\\n if (Array.isArray(material)) {\\n const groups = geometry.groups;\\n for (let k = 0, kl = groups.length; k < kl; k++) {\\n const group = groups[k];\\n const groupMaterial = material[group.materialIndex];\\n if (groupMaterial && groupMaterial.visible) {\\n const depthMaterial = getDepthMaterial(object, geometry, groupMaterial, light, shadowCamera.near, shadowCamera.far, type);\\n _renderer.renderBufferDirect(shadowCamera, null, geometry, depthMaterial, object, group);\\n }\\n }\\n } else if (material.visible) {\\n const depthMaterial = getDepthMaterial(object, geometry, material, light, shadowCamera.near, shadowCamera.far, type);\\n _renderer.renderBufferDirect(shadowCamera, null, geometry, depthMaterial, object, null);\\n }\\n }\\n }\\n const children = object.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n renderObject(children[i], camera, shadowCamera, light, type);\\n }\\n }\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLState.js\\nfunction WebGLState(gl, extensions, capabilities) {\\n const isWebGL2 = capabilities.isWebGL2;\\n function ColorBuffer() {\\n let locked = false;\\n const color = new Vector4();\\n let currentColorMask = null;\\n const currentColorClear = new Vector4(0, 0, 0, 0);\\n return {\\n setMask: function(colorMask) {\\n if (currentColorMask !== colorMask && !locked) {\\n gl.colorMask(colorMask, colorMask, colorMask, colorMask);\\n currentColorMask = colorMask;\\n }\\n },\\n setLocked: function(lock) {\\n locked = lock;\\n },\\n setClear: function(r, g, b, a, premultipliedAlpha) {\\n if (premultipliedAlpha === true) {\\n r *= a;\\n g *= a;\\n b *= a;\\n }\\n color.set(r, g, b, a);\\n if (currentColorClear.equals(color) === false) {\\n gl.clearColor(r, g, b, a);\\n currentColorClear.copy(color);\\n }\\n },\\n reset: function() {\\n locked = false;\\n currentColorMask = null;\\n currentColorClear.set(-1, 0, 0, 0);\\n }\\n };\\n }\\n function DepthBuffer() {\\n let locked = false;\\n let currentDepthMask = null;\\n let currentDepthFunc = null;\\n let currentDepthClear = null;\\n return {\\n setTest: function(depthTest) {\\n if (depthTest) {\\n enable(gl.DEPTH_TEST);\\n } else {\\n disable(gl.DEPTH_TEST);\\n }\\n },\\n setMask: function(depthMask) {\\n if (currentDepthMask !== depthMask && !locked) {\\n gl.depthMask(depthMask);\\n currentDepthMask = depthMask;\\n }\\n },\\n setFunc: function(depthFunc) {\\n if (currentDepthFunc !== depthFunc) {\\n if (depthFunc) {\\n switch (depthFunc) {\\n case NeverDepth:\\n gl.depthFunc(gl.NEVER);\\n break;\\n case AlwaysDepth:\\n gl.depthFunc(gl.ALWAYS);\\n break;\\n case LessDepth:\\n gl.depthFunc(gl.LESS);\\n break;\\n case LessEqualDepth:\\n gl.depthFunc(gl.LEQUAL);\\n break;\\n case EqualDepth:\\n gl.depthFunc(gl.EQUAL);\\n break;\\n case GreaterEqualDepth:\\n gl.depthFunc(gl.GEQUAL);\\n break;\\n case GreaterDepth:\\n gl.depthFunc(gl.GREATER);\\n break;\\n case NotEqualDepth:\\n gl.depthFunc(gl.NOTEQUAL);\\n break;\\n default:\\n gl.depthFunc(gl.LEQUAL);\\n }\\n } else {\\n gl.depthFunc(gl.LEQUAL);\\n }\\n currentDepthFunc = depthFunc;\\n }\\n },\\n setLocked: function(lock) {\\n locked = lock;\\n },\\n setClear: function(depth2) {\\n if (currentDepthClear !== depth2) {\\n gl.clearDepth(depth2);\\n currentDepthClear = depth2;\\n }\\n },\\n reset: function() {\\n locked = false;\\n currentDepthMask = null;\\n currentDepthFunc = null;\\n currentDepthClear = null;\\n }\\n };\\n }\\n function StencilBuffer() {\\n let locked = false;\\n let currentStencilMask = null;\\n let currentStencilFunc = null;\\n let currentStencilRef = null;\\n let currentStencilFuncMask = null;\\n let currentStencilFail = null;\\n let currentStencilZFail = null;\\n let currentStencilZPass = null;\\n let currentStencilClear = null;\\n return {\\n setTest: function(stencilTest) {\\n if (!locked) {\\n if (stencilTest) {\\n enable(gl.STENCIL_TEST);\\n } else {\\n disable(gl.STENCIL_TEST);\\n }\\n }\\n },\\n setMask: function(stencilMask) {\\n if (currentStencilMask !== stencilMask && !locked) {\\n gl.stencilMask(stencilMask);\\n currentStencilMask = stencilMask;\\n }\\n },\\n setFunc: function(stencilFunc, stencilRef, stencilMask) {\\n if (currentStencilFunc !== stencilFunc || currentStencilRef !== stencilRef || currentStencilFuncMask !== stencilMask) {\\n gl.stencilFunc(stencilFunc, stencilRef, stencilMask);\\n currentStencilFunc = stencilFunc;\\n currentStencilRef = stencilRef;\\n currentStencilFuncMask = stencilMask;\\n }\\n },\\n setOp: function(stencilFail, stencilZFail, stencilZPass) {\\n if (currentStencilFail !== stencilFail || currentStencilZFail !== stencilZFail || currentStencilZPass !== stencilZPass) {\\n gl.stencilOp(stencilFail, stencilZFail, stencilZPass);\\n currentStencilFail = stencilFail;\\n currentStencilZFail = stencilZFail;\\n currentStencilZPass = stencilZPass;\\n }\\n },\\n setLocked: function(lock) {\\n locked = lock;\\n },\\n setClear: function(stencil) {\\n if (currentStencilClear !== stencil) {\\n gl.clearStencil(stencil);\\n currentStencilClear = stencil;\\n }\\n },\\n reset: function() {\\n locked = false;\\n currentStencilMask = null;\\n currentStencilFunc = null;\\n currentStencilRef = null;\\n currentStencilFuncMask = null;\\n currentStencilFail = null;\\n currentStencilZFail = null;\\n currentStencilZPass = null;\\n currentStencilClear = null;\\n }\\n };\\n }\\n const colorBuffer = new ColorBuffer();\\n const depthBuffer = new DepthBuffer();\\n const stencilBuffer = new StencilBuffer();\\n let enabledCapabilities = {};\\n let xrFramebuffer = null;\\n let currentBoundFramebuffers = {};\\n let currentProgram = null;\\n let currentBlendingEnabled = false;\\n let currentBlending = null;\\n let currentBlendEquation = null;\\n let currentBlendSrc = null;\\n let currentBlendDst = null;\\n let currentBlendEquationAlpha = null;\\n let currentBlendSrcAlpha = null;\\n let currentBlendDstAlpha = null;\\n let currentPremultipledAlpha = false;\\n let currentFlipSided = null;\\n let currentCullFace = null;\\n let currentLineWidth = null;\\n let currentPolygonOffsetFactor = null;\\n let currentPolygonOffsetUnits = null;\\n const maxTextures = gl.getParameter(gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS);\\n let lineWidthAvailable = false;\\n let version = 0;\\n const glVersion = gl.getParameter(gl.VERSION);\\n if (glVersion.indexOf(\\\\\\\"WebGL\\\\\\\") !== -1) {\\n version = parseFloat(/^WebGL (\\\\d)/.exec(glVersion)[1]);\\n lineWidthAvailable = version >= 1;\\n } else if (glVersion.indexOf(\\\\\\\"OpenGL ES\\\\\\\") !== -1) {\\n version = parseFloat(/^OpenGL ES (\\\\d)/.exec(glVersion)[1]);\\n lineWidthAvailable = version >= 2;\\n }\\n let currentTextureSlot = null;\\n let currentBoundTextures = {};\\n const scissorParam = gl.getParameter(gl.SCISSOR_BOX);\\n const viewportParam = gl.getParameter(gl.VIEWPORT);\\n const currentScissor = new Vector4().fromArray(scissorParam);\\n const currentViewport = new Vector4().fromArray(viewportParam);\\n function createTexture(type, target, count) {\\n const data = new Uint8Array(4);\\n const texture = gl.createTexture();\\n gl.bindTexture(type, texture);\\n gl.texParameteri(type, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\\n gl.texParameteri(type, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\\n for (let i = 0; i < count; i++) {\\n gl.texImage2D(target + i, 0, gl.RGBA, 1, 1, 0, gl.RGBA, gl.UNSIGNED_BYTE, data);\\n }\\n return texture;\\n }\\n const emptyTextures = {};\\n emptyTextures[gl.TEXTURE_2D] = createTexture(gl.TEXTURE_2D, gl.TEXTURE_2D, 1);\\n emptyTextures[gl.TEXTURE_CUBE_MAP] = createTexture(gl.TEXTURE_CUBE_MAP, gl.TEXTURE_CUBE_MAP_POSITIVE_X, 6);\\n colorBuffer.setClear(0, 0, 0, 1);\\n depthBuffer.setClear(1);\\n stencilBuffer.setClear(0);\\n enable(gl.DEPTH_TEST);\\n depthBuffer.setFunc(LessEqualDepth);\\n setFlipSided(false);\\n setCullFace(CullFaceBack);\\n enable(gl.CULL_FACE);\\n setBlending(NoBlending);\\n function enable(id) {\\n if (enabledCapabilities[id] !== true) {\\n gl.enable(id);\\n enabledCapabilities[id] = true;\\n }\\n }\\n function disable(id) {\\n if (enabledCapabilities[id] !== false) {\\n gl.disable(id);\\n enabledCapabilities[id] = false;\\n }\\n }\\n function bindXRFramebuffer(framebuffer) {\\n if (framebuffer !== xrFramebuffer) {\\n gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer);\\n xrFramebuffer = framebuffer;\\n }\\n }\\n function bindFramebuffer(target, framebuffer) {\\n if (framebuffer === null && xrFramebuffer !== null)\\n framebuffer = xrFramebuffer;\\n if (currentBoundFramebuffers[target] !== framebuffer) {\\n gl.bindFramebuffer(target, framebuffer);\\n currentBoundFramebuffers[target] = framebuffer;\\n if (isWebGL2) {\\n if (target === gl.DRAW_FRAMEBUFFER) {\\n currentBoundFramebuffers[gl.FRAMEBUFFER] = framebuffer;\\n }\\n if (target === gl.FRAMEBUFFER) {\\n currentBoundFramebuffers[gl.DRAW_FRAMEBUFFER] = framebuffer;\\n }\\n }\\n return true;\\n }\\n return false;\\n }\\n function useProgram(program) {\\n if (currentProgram !== program) {\\n gl.useProgram(program);\\n currentProgram = program;\\n return true;\\n }\\n return false;\\n }\\n const equationToGL = {\\n [AddEquation]: gl.FUNC_ADD,\\n [SubtractEquation]: gl.FUNC_SUBTRACT,\\n [ReverseSubtractEquation]: gl.FUNC_REVERSE_SUBTRACT\\n };\\n if (isWebGL2) {\\n equationToGL[MinEquation] = gl.MIN;\\n equationToGL[MaxEquation] = gl.MAX;\\n } else {\\n const extension = extensions.get(\\\\\\\"EXT_blend_minmax\\\\\\\");\\n if (extension !== null) {\\n equationToGL[MinEquation] = extension.MIN_EXT;\\n equationToGL[MaxEquation] = extension.MAX_EXT;\\n }\\n }\\n const factorToGL = {\\n [ZeroFactor]: gl.ZERO,\\n [OneFactor]: gl.ONE,\\n [SrcColorFactor]: gl.SRC_COLOR,\\n [SrcAlphaFactor]: gl.SRC_ALPHA,\\n [SrcAlphaSaturateFactor]: gl.SRC_ALPHA_SATURATE,\\n [DstColorFactor]: gl.DST_COLOR,\\n [DstAlphaFactor]: gl.DST_ALPHA,\\n [OneMinusSrcColorFactor]: gl.ONE_MINUS_SRC_COLOR,\\n [OneMinusSrcAlphaFactor]: gl.ONE_MINUS_SRC_ALPHA,\\n [OneMinusDstColorFactor]: gl.ONE_MINUS_DST_COLOR,\\n [OneMinusDstAlphaFactor]: gl.ONE_MINUS_DST_ALPHA\\n };\\n function setBlending(blending, blendEquation, blendSrc, blendDst, blendEquationAlpha, blendSrcAlpha, blendDstAlpha, premultipliedAlpha) {\\n if (blending === NoBlending) {\\n if (currentBlendingEnabled === true) {\\n disable(gl.BLEND);\\n currentBlendingEnabled = false;\\n }\\n return;\\n }\\n if (currentBlendingEnabled === false) {\\n enable(gl.BLEND);\\n currentBlendingEnabled = true;\\n }\\n if (blending !== CustomBlending) {\\n if (blending !== currentBlending || premultipliedAlpha !== currentPremultipledAlpha) {\\n if (currentBlendEquation !== AddEquation || currentBlendEquationAlpha !== AddEquation) {\\n gl.blendEquation(gl.FUNC_ADD);\\n currentBlendEquation = AddEquation;\\n currentBlendEquationAlpha = AddEquation;\\n }\\n if (premultipliedAlpha) {\\n switch (blending) {\\n case NormalBlending:\\n gl.blendFuncSeparate(gl.ONE, gl.ONE_MINUS_SRC_ALPHA, gl.ONE, gl.ONE_MINUS_SRC_ALPHA);\\n break;\\n case AdditiveBlending:\\n gl.blendFunc(gl.ONE, gl.ONE);\\n break;\\n case SubtractiveBlending:\\n gl.blendFuncSeparate(gl.ZERO, gl.ZERO, gl.ONE_MINUS_SRC_COLOR, gl.ONE_MINUS_SRC_ALPHA);\\n break;\\n case MultiplyBlending:\\n gl.blendFuncSeparate(gl.ZERO, gl.SRC_COLOR, gl.ZERO, gl.SRC_ALPHA);\\n break;\\n default:\\n console.error(\\\\\\\"THREE.WebGLState: Invalid blending: \\\\\\\", blending);\\n break;\\n }\\n } else {\\n switch (blending) {\\n case NormalBlending:\\n gl.blendFuncSeparate(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA, gl.ONE, gl.ONE_MINUS_SRC_ALPHA);\\n break;\\n case AdditiveBlending:\\n gl.blendFunc(gl.SRC_ALPHA, gl.ONE);\\n break;\\n case SubtractiveBlending:\\n gl.blendFunc(gl.ZERO, gl.ONE_MINUS_SRC_COLOR);\\n break;\\n case MultiplyBlending:\\n gl.blendFunc(gl.ZERO, gl.SRC_COLOR);\\n break;\\n default:\\n console.error(\\\\\\\"THREE.WebGLState: Invalid blending: \\\\\\\", blending);\\n break;\\n }\\n }\\n currentBlendSrc = null;\\n currentBlendDst = null;\\n currentBlendSrcAlpha = null;\\n currentBlendDstAlpha = null;\\n currentBlending = blending;\\n currentPremultipledAlpha = premultipliedAlpha;\\n }\\n return;\\n }\\n blendEquationAlpha = blendEquationAlpha || blendEquation;\\n blendSrcAlpha = blendSrcAlpha || blendSrc;\\n blendDstAlpha = blendDstAlpha || blendDst;\\n if (blendEquation !== currentBlendEquation || blendEquationAlpha !== currentBlendEquationAlpha) {\\n gl.blendEquationSeparate(equationToGL[blendEquation], equationToGL[blendEquationAlpha]);\\n currentBlendEquation = blendEquation;\\n currentBlendEquationAlpha = blendEquationAlpha;\\n }\\n if (blendSrc !== currentBlendSrc || blendDst !== currentBlendDst || blendSrcAlpha !== currentBlendSrcAlpha || blendDstAlpha !== currentBlendDstAlpha) {\\n gl.blendFuncSeparate(factorToGL[blendSrc], factorToGL[blendDst], factorToGL[blendSrcAlpha], factorToGL[blendDstAlpha]);\\n currentBlendSrc = blendSrc;\\n currentBlendDst = blendDst;\\n currentBlendSrcAlpha = blendSrcAlpha;\\n currentBlendDstAlpha = blendDstAlpha;\\n }\\n currentBlending = blending;\\n currentPremultipledAlpha = null;\\n }\\n function setMaterial(material, frontFaceCW) {\\n material.side === DoubleSide ? disable(gl.CULL_FACE) : enable(gl.CULL_FACE);\\n let flipSided = material.side === BackSide;\\n if (frontFaceCW)\\n flipSided = !flipSided;\\n setFlipSided(flipSided);\\n material.blending === NormalBlending && material.transparent === false ? setBlending(NoBlending) : setBlending(material.blending, material.blendEquation, material.blendSrc, material.blendDst, material.blendEquationAlpha, material.blendSrcAlpha, material.blendDstAlpha, material.premultipliedAlpha);\\n depthBuffer.setFunc(material.depthFunc);\\n depthBuffer.setTest(material.depthTest);\\n depthBuffer.setMask(material.depthWrite);\\n colorBuffer.setMask(material.colorWrite);\\n const stencilWrite = material.stencilWrite;\\n stencilBuffer.setTest(stencilWrite);\\n if (stencilWrite) {\\n stencilBuffer.setMask(material.stencilWriteMask);\\n stencilBuffer.setFunc(material.stencilFunc, material.stencilRef, material.stencilFuncMask);\\n stencilBuffer.setOp(material.stencilFail, material.stencilZFail, material.stencilZPass);\\n }\\n setPolygonOffset(material.polygonOffset, material.polygonOffsetFactor, material.polygonOffsetUnits);\\n material.alphaToCoverage === true ? enable(gl.SAMPLE_ALPHA_TO_COVERAGE) : disable(gl.SAMPLE_ALPHA_TO_COVERAGE);\\n }\\n function setFlipSided(flipSided) {\\n if (currentFlipSided !== flipSided) {\\n if (flipSided) {\\n gl.frontFace(gl.CW);\\n } else {\\n gl.frontFace(gl.CCW);\\n }\\n currentFlipSided = flipSided;\\n }\\n }\\n function setCullFace(cullFace) {\\n if (cullFace !== CullFaceNone) {\\n enable(gl.CULL_FACE);\\n if (cullFace !== currentCullFace) {\\n if (cullFace === CullFaceBack) {\\n gl.cullFace(gl.BACK);\\n } else if (cullFace === CullFaceFront) {\\n gl.cullFace(gl.FRONT);\\n } else {\\n gl.cullFace(gl.FRONT_AND_BACK);\\n }\\n }\\n } else {\\n disable(gl.CULL_FACE);\\n }\\n currentCullFace = cullFace;\\n }\\n function setLineWidth(width) {\\n if (width !== currentLineWidth) {\\n if (lineWidthAvailable)\\n gl.lineWidth(width);\\n currentLineWidth = width;\\n }\\n }\\n function setPolygonOffset(polygonOffset, factor, units) {\\n if (polygonOffset) {\\n enable(gl.POLYGON_OFFSET_FILL);\\n if (currentPolygonOffsetFactor !== factor || currentPolygonOffsetUnits !== units) {\\n gl.polygonOffset(factor, units);\\n currentPolygonOffsetFactor = factor;\\n currentPolygonOffsetUnits = units;\\n }\\n } else {\\n disable(gl.POLYGON_OFFSET_FILL);\\n }\\n }\\n function setScissorTest(scissorTest) {\\n if (scissorTest) {\\n enable(gl.SCISSOR_TEST);\\n } else {\\n disable(gl.SCISSOR_TEST);\\n }\\n }\\n function activeTexture(webglSlot) {\\n if (webglSlot === void 0)\\n webglSlot = gl.TEXTURE0 + maxTextures - 1;\\n if (currentTextureSlot !== webglSlot) {\\n gl.activeTexture(webglSlot);\\n currentTextureSlot = webglSlot;\\n }\\n }\\n function bindTexture(webglType, webglTexture) {\\n if (currentTextureSlot === null) {\\n activeTexture();\\n }\\n let boundTexture = currentBoundTextures[currentTextureSlot];\\n if (boundTexture === void 0) {\\n boundTexture = {type: void 0, texture: void 0};\\n currentBoundTextures[currentTextureSlot] = boundTexture;\\n }\\n if (boundTexture.type !== webglType || boundTexture.texture !== webglTexture) {\\n gl.bindTexture(webglType, webglTexture || emptyTextures[webglType]);\\n boundTexture.type = webglType;\\n boundTexture.texture = webglTexture;\\n }\\n }\\n function unbindTexture() {\\n const boundTexture = currentBoundTextures[currentTextureSlot];\\n if (boundTexture !== void 0 && boundTexture.type !== void 0) {\\n gl.bindTexture(boundTexture.type, null);\\n boundTexture.type = void 0;\\n boundTexture.texture = void 0;\\n }\\n }\\n function compressedTexImage2D() {\\n try {\\n gl.compressedTexImage2D.apply(gl, arguments);\\n } catch (error) {\\n console.error(\\\\\\\"THREE.WebGLState:\\\\\\\", error);\\n }\\n }\\n function texImage2D() {\\n try {\\n gl.texImage2D.apply(gl, arguments);\\n } catch (error) {\\n console.error(\\\\\\\"THREE.WebGLState:\\\\\\\", error);\\n }\\n }\\n function texImage3D() {\\n try {\\n gl.texImage3D.apply(gl, arguments);\\n } catch (error) {\\n console.error(\\\\\\\"THREE.WebGLState:\\\\\\\", error);\\n }\\n }\\n function scissor(scissor2) {\\n if (currentScissor.equals(scissor2) === false) {\\n gl.scissor(scissor2.x, scissor2.y, scissor2.z, scissor2.w);\\n currentScissor.copy(scissor2);\\n }\\n }\\n function viewport(viewport2) {\\n if (currentViewport.equals(viewport2) === false) {\\n gl.viewport(viewport2.x, viewport2.y, viewport2.z, viewport2.w);\\n currentViewport.copy(viewport2);\\n }\\n }\\n function reset() {\\n gl.disable(gl.BLEND);\\n gl.disable(gl.CULL_FACE);\\n gl.disable(gl.DEPTH_TEST);\\n gl.disable(gl.POLYGON_OFFSET_FILL);\\n gl.disable(gl.SCISSOR_TEST);\\n gl.disable(gl.STENCIL_TEST);\\n gl.disable(gl.SAMPLE_ALPHA_TO_COVERAGE);\\n gl.blendEquation(gl.FUNC_ADD);\\n gl.blendFunc(gl.ONE, gl.ZERO);\\n gl.blendFuncSeparate(gl.ONE, gl.ZERO, gl.ONE, gl.ZERO);\\n gl.colorMask(true, true, true, true);\\n gl.clearColor(0, 0, 0, 0);\\n gl.depthMask(true);\\n gl.depthFunc(gl.LESS);\\n gl.clearDepth(1);\\n gl.stencilMask(4294967295);\\n gl.stencilFunc(gl.ALWAYS, 0, 4294967295);\\n gl.stencilOp(gl.KEEP, gl.KEEP, gl.KEEP);\\n gl.clearStencil(0);\\n gl.cullFace(gl.BACK);\\n gl.frontFace(gl.CCW);\\n gl.polygonOffset(0, 0);\\n gl.activeTexture(gl.TEXTURE0);\\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\\n if (isWebGL2 === true) {\\n gl.bindFramebuffer(gl.DRAW_FRAMEBUFFER, null);\\n gl.bindFramebuffer(gl.READ_FRAMEBUFFER, null);\\n }\\n gl.useProgram(null);\\n gl.lineWidth(1);\\n gl.scissor(0, 0, gl.canvas.width, gl.canvas.height);\\n gl.viewport(0, 0, gl.canvas.width, gl.canvas.height);\\n enabledCapabilities = {};\\n currentTextureSlot = null;\\n currentBoundTextures = {};\\n xrFramebuffer = null;\\n currentBoundFramebuffers = {};\\n currentProgram = null;\\n currentBlendingEnabled = false;\\n currentBlending = null;\\n currentBlendEquation = null;\\n currentBlendSrc = null;\\n currentBlendDst = null;\\n currentBlendEquationAlpha = null;\\n currentBlendSrcAlpha = null;\\n currentBlendDstAlpha = null;\\n currentPremultipledAlpha = false;\\n currentFlipSided = null;\\n currentCullFace = null;\\n currentLineWidth = null;\\n currentPolygonOffsetFactor = null;\\n currentPolygonOffsetUnits = null;\\n currentScissor.set(0, 0, gl.canvas.width, gl.canvas.height);\\n currentViewport.set(0, 0, gl.canvas.width, gl.canvas.height);\\n colorBuffer.reset();\\n depthBuffer.reset();\\n stencilBuffer.reset();\\n }\\n return {\\n buffers: {\\n color: colorBuffer,\\n depth: depthBuffer,\\n stencil: stencilBuffer\\n },\\n enable,\\n disable,\\n bindFramebuffer,\\n bindXRFramebuffer,\\n useProgram,\\n setBlending,\\n setMaterial,\\n setFlipSided,\\n setCullFace,\\n setLineWidth,\\n setPolygonOffset,\\n setScissorTest,\\n activeTexture,\\n bindTexture,\\n unbindTexture,\\n compressedTexImage2D,\\n texImage2D,\\n texImage3D,\\n scissor,\\n viewport,\\n reset\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLTextures.js\\nfunction WebGLTextures(_gl, extensions, state, properties, capabilities, utils, info) {\\n const isWebGL2 = capabilities.isWebGL2;\\n const maxTextures = capabilities.maxTextures;\\n const maxCubemapSize = capabilities.maxCubemapSize;\\n const maxTextureSize = capabilities.maxTextureSize;\\n const maxSamples = capabilities.maxSamples;\\n const _videoTextures = new WeakMap();\\n let _canvas2;\\n let useOffscreenCanvas = false;\\n try {\\n useOffscreenCanvas = typeof OffscreenCanvas !== \\\\\\\"undefined\\\\\\\" && new OffscreenCanvas(1, 1).getContext(\\\\\\\"2d\\\\\\\") !== null;\\n } catch (err) {\\n }\\n function createCanvas(width, height) {\\n return useOffscreenCanvas ? new OffscreenCanvas(width, height) : createElementNS(\\\\\\\"canvas\\\\\\\");\\n }\\n function resizeImage(image, needsPowerOfTwo, needsNewCanvas, maxSize) {\\n let scale = 1;\\n if (image.width > maxSize || image.height > maxSize) {\\n scale = maxSize / Math.max(image.width, image.height);\\n }\\n if (scale < 1 || needsPowerOfTwo === true) {\\n if (typeof HTMLImageElement !== \\\\\\\"undefined\\\\\\\" && image instanceof HTMLImageElement || typeof HTMLCanvasElement !== \\\\\\\"undefined\\\\\\\" && image instanceof HTMLCanvasElement || typeof ImageBitmap !== \\\\\\\"undefined\\\\\\\" && image instanceof ImageBitmap) {\\n const floor = needsPowerOfTwo ? floorPowerOfTwo : Math.floor;\\n const width = floor(scale * image.width);\\n const height = floor(scale * image.height);\\n if (_canvas2 === void 0)\\n _canvas2 = createCanvas(width, height);\\n const canvas = needsNewCanvas ? createCanvas(width, height) : _canvas2;\\n canvas.width = width;\\n canvas.height = height;\\n const context = canvas.getContext(\\\\\\\"2d\\\\\\\");\\n context.drawImage(image, 0, 0, width, height);\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\" + image.width + \\\\\\\"x\\\\\\\" + image.height + \\\\\\\") to (\\\\\\\" + width + \\\\\\\"x\\\\\\\" + height + \\\\\\\").\\\\\\\");\\n return canvas;\\n } else {\\n if (\\\\\\\"data\\\\\\\" in image) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\" + image.width + \\\\\\\"x\\\\\\\" + image.height + \\\\\\\").\\\\\\\");\\n }\\n return image;\\n }\\n }\\n return image;\\n }\\n function isPowerOfTwo2(image) {\\n return isPowerOfTwo(image.width) && isPowerOfTwo(image.height);\\n }\\n function textureNeedsPowerOfTwo(texture) {\\n if (isWebGL2)\\n return false;\\n return texture.wrapS !== ClampToEdgeWrapping || texture.wrapT !== ClampToEdgeWrapping || texture.minFilter !== NearestFilter && texture.minFilter !== LinearFilter;\\n }\\n function textureNeedsGenerateMipmaps(texture, supportsMips) {\\n return texture.generateMipmaps && supportsMips && texture.minFilter !== NearestFilter && texture.minFilter !== LinearFilter;\\n }\\n function generateMipmap(target, texture, width, height, depth2 = 1) {\\n _gl.generateMipmap(target);\\n const textureProperties = properties.get(texture);\\n textureProperties.__maxMipLevel = Math.log2(Math.max(width, height, depth2));\\n }\\n function getInternalFormat(internalFormatName, glFormat, glType, encoding) {\\n if (isWebGL2 === false)\\n return glFormat;\\n if (internalFormatName !== null) {\\n if (_gl[internalFormatName] !== void 0)\\n return _gl[internalFormatName];\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\" + internalFormatName + \\\\\\\"'\\\\\\\");\\n }\\n let internalFormat = glFormat;\\n if (glFormat === _gl.RED) {\\n if (glType === _gl.FLOAT)\\n internalFormat = _gl.R32F;\\n if (glType === _gl.HALF_FLOAT)\\n internalFormat = _gl.R16F;\\n if (glType === _gl.UNSIGNED_BYTE)\\n internalFormat = _gl.R8;\\n }\\n if (glFormat === _gl.RGB) {\\n if (glType === _gl.FLOAT)\\n internalFormat = _gl.RGB32F;\\n if (glType === _gl.HALF_FLOAT)\\n internalFormat = _gl.RGB16F;\\n if (glType === _gl.UNSIGNED_BYTE)\\n internalFormat = _gl.RGB8;\\n }\\n if (glFormat === _gl.RGBA) {\\n if (glType === _gl.FLOAT)\\n internalFormat = _gl.RGBA32F;\\n if (glType === _gl.HALF_FLOAT)\\n internalFormat = _gl.RGBA16F;\\n if (glType === _gl.UNSIGNED_BYTE)\\n internalFormat = encoding === sRGBEncoding ? _gl.SRGB8_ALPHA8 : _gl.RGBA8;\\n }\\n if (internalFormat === _gl.R16F || internalFormat === _gl.R32F || internalFormat === _gl.RGBA16F || internalFormat === _gl.RGBA32F) {\\n extensions.get(\\\\\\\"EXT_color_buffer_float\\\\\\\");\\n }\\n return internalFormat;\\n }\\n function filterFallback(f) {\\n if (f === NearestFilter || f === NearestMipmapNearestFilter || f === NearestMipmapLinearFilter) {\\n return _gl.NEAREST;\\n }\\n return _gl.LINEAR;\\n }\\n function onTextureDispose(event) {\\n const texture = event.target;\\n texture.removeEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n deallocateTexture(texture);\\n if (texture.isVideoTexture) {\\n _videoTextures.delete(texture);\\n }\\n info.memory.textures--;\\n }\\n function onRenderTargetDispose(event) {\\n const renderTarget = event.target;\\n renderTarget.removeEventListener(\\\\\\\"dispose\\\\\\\", onRenderTargetDispose);\\n deallocateRenderTarget(renderTarget);\\n }\\n function deallocateTexture(texture) {\\n const textureProperties = properties.get(texture);\\n if (textureProperties.__webglInit === void 0)\\n return;\\n _gl.deleteTexture(textureProperties.__webglTexture);\\n properties.remove(texture);\\n }\\n function deallocateRenderTarget(renderTarget) {\\n const texture = renderTarget.texture;\\n const renderTargetProperties = properties.get(renderTarget);\\n const textureProperties = properties.get(texture);\\n if (!renderTarget)\\n return;\\n if (textureProperties.__webglTexture !== void 0) {\\n _gl.deleteTexture(textureProperties.__webglTexture);\\n info.memory.textures--;\\n }\\n if (renderTarget.depthTexture) {\\n renderTarget.depthTexture.dispose();\\n }\\n if (renderTarget.isWebGLCubeRenderTarget) {\\n for (let i = 0; i < 6; i++) {\\n _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer[i]);\\n if (renderTargetProperties.__webglDepthbuffer)\\n _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthbuffer[i]);\\n }\\n } else {\\n _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer);\\n if (renderTargetProperties.__webglDepthbuffer)\\n _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthbuffer);\\n if (renderTargetProperties.__webglMultisampledFramebuffer)\\n _gl.deleteFramebuffer(renderTargetProperties.__webglMultisampledFramebuffer);\\n if (renderTargetProperties.__webglColorRenderbuffer)\\n _gl.deleteRenderbuffer(renderTargetProperties.__webglColorRenderbuffer);\\n if (renderTargetProperties.__webglDepthRenderbuffer)\\n _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthRenderbuffer);\\n }\\n if (renderTarget.isWebGLMultipleRenderTargets) {\\n for (let i = 0, il = texture.length; i < il; i++) {\\n const attachmentProperties = properties.get(texture[i]);\\n if (attachmentProperties.__webglTexture) {\\n _gl.deleteTexture(attachmentProperties.__webglTexture);\\n info.memory.textures--;\\n }\\n properties.remove(texture[i]);\\n }\\n }\\n properties.remove(texture);\\n properties.remove(renderTarget);\\n }\\n let textureUnits = 0;\\n function resetTextureUnits() {\\n textureUnits = 0;\\n }\\n function allocateTextureUnit() {\\n const textureUnit = textureUnits;\\n if (textureUnit >= maxTextures) {\\n console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\" + textureUnit + \\\\\\\" texture units while this GPU supports only \\\\\\\" + maxTextures);\\n }\\n textureUnits += 1;\\n return textureUnit;\\n }\\n function setTexture2D(texture, slot) {\\n const textureProperties = properties.get(texture);\\n if (texture.isVideoTexture)\\n updateVideoTexture(texture);\\n if (texture.version > 0 && textureProperties.__version !== texture.version) {\\n const image = texture.image;\\n if (image === void 0) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");\\n } else if (image.complete === false) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\");\\n } else {\\n uploadTexture(textureProperties, texture, slot);\\n return;\\n }\\n }\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(_gl.TEXTURE_2D, textureProperties.__webglTexture);\\n }\\n function setTexture2DArray(texture, slot) {\\n const textureProperties = properties.get(texture);\\n if (texture.version > 0 && textureProperties.__version !== texture.version) {\\n uploadTexture(textureProperties, texture, slot);\\n return;\\n }\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(_gl.TEXTURE_2D_ARRAY, textureProperties.__webglTexture);\\n }\\n function setTexture3D(texture, slot) {\\n const textureProperties = properties.get(texture);\\n if (texture.version > 0 && textureProperties.__version !== texture.version) {\\n uploadTexture(textureProperties, texture, slot);\\n return;\\n }\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(_gl.TEXTURE_3D, textureProperties.__webglTexture);\\n }\\n function setTextureCube(texture, slot) {\\n const textureProperties = properties.get(texture);\\n if (texture.version > 0 && textureProperties.__version !== texture.version) {\\n uploadCubeTexture(textureProperties, texture, slot);\\n return;\\n }\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture);\\n }\\n const wrappingToGL = {\\n [RepeatWrapping]: _gl.REPEAT,\\n [ClampToEdgeWrapping]: _gl.CLAMP_TO_EDGE,\\n [MirroredRepeatWrapping]: _gl.MIRRORED_REPEAT\\n };\\n const filterToGL = {\\n [NearestFilter]: _gl.NEAREST,\\n [NearestMipmapNearestFilter]: _gl.NEAREST_MIPMAP_NEAREST,\\n [NearestMipmapLinearFilter]: _gl.NEAREST_MIPMAP_LINEAR,\\n [LinearFilter]: _gl.LINEAR,\\n [LinearMipmapNearestFilter]: _gl.LINEAR_MIPMAP_NEAREST,\\n [LinearMipmapLinearFilter]: _gl.LINEAR_MIPMAP_LINEAR\\n };\\n function setTextureParameters(textureType, texture, supportsMips) {\\n if (supportsMips) {\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_S, wrappingToGL[texture.wrapS]);\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_T, wrappingToGL[texture.wrapT]);\\n if (textureType === _gl.TEXTURE_3D || textureType === _gl.TEXTURE_2D_ARRAY) {\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_R, wrappingToGL[texture.wrapR]);\\n }\\n _gl.texParameteri(textureType, _gl.TEXTURE_MAG_FILTER, filterToGL[texture.magFilter]);\\n _gl.texParameteri(textureType, _gl.TEXTURE_MIN_FILTER, filterToGL[texture.minFilter]);\\n } else {\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_S, _gl.CLAMP_TO_EDGE);\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_T, _gl.CLAMP_TO_EDGE);\\n if (textureType === _gl.TEXTURE_3D || textureType === _gl.TEXTURE_2D_ARRAY) {\\n _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_R, _gl.CLAMP_TO_EDGE);\\n }\\n if (texture.wrapS !== ClampToEdgeWrapping || texture.wrapT !== ClampToEdgeWrapping) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Texture is not power of two. Texture.wrapS and Texture.wrapT should be set to THREE.ClampToEdgeWrapping.\\\\\\\");\\n }\\n _gl.texParameteri(textureType, _gl.TEXTURE_MAG_FILTER, filterFallback(texture.magFilter));\\n _gl.texParameteri(textureType, _gl.TEXTURE_MIN_FILTER, filterFallback(texture.minFilter));\\n if (texture.minFilter !== NearestFilter && texture.minFilter !== LinearFilter) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Texture is not power of two. Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter.\\\\\\\");\\n }\\n }\\n if (extensions.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\") === true) {\\n const extension = extensions.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");\\n if (texture.type === FloatType && extensions.has(\\\\\\\"OES_texture_float_linear\\\\\\\") === false)\\n return;\\n if (isWebGL2 === false && (texture.type === HalfFloatType && extensions.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\") === false))\\n return;\\n if (texture.anisotropy > 1 || properties.get(texture).__currentAnisotropy) {\\n _gl.texParameterf(textureType, extension.TEXTURE_MAX_ANISOTROPY_EXT, Math.min(texture.anisotropy, capabilities.getMaxAnisotropy()));\\n properties.get(texture).__currentAnisotropy = texture.anisotropy;\\n }\\n }\\n }\\n function initTexture(textureProperties, texture) {\\n if (textureProperties.__webglInit === void 0) {\\n textureProperties.__webglInit = true;\\n texture.addEventListener(\\\\\\\"dispose\\\\\\\", onTextureDispose);\\n textureProperties.__webglTexture = _gl.createTexture();\\n info.memory.textures++;\\n }\\n }\\n function uploadTexture(textureProperties, texture, slot) {\\n let textureType = _gl.TEXTURE_2D;\\n if (texture.isDataTexture2DArray)\\n textureType = _gl.TEXTURE_2D_ARRAY;\\n if (texture.isDataTexture3D)\\n textureType = _gl.TEXTURE_3D;\\n initTexture(textureProperties, texture);\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(textureType, textureProperties.__webglTexture);\\n _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, texture.flipY);\\n _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, texture.premultiplyAlpha);\\n _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, texture.unpackAlignment);\\n _gl.pixelStorei(_gl.UNPACK_COLORSPACE_CONVERSION_WEBGL, _gl.NONE);\\n const needsPowerOfTwo = textureNeedsPowerOfTwo(texture) && isPowerOfTwo2(texture.image) === false;\\n const image = resizeImage(texture.image, needsPowerOfTwo, false, maxTextureSize);\\n const supportsMips = isPowerOfTwo2(image) || isWebGL2, glFormat = utils.convert(texture.format);\\n let glType = utils.convert(texture.type), glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.encoding);\\n setTextureParameters(textureType, texture, supportsMips);\\n let mipmap;\\n const mipmaps = texture.mipmaps;\\n if (texture.isDepthTexture) {\\n glInternalFormat = _gl.DEPTH_COMPONENT;\\n if (isWebGL2) {\\n if (texture.type === FloatType) {\\n glInternalFormat = _gl.DEPTH_COMPONENT32F;\\n } else if (texture.type === UnsignedIntType) {\\n glInternalFormat = _gl.DEPTH_COMPONENT24;\\n } else if (texture.type === UnsignedInt248Type) {\\n glInternalFormat = _gl.DEPTH24_STENCIL8;\\n } else {\\n glInternalFormat = _gl.DEPTH_COMPONENT16;\\n }\\n } else {\\n if (texture.type === FloatType) {\\n console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\");\\n }\\n }\\n if (texture.format === DepthFormat && glInternalFormat === _gl.DEPTH_COMPONENT) {\\n if (texture.type !== UnsignedShortType && texture.type !== UnsignedIntType) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\");\\n texture.type = UnsignedShortType;\\n glType = utils.convert(texture.type);\\n }\\n }\\n if (texture.format === DepthStencilFormat && glInternalFormat === _gl.DEPTH_COMPONENT) {\\n glInternalFormat = _gl.DEPTH_STENCIL;\\n if (texture.type !== UnsignedInt248Type) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\");\\n texture.type = UnsignedInt248Type;\\n glType = utils.convert(texture.type);\\n }\\n }\\n state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, image.width, image.height, 0, glFormat, glType, null);\\n } else if (texture.isDataTexture) {\\n if (mipmaps.length > 0 && supportsMips) {\\n for (let i = 0, il = mipmaps.length; i < il; i++) {\\n mipmap = mipmaps[i];\\n state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data);\\n }\\n texture.generateMipmaps = false;\\n textureProperties.__maxMipLevel = mipmaps.length - 1;\\n } else {\\n state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, image.width, image.height, 0, glFormat, glType, image.data);\\n textureProperties.__maxMipLevel = 0;\\n }\\n } else if (texture.isCompressedTexture) {\\n for (let i = 0, il = mipmaps.length; i < il; i++) {\\n mipmap = mipmaps[i];\\n if (texture.format !== RGBAFormat && texture.format !== RGBFormat) {\\n if (glFormat !== null) {\\n state.compressedTexImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, mipmap.data);\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()\\\\\\\");\\n }\\n } else {\\n state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data);\\n }\\n }\\n textureProperties.__maxMipLevel = mipmaps.length - 1;\\n } else if (texture.isDataTexture2DArray) {\\n state.texImage3D(_gl.TEXTURE_2D_ARRAY, 0, glInternalFormat, image.width, image.height, image.depth, 0, glFormat, glType, image.data);\\n textureProperties.__maxMipLevel = 0;\\n } else if (texture.isDataTexture3D) {\\n state.texImage3D(_gl.TEXTURE_3D, 0, glInternalFormat, image.width, image.height, image.depth, 0, glFormat, glType, image.data);\\n textureProperties.__maxMipLevel = 0;\\n } else {\\n if (mipmaps.length > 0 && supportsMips) {\\n for (let i = 0, il = mipmaps.length; i < il; i++) {\\n mipmap = mipmaps[i];\\n state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, glFormat, glType, mipmap);\\n }\\n texture.generateMipmaps = false;\\n textureProperties.__maxMipLevel = mipmaps.length - 1;\\n } else {\\n state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, glFormat, glType, image);\\n textureProperties.__maxMipLevel = 0;\\n }\\n }\\n if (textureNeedsGenerateMipmaps(texture, supportsMips)) {\\n generateMipmap(textureType, texture, image.width, image.height);\\n }\\n textureProperties.__version = texture.version;\\n if (texture.onUpdate)\\n texture.onUpdate(texture);\\n }\\n function uploadCubeTexture(textureProperties, texture, slot) {\\n if (texture.image.length !== 6)\\n return;\\n initTexture(textureProperties, texture);\\n state.activeTexture(_gl.TEXTURE0 + slot);\\n state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture);\\n _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, texture.flipY);\\n _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, texture.premultiplyAlpha);\\n _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, texture.unpackAlignment);\\n _gl.pixelStorei(_gl.UNPACK_COLORSPACE_CONVERSION_WEBGL, _gl.NONE);\\n const isCompressed = texture && (texture.isCompressedTexture || texture.image[0].isCompressedTexture);\\n const isDataTexture = texture.image[0] && texture.image[0].isDataTexture;\\n const cubeImage = [];\\n for (let i = 0; i < 6; i++) {\\n if (!isCompressed && !isDataTexture) {\\n cubeImage[i] = resizeImage(texture.image[i], false, true, maxCubemapSize);\\n } else {\\n cubeImage[i] = isDataTexture ? texture.image[i].image : texture.image[i];\\n }\\n }\\n const image = cubeImage[0], supportsMips = isPowerOfTwo2(image) || isWebGL2, glFormat = utils.convert(texture.format), glType = utils.convert(texture.type), glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.encoding);\\n setTextureParameters(_gl.TEXTURE_CUBE_MAP, texture, supportsMips);\\n let mipmaps;\\n if (isCompressed) {\\n for (let i = 0; i < 6; i++) {\\n mipmaps = cubeImage[i].mipmaps;\\n for (let j = 0; j < mipmaps.length; j++) {\\n const mipmap = mipmaps[j];\\n if (texture.format !== RGBAFormat && texture.format !== RGBFormat) {\\n if (glFormat !== null) {\\n state.compressedTexImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, glInternalFormat, mipmap.width, mipmap.height, 0, mipmap.data);\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()\\\\\\\");\\n }\\n } else {\\n state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data);\\n }\\n }\\n }\\n textureProperties.__maxMipLevel = mipmaps.length - 1;\\n } else {\\n mipmaps = texture.mipmaps;\\n for (let i = 0; i < 6; i++) {\\n if (isDataTexture) {\\n state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, glInternalFormat, cubeImage[i].width, cubeImage[i].height, 0, glFormat, glType, cubeImage[i].data);\\n for (let j = 0; j < mipmaps.length; j++) {\\n const mipmap = mipmaps[j];\\n const mipmapImage = mipmap.image[i].image;\\n state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, glInternalFormat, mipmapImage.width, mipmapImage.height, 0, glFormat, glType, mipmapImage.data);\\n }\\n } else {\\n state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, glInternalFormat, glFormat, glType, cubeImage[i]);\\n for (let j = 0; j < mipmaps.length; j++) {\\n const mipmap = mipmaps[j];\\n state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, glInternalFormat, glFormat, glType, mipmap.image[i]);\\n }\\n }\\n }\\n textureProperties.__maxMipLevel = mipmaps.length;\\n }\\n if (textureNeedsGenerateMipmaps(texture, supportsMips)) {\\n generateMipmap(_gl.TEXTURE_CUBE_MAP, texture, image.width, image.height);\\n }\\n textureProperties.__version = texture.version;\\n if (texture.onUpdate)\\n texture.onUpdate(texture);\\n }\\n function setupFrameBufferTexture(framebuffer, renderTarget, texture, attachment, textureTarget) {\\n const glFormat = utils.convert(texture.format);\\n const glType = utils.convert(texture.type);\\n const glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.encoding);\\n if (textureTarget === _gl.TEXTURE_3D || textureTarget === _gl.TEXTURE_2D_ARRAY) {\\n state.texImage3D(textureTarget, 0, glInternalFormat, renderTarget.width, renderTarget.height, renderTarget.depth, 0, glFormat, glType, null);\\n } else {\\n state.texImage2D(textureTarget, 0, glInternalFormat, renderTarget.width, renderTarget.height, 0, glFormat, glType, null);\\n }\\n state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer);\\n _gl.framebufferTexture2D(_gl.FRAMEBUFFER, attachment, textureTarget, properties.get(texture).__webglTexture, 0);\\n state.bindFramebuffer(_gl.FRAMEBUFFER, null);\\n }\\n function setupRenderBufferStorage(renderbuffer, renderTarget, isMultisample) {\\n _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderbuffer);\\n if (renderTarget.depthBuffer && !renderTarget.stencilBuffer) {\\n let glInternalFormat = _gl.DEPTH_COMPONENT16;\\n if (isMultisample) {\\n const depthTexture = renderTarget.depthTexture;\\n if (depthTexture && depthTexture.isDepthTexture) {\\n if (depthTexture.type === FloatType) {\\n glInternalFormat = _gl.DEPTH_COMPONENT32F;\\n } else if (depthTexture.type === UnsignedIntType) {\\n glInternalFormat = _gl.DEPTH_COMPONENT24;\\n }\\n }\\n const samples = getRenderTargetSamples(renderTarget);\\n _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height);\\n } else {\\n _gl.renderbufferStorage(_gl.RENDERBUFFER, glInternalFormat, renderTarget.width, renderTarget.height);\\n }\\n _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.DEPTH_ATTACHMENT, _gl.RENDERBUFFER, renderbuffer);\\n } else if (renderTarget.depthBuffer && renderTarget.stencilBuffer) {\\n if (isMultisample) {\\n const samples = getRenderTargetSamples(renderTarget);\\n _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, _gl.DEPTH24_STENCIL8, renderTarget.width, renderTarget.height);\\n } else {\\n _gl.renderbufferStorage(_gl.RENDERBUFFER, _gl.DEPTH_STENCIL, renderTarget.width, renderTarget.height);\\n }\\n _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.DEPTH_STENCIL_ATTACHMENT, _gl.RENDERBUFFER, renderbuffer);\\n } else {\\n const texture = renderTarget.isWebGLMultipleRenderTargets === true ? renderTarget.texture[0] : renderTarget.texture;\\n const glFormat = utils.convert(texture.format);\\n const glType = utils.convert(texture.type);\\n const glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.encoding);\\n if (isMultisample) {\\n const samples = getRenderTargetSamples(renderTarget);\\n _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height);\\n } else {\\n _gl.renderbufferStorage(_gl.RENDERBUFFER, glInternalFormat, renderTarget.width, renderTarget.height);\\n }\\n }\\n _gl.bindRenderbuffer(_gl.RENDERBUFFER, null);\\n }\\n function setupDepthTexture(framebuffer, renderTarget) {\\n const isCube = renderTarget && renderTarget.isWebGLCubeRenderTarget;\\n if (isCube)\\n throw new Error(\\\\\\\"Depth Texture with cube render targets is not supported\\\\\\\");\\n state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer);\\n if (!(renderTarget.depthTexture && renderTarget.depthTexture.isDepthTexture)) {\\n throw new Error(\\\\\\\"renderTarget.depthTexture must be an instance of THREE.DepthTexture\\\\\\\");\\n }\\n if (!properties.get(renderTarget.depthTexture).__webglTexture || renderTarget.depthTexture.image.width !== renderTarget.width || renderTarget.depthTexture.image.height !== renderTarget.height) {\\n renderTarget.depthTexture.image.width = renderTarget.width;\\n renderTarget.depthTexture.image.height = renderTarget.height;\\n renderTarget.depthTexture.needsUpdate = true;\\n }\\n setTexture2D(renderTarget.depthTexture, 0);\\n const webglDepthTexture = properties.get(renderTarget.depthTexture).__webglTexture;\\n if (renderTarget.depthTexture.format === DepthFormat) {\\n _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.DEPTH_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0);\\n } else if (renderTarget.depthTexture.format === DepthStencilFormat) {\\n _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.DEPTH_STENCIL_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0);\\n } else {\\n throw new Error(\\\\\\\"Unknown depthTexture format\\\\\\\");\\n }\\n }\\n function setupDepthRenderbuffer(renderTarget) {\\n const renderTargetProperties = properties.get(renderTarget);\\n const isCube = renderTarget.isWebGLCubeRenderTarget === true;\\n if (renderTarget.depthTexture) {\\n if (isCube)\\n throw new Error(\\\\\\\"target.depthTexture not supported in Cube render targets\\\\\\\");\\n setupDepthTexture(renderTargetProperties.__webglFramebuffer, renderTarget);\\n } else {\\n if (isCube) {\\n renderTargetProperties.__webglDepthbuffer = [];\\n for (let i = 0; i < 6; i++) {\\n state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer[i]);\\n renderTargetProperties.__webglDepthbuffer[i] = _gl.createRenderbuffer();\\n setupRenderBufferStorage(renderTargetProperties.__webglDepthbuffer[i], renderTarget, false);\\n }\\n } else {\\n state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer);\\n renderTargetProperties.__webglDepthbuffer = _gl.createRenderbuffer();\\n setupRenderBufferStorage(renderTargetProperties.__webglDepthbuffer, renderTarget, false);\\n }\\n }\\n state.bindFramebuffer(_gl.FRAMEBUFFER, null);\\n }\\n function setupRenderTarget(renderTarget) {\\n const texture = renderTarget.texture;\\n const renderTargetProperties = properties.get(renderTarget);\\n const textureProperties = properties.get(texture);\\n renderTarget.addEventListener(\\\\\\\"dispose\\\\\\\", onRenderTargetDispose);\\n if (renderTarget.isWebGLMultipleRenderTargets !== true) {\\n textureProperties.__webglTexture = _gl.createTexture();\\n textureProperties.__version = texture.version;\\n info.memory.textures++;\\n }\\n const isCube = renderTarget.isWebGLCubeRenderTarget === true;\\n const isMultipleRenderTargets = renderTarget.isWebGLMultipleRenderTargets === true;\\n const isMultisample = renderTarget.isWebGLMultisampleRenderTarget === true;\\n const isRenderTarget3D = texture.isDataTexture3D || texture.isDataTexture2DArray;\\n const supportsMips = isPowerOfTwo2(renderTarget) || isWebGL2;\\n if (isWebGL2 && texture.format === RGBFormat && (texture.type === FloatType || texture.type === HalfFloatType)) {\\n texture.format = RGBAFormat;\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\");\\n }\\n if (isCube) {\\n renderTargetProperties.__webglFramebuffer = [];\\n for (let i = 0; i < 6; i++) {\\n renderTargetProperties.__webglFramebuffer[i] = _gl.createFramebuffer();\\n }\\n } else {\\n renderTargetProperties.__webglFramebuffer = _gl.createFramebuffer();\\n if (isMultipleRenderTargets) {\\n if (capabilities.drawBuffers) {\\n const textures = renderTarget.texture;\\n for (let i = 0, il = textures.length; i < il; i++) {\\n const attachmentProperties = properties.get(textures[i]);\\n if (attachmentProperties.__webglTexture === void 0) {\\n attachmentProperties.__webglTexture = _gl.createTexture();\\n info.memory.textures++;\\n }\\n }\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.\\\\\\\");\\n }\\n } else if (isMultisample) {\\n if (isWebGL2) {\\n renderTargetProperties.__webglMultisampledFramebuffer = _gl.createFramebuffer();\\n renderTargetProperties.__webglColorRenderbuffer = _gl.createRenderbuffer();\\n _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer);\\n const glFormat = utils.convert(texture.format);\\n const glType = utils.convert(texture.type);\\n const glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.encoding);\\n const samples = getRenderTargetSamples(renderTarget);\\n _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height);\\n state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer);\\n _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, _gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer);\\n _gl.bindRenderbuffer(_gl.RENDERBUFFER, null);\\n if (renderTarget.depthBuffer) {\\n renderTargetProperties.__webglDepthRenderbuffer = _gl.createRenderbuffer();\\n setupRenderBufferStorage(renderTargetProperties.__webglDepthRenderbuffer, renderTarget, true);\\n }\\n state.bindFramebuffer(_gl.FRAMEBUFFER, null);\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\");\\n }\\n }\\n }\\n if (isCube) {\\n state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture);\\n setTextureParameters(_gl.TEXTURE_CUBE_MAP, texture, supportsMips);\\n for (let i = 0; i < 6; i++) {\\n setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer[i], renderTarget, texture, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_CUBE_MAP_POSITIVE_X + i);\\n }\\n if (textureNeedsGenerateMipmaps(texture, supportsMips)) {\\n generateMipmap(_gl.TEXTURE_CUBE_MAP, texture, renderTarget.width, renderTarget.height);\\n }\\n state.unbindTexture();\\n } else if (isMultipleRenderTargets) {\\n const textures = renderTarget.texture;\\n for (let i = 0, il = textures.length; i < il; i++) {\\n const attachment = textures[i];\\n const attachmentProperties = properties.get(attachment);\\n state.bindTexture(_gl.TEXTURE_2D, attachmentProperties.__webglTexture);\\n setTextureParameters(_gl.TEXTURE_2D, attachment, supportsMips);\\n setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer, renderTarget, attachment, _gl.COLOR_ATTACHMENT0 + i, _gl.TEXTURE_2D);\\n if (textureNeedsGenerateMipmaps(attachment, supportsMips)) {\\n generateMipmap(_gl.TEXTURE_2D, attachment, renderTarget.width, renderTarget.height);\\n }\\n }\\n state.unbindTexture();\\n } else {\\n let glTextureType = _gl.TEXTURE_2D;\\n if (isRenderTarget3D) {\\n if (isWebGL2) {\\n const isTexture3D = texture.isDataTexture3D;\\n glTextureType = isTexture3D ? _gl.TEXTURE_3D : _gl.TEXTURE_2D_ARRAY;\\n } else {\\n console.warn(\\\\\\\"THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.\\\\\\\");\\n }\\n }\\n state.bindTexture(glTextureType, textureProperties.__webglTexture);\\n setTextureParameters(glTextureType, texture, supportsMips);\\n setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer, renderTarget, texture, _gl.COLOR_ATTACHMENT0, glTextureType);\\n if (textureNeedsGenerateMipmaps(texture, supportsMips)) {\\n generateMipmap(glTextureType, texture, renderTarget.width, renderTarget.height, renderTarget.depth);\\n }\\n state.unbindTexture();\\n }\\n if (renderTarget.depthBuffer) {\\n setupDepthRenderbuffer(renderTarget);\\n }\\n }\\n function updateRenderTargetMipmap(renderTarget) {\\n const supportsMips = isPowerOfTwo2(renderTarget) || isWebGL2;\\n const textures = renderTarget.isWebGLMultipleRenderTargets === true ? renderTarget.texture : [renderTarget.texture];\\n for (let i = 0, il = textures.length; i < il; i++) {\\n const texture = textures[i];\\n if (textureNeedsGenerateMipmaps(texture, supportsMips)) {\\n const target = renderTarget.isWebGLCubeRenderTarget ? _gl.TEXTURE_CUBE_MAP : _gl.TEXTURE_2D;\\n const webglTexture = properties.get(texture).__webglTexture;\\n state.bindTexture(target, webglTexture);\\n generateMipmap(target, texture, renderTarget.width, renderTarget.height);\\n state.unbindTexture();\\n }\\n }\\n }\\n function updateMultisampleRenderTarget(renderTarget) {\\n if (renderTarget.isWebGLMultisampleRenderTarget) {\\n if (isWebGL2) {\\n const width = renderTarget.width;\\n const height = renderTarget.height;\\n let mask = _gl.COLOR_BUFFER_BIT;\\n if (renderTarget.depthBuffer)\\n mask |= _gl.DEPTH_BUFFER_BIT;\\n if (renderTarget.stencilBuffer)\\n mask |= _gl.STENCIL_BUFFER_BIT;\\n const renderTargetProperties = properties.get(renderTarget);\\n state.bindFramebuffer(_gl.READ_FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer);\\n state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, renderTargetProperties.__webglFramebuffer);\\n _gl.blitFramebuffer(0, 0, width, height, 0, 0, width, height, mask, _gl.NEAREST);\\n state.bindFramebuffer(_gl.READ_FRAMEBUFFER, null);\\n state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer);\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\");\\n }\\n }\\n }\\n function getRenderTargetSamples(renderTarget) {\\n return isWebGL2 && renderTarget.isWebGLMultisampleRenderTarget ? Math.min(maxSamples, renderTarget.samples) : 0;\\n }\\n function updateVideoTexture(texture) {\\n const frame = info.render.frame;\\n if (_videoTextures.get(texture) !== frame) {\\n _videoTextures.set(texture, frame);\\n texture.update();\\n }\\n }\\n let warnedTexture2D = false;\\n let warnedTextureCube = false;\\n function safeSetTexture2D(texture, slot) {\\n if (texture && texture.isWebGLRenderTarget) {\\n if (warnedTexture2D === false) {\\n console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead.\\\\\\\");\\n warnedTexture2D = true;\\n }\\n texture = texture.texture;\\n }\\n setTexture2D(texture, slot);\\n }\\n function safeSetTextureCube(texture, slot) {\\n if (texture && texture.isWebGLCubeRenderTarget) {\\n if (warnedTextureCube === false) {\\n console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead.\\\\\\\");\\n warnedTextureCube = true;\\n }\\n texture = texture.texture;\\n }\\n setTextureCube(texture, slot);\\n }\\n this.allocateTextureUnit = allocateTextureUnit;\\n this.resetTextureUnits = resetTextureUnits;\\n this.setTexture2D = setTexture2D;\\n this.setTexture2DArray = setTexture2DArray;\\n this.setTexture3D = setTexture3D;\\n this.setTextureCube = setTextureCube;\\n this.setupRenderTarget = setupRenderTarget;\\n this.updateRenderTargetMipmap = updateRenderTargetMipmap;\\n this.updateMultisampleRenderTarget = updateMultisampleRenderTarget;\\n this.safeSetTexture2D = safeSetTexture2D;\\n this.safeSetTextureCube = safeSetTextureCube;\\n}\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLUtils.js\\nfunction WebGLUtils(gl, extensions, capabilities) {\\n const isWebGL2 = capabilities.isWebGL2;\\n function convert(p) {\\n let extension;\\n if (p === UnsignedByteType)\\n return gl.UNSIGNED_BYTE;\\n if (p === UnsignedShort4444Type)\\n return gl.UNSIGNED_SHORT_4_4_4_4;\\n if (p === UnsignedShort5551Type)\\n return gl.UNSIGNED_SHORT_5_5_5_1;\\n if (p === UnsignedShort565Type)\\n return gl.UNSIGNED_SHORT_5_6_5;\\n if (p === ByteType)\\n return gl.BYTE;\\n if (p === ShortType)\\n return gl.SHORT;\\n if (p === UnsignedShortType)\\n return gl.UNSIGNED_SHORT;\\n if (p === IntType)\\n return gl.INT;\\n if (p === UnsignedIntType)\\n return gl.UNSIGNED_INT;\\n if (p === FloatType)\\n return gl.FLOAT;\\n if (p === HalfFloatType) {\\n if (isWebGL2)\\n return gl.HALF_FLOAT;\\n extension = extensions.get(\\\\\\\"OES_texture_half_float\\\\\\\");\\n if (extension !== null) {\\n return extension.HALF_FLOAT_OES;\\n } else {\\n return null;\\n }\\n }\\n if (p === AlphaFormat)\\n return gl.ALPHA;\\n if (p === RGBFormat)\\n return gl.RGB;\\n if (p === RGBAFormat)\\n return gl.RGBA;\\n if (p === LuminanceFormat)\\n return gl.LUMINANCE;\\n if (p === LuminanceAlphaFormat)\\n return gl.LUMINANCE_ALPHA;\\n if (p === DepthFormat)\\n return gl.DEPTH_COMPONENT;\\n if (p === DepthStencilFormat)\\n return gl.DEPTH_STENCIL;\\n if (p === RedFormat)\\n return gl.RED;\\n if (p === RedIntegerFormat)\\n return gl.RED_INTEGER;\\n if (p === RGFormat)\\n return gl.RG;\\n if (p === RGIntegerFormat)\\n return gl.RG_INTEGER;\\n if (p === RGBIntegerFormat)\\n return gl.RGB_INTEGER;\\n if (p === RGBAIntegerFormat)\\n return gl.RGBA_INTEGER;\\n if (p === RGB_S3TC_DXT1_Format || p === RGBA_S3TC_DXT1_Format || p === RGBA_S3TC_DXT3_Format || p === RGBA_S3TC_DXT5_Format) {\\n extension = extensions.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\");\\n if (extension !== null) {\\n if (p === RGB_S3TC_DXT1_Format)\\n return extension.COMPRESSED_RGB_S3TC_DXT1_EXT;\\n if (p === RGBA_S3TC_DXT1_Format)\\n return extension.COMPRESSED_RGBA_S3TC_DXT1_EXT;\\n if (p === RGBA_S3TC_DXT3_Format)\\n return extension.COMPRESSED_RGBA_S3TC_DXT3_EXT;\\n if (p === RGBA_S3TC_DXT5_Format)\\n return extension.COMPRESSED_RGBA_S3TC_DXT5_EXT;\\n } else {\\n return null;\\n }\\n }\\n if (p === RGB_PVRTC_4BPPV1_Format || p === RGB_PVRTC_2BPPV1_Format || p === RGBA_PVRTC_4BPPV1_Format || p === RGBA_PVRTC_2BPPV1_Format) {\\n extension = extensions.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\");\\n if (extension !== null) {\\n if (p === RGB_PVRTC_4BPPV1_Format)\\n return extension.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;\\n if (p === RGB_PVRTC_2BPPV1_Format)\\n return extension.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;\\n if (p === RGBA_PVRTC_4BPPV1_Format)\\n return extension.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;\\n if (p === RGBA_PVRTC_2BPPV1_Format)\\n return extension.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG;\\n } else {\\n return null;\\n }\\n }\\n if (p === RGB_ETC1_Format) {\\n extension = extensions.get(\\\\\\\"WEBGL_compressed_texture_etc1\\\\\\\");\\n if (extension !== null) {\\n return extension.COMPRESSED_RGB_ETC1_WEBGL;\\n } else {\\n return null;\\n }\\n }\\n if (p === RGB_ETC2_Format || p === RGBA_ETC2_EAC_Format) {\\n extension = extensions.get(\\\\\\\"WEBGL_compressed_texture_etc\\\\\\\");\\n if (extension !== null) {\\n if (p === RGB_ETC2_Format)\\n return extension.COMPRESSED_RGB8_ETC2;\\n if (p === RGBA_ETC2_EAC_Format)\\n return extension.COMPRESSED_RGBA8_ETC2_EAC;\\n }\\n }\\n if (p === RGBA_ASTC_4x4_Format || p === RGBA_ASTC_5x4_Format || p === RGBA_ASTC_5x5_Format || p === RGBA_ASTC_6x5_Format || p === RGBA_ASTC_6x6_Format || p === RGBA_ASTC_8x5_Format || p === RGBA_ASTC_8x6_Format || p === RGBA_ASTC_8x8_Format || p === RGBA_ASTC_10x5_Format || p === RGBA_ASTC_10x6_Format || p === RGBA_ASTC_10x8_Format || p === RGBA_ASTC_10x10_Format || p === RGBA_ASTC_12x10_Format || p === RGBA_ASTC_12x12_Format || p === SRGB8_ALPHA8_ASTC_4x4_Format || p === SRGB8_ALPHA8_ASTC_5x4_Format || p === SRGB8_ALPHA8_ASTC_5x5_Format || p === SRGB8_ALPHA8_ASTC_6x5_Format || p === SRGB8_ALPHA8_ASTC_6x6_Format || p === SRGB8_ALPHA8_ASTC_8x5_Format || p === SRGB8_ALPHA8_ASTC_8x6_Format || p === SRGB8_ALPHA8_ASTC_8x8_Format || p === SRGB8_ALPHA8_ASTC_10x5_Format || p === SRGB8_ALPHA8_ASTC_10x6_Format || p === SRGB8_ALPHA8_ASTC_10x8_Format || p === SRGB8_ALPHA8_ASTC_10x10_Format || p === SRGB8_ALPHA8_ASTC_12x10_Format || p === SRGB8_ALPHA8_ASTC_12x12_Format) {\\n extension = extensions.get(\\\\\\\"WEBGL_compressed_texture_astc\\\\\\\");\\n if (extension !== null) {\\n return p;\\n } else {\\n return null;\\n }\\n }\\n if (p === RGBA_BPTC_Format) {\\n extension = extensions.get(\\\\\\\"EXT_texture_compression_bptc\\\\\\\");\\n if (extension !== null) {\\n return p;\\n } else {\\n return null;\\n }\\n }\\n if (p === UnsignedInt248Type) {\\n if (isWebGL2)\\n return gl.UNSIGNED_INT_24_8;\\n extension = extensions.get(\\\\\\\"WEBGL_depth_texture\\\\\\\");\\n if (extension !== null) {\\n return extension.UNSIGNED_INT_24_8_WEBGL;\\n } else {\\n return null;\\n }\\n }\\n }\\n return {convert};\\n}\\n\\n// ../../../node_modules/three/src/cameras/ArrayCamera.js\\nvar ArrayCamera = class extends PerspectiveCamera {\\n constructor(array = []) {\\n super();\\n this.cameras = array;\\n }\\n};\\nArrayCamera.prototype.isArrayCamera = true;\\n\\n// ../../../node_modules/three/src/objects/Group.js\\nvar Group = class extends Object3D {\\n constructor() {\\n super();\\n this.type = \\\\\\\"Group\\\\\\\";\\n }\\n};\\nGroup.prototype.isGroup = true;\\n\\n// ../../../node_modules/three/src/renderers/webxr/WebXRController.js\\nvar _moveEvent = {type: \\\\\\\"move\\\\\\\"};\\nvar WebXRController = class {\\n constructor() {\\n this._targetRay = null;\\n this._grip = null;\\n this._hand = null;\\n }\\n getHandSpace() {\\n if (this._hand === null) {\\n this._hand = new Group();\\n this._hand.matrixAutoUpdate = false;\\n this._hand.visible = false;\\n this._hand.joints = {};\\n this._hand.inputState = {pinching: false};\\n }\\n return this._hand;\\n }\\n getTargetRaySpace() {\\n if (this._targetRay === null) {\\n this._targetRay = new Group();\\n this._targetRay.matrixAutoUpdate = false;\\n this._targetRay.visible = false;\\n this._targetRay.hasLinearVelocity = false;\\n this._targetRay.linearVelocity = new Vector3();\\n this._targetRay.hasAngularVelocity = false;\\n this._targetRay.angularVelocity = new Vector3();\\n }\\n return this._targetRay;\\n }\\n getGripSpace() {\\n if (this._grip === null) {\\n this._grip = new Group();\\n this._grip.matrixAutoUpdate = false;\\n this._grip.visible = false;\\n this._grip.hasLinearVelocity = false;\\n this._grip.linearVelocity = new Vector3();\\n this._grip.hasAngularVelocity = false;\\n this._grip.angularVelocity = new Vector3();\\n }\\n return this._grip;\\n }\\n dispatchEvent(event) {\\n if (this._targetRay !== null) {\\n this._targetRay.dispatchEvent(event);\\n }\\n if (this._grip !== null) {\\n this._grip.dispatchEvent(event);\\n }\\n if (this._hand !== null) {\\n this._hand.dispatchEvent(event);\\n }\\n return this;\\n }\\n disconnect(inputSource) {\\n this.dispatchEvent({type: \\\\\\\"disconnected\\\\\\\", data: inputSource});\\n if (this._targetRay !== null) {\\n this._targetRay.visible = false;\\n }\\n if (this._grip !== null) {\\n this._grip.visible = false;\\n }\\n if (this._hand !== null) {\\n this._hand.visible = false;\\n }\\n return this;\\n }\\n update(inputSource, frame, referenceSpace) {\\n let inputPose = null;\\n let gripPose = null;\\n let handPose = null;\\n const targetRay = this._targetRay;\\n const grip = this._grip;\\n const hand = this._hand;\\n if (inputSource && frame.session.visibilityState !== \\\\\\\"visible-blurred\\\\\\\") {\\n if (targetRay !== null) {\\n inputPose = frame.getPose(inputSource.targetRaySpace, referenceSpace);\\n if (inputPose !== null) {\\n targetRay.matrix.fromArray(inputPose.transform.matrix);\\n targetRay.matrix.decompose(targetRay.position, targetRay.rotation, targetRay.scale);\\n if (inputPose.linearVelocity) {\\n targetRay.hasLinearVelocity = true;\\n targetRay.linearVelocity.copy(inputPose.linearVelocity);\\n } else {\\n targetRay.hasLinearVelocity = false;\\n }\\n if (inputPose.angularVelocity) {\\n targetRay.hasAngularVelocity = true;\\n targetRay.angularVelocity.copy(inputPose.angularVelocity);\\n } else {\\n targetRay.hasAngularVelocity = false;\\n }\\n this.dispatchEvent(_moveEvent);\\n }\\n }\\n if (hand && inputSource.hand) {\\n handPose = true;\\n for (const inputjoint of inputSource.hand.values()) {\\n const jointPose = frame.getJointPose(inputjoint, referenceSpace);\\n if (hand.joints[inputjoint.jointName] === void 0) {\\n const joint2 = new Group();\\n joint2.matrixAutoUpdate = false;\\n joint2.visible = false;\\n hand.joints[inputjoint.jointName] = joint2;\\n hand.add(joint2);\\n }\\n const joint = hand.joints[inputjoint.jointName];\\n if (jointPose !== null) {\\n joint.matrix.fromArray(jointPose.transform.matrix);\\n joint.matrix.decompose(joint.position, joint.rotation, joint.scale);\\n joint.jointRadius = jointPose.radius;\\n }\\n joint.visible = jointPose !== null;\\n }\\n const indexTip = hand.joints[\\\\\\\"index-finger-tip\\\\\\\"];\\n const thumbTip = hand.joints[\\\\\\\"thumb-tip\\\\\\\"];\\n const distance = indexTip.position.distanceTo(thumbTip.position);\\n const distanceToPinch = 0.02;\\n const threshold = 5e-3;\\n if (hand.inputState.pinching && distance > distanceToPinch + threshold) {\\n hand.inputState.pinching = false;\\n this.dispatchEvent({\\n type: \\\\\\\"pinchend\\\\\\\",\\n handedness: inputSource.handedness,\\n target: this\\n });\\n } else if (!hand.inputState.pinching && distance <= distanceToPinch - threshold) {\\n hand.inputState.pinching = true;\\n this.dispatchEvent({\\n type: \\\\\\\"pinchstart\\\\\\\",\\n handedness: inputSource.handedness,\\n target: this\\n });\\n }\\n } else {\\n if (grip !== null && inputSource.gripSpace) {\\n gripPose = frame.getPose(inputSource.gripSpace, referenceSpace);\\n if (gripPose !== null) {\\n grip.matrix.fromArray(gripPose.transform.matrix);\\n grip.matrix.decompose(grip.position, grip.rotation, grip.scale);\\n if (gripPose.linearVelocity) {\\n grip.hasLinearVelocity = true;\\n grip.linearVelocity.copy(gripPose.linearVelocity);\\n } else {\\n grip.hasLinearVelocity = false;\\n }\\n if (gripPose.angularVelocity) {\\n grip.hasAngularVelocity = true;\\n grip.angularVelocity.copy(gripPose.angularVelocity);\\n } else {\\n grip.hasAngularVelocity = false;\\n }\\n }\\n }\\n }\\n }\\n if (targetRay !== null) {\\n targetRay.visible = inputPose !== null;\\n }\\n if (grip !== null) {\\n grip.visible = gripPose !== null;\\n }\\n if (hand !== null) {\\n hand.visible = handPose !== null;\\n }\\n return this;\\n }\\n};\\n\\n// ../../../node_modules/three/src/renderers/webxr/WebXRManager.js\\nvar WebXRManager = class extends EventDispatcher {\\n constructor(renderer, gl) {\\n super();\\n const scope = this;\\n const state = renderer.state;\\n let session = null;\\n let framebufferScaleFactor = 1;\\n let referenceSpace = null;\\n let referenceSpaceType = \\\\\\\"local-floor\\\\\\\";\\n let pose = null;\\n let glBinding = null;\\n let glFramebuffer = null;\\n let glProjLayer = null;\\n let glBaseLayer = null;\\n let isMultisample = false;\\n let glMultisampledFramebuffer = null;\\n let glColorRenderbuffer = null;\\n let glDepthRenderbuffer = null;\\n let xrFrame = null;\\n let depthStyle = null;\\n let clearStyle = null;\\n const controllers = [];\\n const inputSourcesMap = new Map();\\n const cameraL = new PerspectiveCamera();\\n cameraL.layers.enable(1);\\n cameraL.viewport = new Vector4();\\n const cameraR = new PerspectiveCamera();\\n cameraR.layers.enable(2);\\n cameraR.viewport = new Vector4();\\n const cameras = [cameraL, cameraR];\\n const cameraVR = new ArrayCamera();\\n cameraVR.layers.enable(1);\\n cameraVR.layers.enable(2);\\n let _currentDepthNear = null;\\n let _currentDepthFar = null;\\n this.cameraAutoUpdate = true;\\n this.enabled = false;\\n this.isPresenting = false;\\n this.getController = function(index) {\\n let controller = controllers[index];\\n if (controller === void 0) {\\n controller = new WebXRController();\\n controllers[index] = controller;\\n }\\n return controller.getTargetRaySpace();\\n };\\n this.getControllerGrip = function(index) {\\n let controller = controllers[index];\\n if (controller === void 0) {\\n controller = new WebXRController();\\n controllers[index] = controller;\\n }\\n return controller.getGripSpace();\\n };\\n this.getHand = function(index) {\\n let controller = controllers[index];\\n if (controller === void 0) {\\n controller = new WebXRController();\\n controllers[index] = controller;\\n }\\n return controller.getHandSpace();\\n };\\n function onSessionEvent(event) {\\n const controller = inputSourcesMap.get(event.inputSource);\\n if (controller) {\\n controller.dispatchEvent({type: event.type, data: event.inputSource});\\n }\\n }\\n function onSessionEnd() {\\n inputSourcesMap.forEach(function(controller, inputSource) {\\n controller.disconnect(inputSource);\\n });\\n inputSourcesMap.clear();\\n _currentDepthNear = null;\\n _currentDepthFar = null;\\n state.bindXRFramebuffer(null);\\n renderer.setRenderTarget(renderer.getRenderTarget());\\n if (glFramebuffer)\\n gl.deleteFramebuffer(glFramebuffer);\\n if (glMultisampledFramebuffer)\\n gl.deleteFramebuffer(glMultisampledFramebuffer);\\n if (glColorRenderbuffer)\\n gl.deleteRenderbuffer(glColorRenderbuffer);\\n if (glDepthRenderbuffer)\\n gl.deleteRenderbuffer(glDepthRenderbuffer);\\n glFramebuffer = null;\\n glMultisampledFramebuffer = null;\\n glColorRenderbuffer = null;\\n glDepthRenderbuffer = null;\\n glBaseLayer = null;\\n glProjLayer = null;\\n glBinding = null;\\n session = null;\\n animation.stop();\\n scope.isPresenting = false;\\n scope.dispatchEvent({type: \\\\\\\"sessionend\\\\\\\"});\\n }\\n this.setFramebufferScaleFactor = function(value) {\\n framebufferScaleFactor = value;\\n if (scope.isPresenting === true) {\\n console.warn(\\\\\\\"THREE.WebXRManager: Cannot change framebuffer scale while presenting.\\\\\\\");\\n }\\n };\\n this.setReferenceSpaceType = function(value) {\\n referenceSpaceType = value;\\n if (scope.isPresenting === true) {\\n console.warn(\\\\\\\"THREE.WebXRManager: Cannot change reference space type while presenting.\\\\\\\");\\n }\\n };\\n this.getReferenceSpace = function() {\\n return referenceSpace;\\n };\\n this.getBaseLayer = function() {\\n return glProjLayer !== null ? glProjLayer : glBaseLayer;\\n };\\n this.getBinding = function() {\\n return glBinding;\\n };\\n this.getFrame = function() {\\n return xrFrame;\\n };\\n this.getSession = function() {\\n return session;\\n };\\n this.setSession = async function(value) {\\n session = value;\\n if (session !== null) {\\n session.addEventListener(\\\\\\\"select\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"selectstart\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"selectend\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"squeeze\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"squeezestart\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"squeezeend\\\\\\\", onSessionEvent);\\n session.addEventListener(\\\\\\\"end\\\\\\\", onSessionEnd);\\n session.addEventListener(\\\\\\\"inputsourceschange\\\\\\\", onInputSourcesChange);\\n const attributes = gl.getContextAttributes();\\n if (attributes.xrCompatible !== true) {\\n await gl.makeXRCompatible();\\n }\\n if (session.renderState.layers === void 0) {\\n const layerInit = {\\n antialias: attributes.antialias,\\n alpha: attributes.alpha,\\n depth: attributes.depth,\\n stencil: attributes.stencil,\\n framebufferScaleFactor\\n };\\n glBaseLayer = new XRWebGLLayer(session, gl, layerInit);\\n session.updateRenderState({baseLayer: glBaseLayer});\\n } else if (gl instanceof WebGLRenderingContext) {\\n const layerInit = {\\n antialias: true,\\n alpha: attributes.alpha,\\n depth: attributes.depth,\\n stencil: attributes.stencil,\\n framebufferScaleFactor\\n };\\n glBaseLayer = new XRWebGLLayer(session, gl, layerInit);\\n session.updateRenderState({layers: [glBaseLayer]});\\n } else {\\n isMultisample = attributes.antialias;\\n let depthFormat = null;\\n if (attributes.depth) {\\n clearStyle = gl.DEPTH_BUFFER_BIT;\\n if (attributes.stencil)\\n clearStyle |= gl.STENCIL_BUFFER_BIT;\\n depthStyle = attributes.stencil ? gl.DEPTH_STENCIL_ATTACHMENT : gl.DEPTH_ATTACHMENT;\\n depthFormat = attributes.stencil ? gl.DEPTH24_STENCIL8 : gl.DEPTH_COMPONENT24;\\n }\\n const projectionlayerInit = {\\n colorFormat: attributes.alpha ? gl.RGBA8 : gl.RGB8,\\n depthFormat,\\n scaleFactor: framebufferScaleFactor\\n };\\n glBinding = new XRWebGLBinding(session, gl);\\n glProjLayer = glBinding.createProjectionLayer(projectionlayerInit);\\n glFramebuffer = gl.createFramebuffer();\\n session.updateRenderState({layers: [glProjLayer]});\\n if (isMultisample) {\\n glMultisampledFramebuffer = gl.createFramebuffer();\\n glColorRenderbuffer = gl.createRenderbuffer();\\n gl.bindRenderbuffer(gl.RENDERBUFFER, glColorRenderbuffer);\\n gl.renderbufferStorageMultisample(gl.RENDERBUFFER, 4, gl.RGBA8, glProjLayer.textureWidth, glProjLayer.textureHeight);\\n state.bindFramebuffer(gl.FRAMEBUFFER, glMultisampledFramebuffer);\\n gl.framebufferRenderbuffer(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.RENDERBUFFER, glColorRenderbuffer);\\n gl.bindRenderbuffer(gl.RENDERBUFFER, null);\\n if (depthFormat !== null) {\\n glDepthRenderbuffer = gl.createRenderbuffer();\\n gl.bindRenderbuffer(gl.RENDERBUFFER, glDepthRenderbuffer);\\n gl.renderbufferStorageMultisample(gl.RENDERBUFFER, 4, depthFormat, glProjLayer.textureWidth, glProjLayer.textureHeight);\\n gl.framebufferRenderbuffer(gl.FRAMEBUFFER, depthStyle, gl.RENDERBUFFER, glDepthRenderbuffer);\\n gl.bindRenderbuffer(gl.RENDERBUFFER, null);\\n }\\n state.bindFramebuffer(gl.FRAMEBUFFER, null);\\n }\\n }\\n referenceSpace = await session.requestReferenceSpace(referenceSpaceType);\\n animation.setContext(session);\\n animation.start();\\n scope.isPresenting = true;\\n scope.dispatchEvent({type: \\\\\\\"sessionstart\\\\\\\"});\\n }\\n };\\n function onInputSourcesChange(event) {\\n const inputSources = session.inputSources;\\n for (let i = 0; i < controllers.length; i++) {\\n inputSourcesMap.set(inputSources[i], controllers[i]);\\n }\\n for (let i = 0; i < event.removed.length; i++) {\\n const inputSource = event.removed[i];\\n const controller = inputSourcesMap.get(inputSource);\\n if (controller) {\\n controller.dispatchEvent({type: \\\\\\\"disconnected\\\\\\\", data: inputSource});\\n inputSourcesMap.delete(inputSource);\\n }\\n }\\n for (let i = 0; i < event.added.length; i++) {\\n const inputSource = event.added[i];\\n const controller = inputSourcesMap.get(inputSource);\\n if (controller) {\\n controller.dispatchEvent({type: \\\\\\\"connected\\\\\\\", data: inputSource});\\n }\\n }\\n }\\n const cameraLPos = new Vector3();\\n const cameraRPos = new Vector3();\\n function setProjectionFromUnion(camera, cameraL2, cameraR2) {\\n cameraLPos.setFromMatrixPosition(cameraL2.matrixWorld);\\n cameraRPos.setFromMatrixPosition(cameraR2.matrixWorld);\\n const ipd = cameraLPos.distanceTo(cameraRPos);\\n const projL = cameraL2.projectionMatrix.elements;\\n const projR = cameraR2.projectionMatrix.elements;\\n const near = projL[14] / (projL[10] - 1);\\n const far = projL[14] / (projL[10] + 1);\\n const topFov = (projL[9] + 1) / projL[5];\\n const bottomFov = (projL[9] - 1) / projL[5];\\n const leftFov = (projL[8] - 1) / projL[0];\\n const rightFov = (projR[8] + 1) / projR[0];\\n const left = near * leftFov;\\n const right = near * rightFov;\\n const zOffset = ipd / (-leftFov + rightFov);\\n const xOffset = zOffset * -leftFov;\\n cameraL2.matrixWorld.decompose(camera.position, camera.quaternion, camera.scale);\\n camera.translateX(xOffset);\\n camera.translateZ(zOffset);\\n camera.matrixWorld.compose(camera.position, camera.quaternion, camera.scale);\\n camera.matrixWorldInverse.copy(camera.matrixWorld).invert();\\n const near2 = near + zOffset;\\n const far2 = far + zOffset;\\n const left2 = left - xOffset;\\n const right2 = right + (ipd - xOffset);\\n const top2 = topFov * far / far2 * near2;\\n const bottom2 = bottomFov * far / far2 * near2;\\n camera.projectionMatrix.makePerspective(left2, right2, top2, bottom2, near2, far2);\\n }\\n function updateCamera(camera, parent) {\\n if (parent === null) {\\n camera.matrixWorld.copy(camera.matrix);\\n } else {\\n camera.matrixWorld.multiplyMatrices(parent.matrixWorld, camera.matrix);\\n }\\n camera.matrixWorldInverse.copy(camera.matrixWorld).invert();\\n }\\n this.updateCamera = function(camera) {\\n if (session === null)\\n return;\\n cameraVR.near = cameraR.near = cameraL.near = camera.near;\\n cameraVR.far = cameraR.far = cameraL.far = camera.far;\\n if (_currentDepthNear !== cameraVR.near || _currentDepthFar !== cameraVR.far) {\\n session.updateRenderState({\\n depthNear: cameraVR.near,\\n depthFar: cameraVR.far\\n });\\n _currentDepthNear = cameraVR.near;\\n _currentDepthFar = cameraVR.far;\\n }\\n const parent = camera.parent;\\n const cameras2 = cameraVR.cameras;\\n updateCamera(cameraVR, parent);\\n for (let i = 0; i < cameras2.length; i++) {\\n updateCamera(cameras2[i], parent);\\n }\\n cameraVR.matrixWorld.decompose(cameraVR.position, cameraVR.quaternion, cameraVR.scale);\\n camera.position.copy(cameraVR.position);\\n camera.quaternion.copy(cameraVR.quaternion);\\n camera.scale.copy(cameraVR.scale);\\n camera.matrix.copy(cameraVR.matrix);\\n camera.matrixWorld.copy(cameraVR.matrixWorld);\\n const children = camera.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n children[i].updateMatrixWorld(true);\\n }\\n if (cameras2.length === 2) {\\n setProjectionFromUnion(cameraVR, cameraL, cameraR);\\n } else {\\n cameraVR.projectionMatrix.copy(cameraL.projectionMatrix);\\n }\\n };\\n this.getCamera = function() {\\n return cameraVR;\\n };\\n this.getFoveation = function() {\\n if (glProjLayer !== null) {\\n return glProjLayer.fixedFoveation;\\n }\\n if (glBaseLayer !== null) {\\n return glBaseLayer.fixedFoveation;\\n }\\n return void 0;\\n };\\n this.setFoveation = function(foveation) {\\n if (glProjLayer !== null) {\\n glProjLayer.fixedFoveation = foveation;\\n }\\n if (glBaseLayer !== null && glBaseLayer.fixedFoveation !== void 0) {\\n glBaseLayer.fixedFoveation = foveation;\\n }\\n };\\n let onAnimationFrameCallback = null;\\n function onAnimationFrame(time, frame) {\\n pose = frame.getViewerPose(referenceSpace);\\n xrFrame = frame;\\n if (pose !== null) {\\n const views = pose.views;\\n if (glBaseLayer !== null) {\\n state.bindXRFramebuffer(glBaseLayer.framebuffer);\\n }\\n let cameraVRNeedsUpdate = false;\\n if (views.length !== cameraVR.cameras.length) {\\n cameraVR.cameras.length = 0;\\n cameraVRNeedsUpdate = true;\\n }\\n for (let i = 0; i < views.length; i++) {\\n const view = views[i];\\n let viewport = null;\\n if (glBaseLayer !== null) {\\n viewport = glBaseLayer.getViewport(view);\\n } else {\\n const glSubImage = glBinding.getViewSubImage(glProjLayer, view);\\n state.bindXRFramebuffer(glFramebuffer);\\n if (glSubImage.depthStencilTexture !== void 0) {\\n gl.framebufferTexture2D(gl.FRAMEBUFFER, depthStyle, gl.TEXTURE_2D, glSubImage.depthStencilTexture, 0);\\n }\\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, glSubImage.colorTexture, 0);\\n viewport = glSubImage.viewport;\\n }\\n const camera = cameras[i];\\n camera.matrix.fromArray(view.transform.matrix);\\n camera.projectionMatrix.fromArray(view.projectionMatrix);\\n camera.viewport.set(viewport.x, viewport.y, viewport.width, viewport.height);\\n if (i === 0) {\\n cameraVR.matrix.copy(camera.matrix);\\n }\\n if (cameraVRNeedsUpdate === true) {\\n cameraVR.cameras.push(camera);\\n }\\n }\\n if (isMultisample) {\\n state.bindXRFramebuffer(glMultisampledFramebuffer);\\n if (clearStyle !== null)\\n gl.clear(clearStyle);\\n }\\n }\\n const inputSources = session.inputSources;\\n for (let i = 0; i < controllers.length; i++) {\\n const controller = controllers[i];\\n const inputSource = inputSources[i];\\n controller.update(inputSource, frame, referenceSpace);\\n }\\n if (onAnimationFrameCallback)\\n onAnimationFrameCallback(time, frame);\\n if (isMultisample) {\\n const width = glProjLayer.textureWidth;\\n const height = glProjLayer.textureHeight;\\n state.bindFramebuffer(gl.READ_FRAMEBUFFER, glMultisampledFramebuffer);\\n state.bindFramebuffer(gl.DRAW_FRAMEBUFFER, glFramebuffer);\\n gl.invalidateFramebuffer(gl.READ_FRAMEBUFFER, [depthStyle]);\\n gl.invalidateFramebuffer(gl.DRAW_FRAMEBUFFER, [depthStyle]);\\n gl.blitFramebuffer(0, 0, width, height, 0, 0, width, height, gl.COLOR_BUFFER_BIT, gl.NEAREST);\\n gl.invalidateFramebuffer(gl.READ_FRAMEBUFFER, [gl.COLOR_ATTACHMENT0]);\\n state.bindFramebuffer(gl.READ_FRAMEBUFFER, null);\\n state.bindFramebuffer(gl.DRAW_FRAMEBUFFER, null);\\n state.bindFramebuffer(gl.FRAMEBUFFER, glMultisampledFramebuffer);\\n }\\n xrFrame = null;\\n }\\n const animation = new WebGLAnimation();\\n animation.setAnimationLoop(onAnimationFrame);\\n this.setAnimationLoop = function(callback) {\\n onAnimationFrameCallback = callback;\\n };\\n this.dispose = function() {\\n };\\n }\\n};\\n\\n// ../../../node_modules/three/src/renderers/webgl/WebGLMaterials.js\\nfunction WebGLMaterials(properties) {\\n function refreshFogUniforms(uniforms, fog) {\\n uniforms.fogColor.value.copy(fog.color);\\n if (fog.isFog) {\\n uniforms.fogNear.value = fog.near;\\n uniforms.fogFar.value = fog.far;\\n } else if (fog.isFogExp2) {\\n uniforms.fogDensity.value = fog.density;\\n }\\n }\\n function refreshMaterialUniforms(uniforms, material, pixelRatio, height, transmissionRenderTarget) {\\n if (material.isMeshBasicMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n } else if (material.isMeshLambertMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsLambert(uniforms, material);\\n } else if (material.isMeshToonMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsToon(uniforms, material);\\n } else if (material.isMeshPhongMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsPhong(uniforms, material);\\n } else if (material.isMeshStandardMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n if (material.isMeshPhysicalMaterial) {\\n refreshUniformsPhysical(uniforms, material, transmissionRenderTarget);\\n } else {\\n refreshUniformsStandard(uniforms, material);\\n }\\n } else if (material.isMeshMatcapMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsMatcap(uniforms, material);\\n } else if (material.isMeshDepthMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsDepth(uniforms, material);\\n } else if (material.isMeshDistanceMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsDistance(uniforms, material);\\n } else if (material.isMeshNormalMaterial) {\\n refreshUniformsCommon(uniforms, material);\\n refreshUniformsNormal(uniforms, material);\\n } else if (material.isLineBasicMaterial) {\\n refreshUniformsLine(uniforms, material);\\n if (material.isLineDashedMaterial) {\\n refreshUniformsDash(uniforms, material);\\n }\\n } else if (material.isPointsMaterial) {\\n refreshUniformsPoints(uniforms, material, pixelRatio, height);\\n } else if (material.isSpriteMaterial) {\\n refreshUniformsSprites(uniforms, material);\\n } else if (material.isShadowMaterial) {\\n uniforms.color.value.copy(material.color);\\n uniforms.opacity.value = material.opacity;\\n } else if (material.isShaderMaterial) {\\n material.uniformsNeedUpdate = false;\\n }\\n }\\n function refreshUniformsCommon(uniforms, material) {\\n uniforms.opacity.value = material.opacity;\\n if (material.color) {\\n uniforms.diffuse.value.copy(material.color);\\n }\\n if (material.emissive) {\\n uniforms.emissive.value.copy(material.emissive).multiplyScalar(material.emissiveIntensity);\\n }\\n if (material.map) {\\n uniforms.map.value = material.map;\\n }\\n if (material.alphaMap) {\\n uniforms.alphaMap.value = material.alphaMap;\\n }\\n if (material.specularMap) {\\n uniforms.specularMap.value = material.specularMap;\\n }\\n if (material.alphaTest > 0) {\\n uniforms.alphaTest.value = material.alphaTest;\\n }\\n const envMap = properties.get(material).envMap;\\n if (envMap) {\\n uniforms.envMap.value = envMap;\\n uniforms.flipEnvMap.value = envMap.isCubeTexture && envMap.isRenderTargetTexture === false ? -1 : 1;\\n uniforms.reflectivity.value = material.reflectivity;\\n uniforms.ior.value = material.ior;\\n uniforms.refractionRatio.value = material.refractionRatio;\\n const maxMipLevel = properties.get(envMap).__maxMipLevel;\\n if (maxMipLevel !== void 0) {\\n uniforms.maxMipLevel.value = maxMipLevel;\\n }\\n }\\n if (material.lightMap) {\\n uniforms.lightMap.value = material.lightMap;\\n uniforms.lightMapIntensity.value = material.lightMapIntensity;\\n }\\n if (material.aoMap) {\\n uniforms.aoMap.value = material.aoMap;\\n uniforms.aoMapIntensity.value = material.aoMapIntensity;\\n }\\n let uvScaleMap;\\n if (material.map) {\\n uvScaleMap = material.map;\\n } else if (material.specularMap) {\\n uvScaleMap = material.specularMap;\\n } else if (material.displacementMap) {\\n uvScaleMap = material.displacementMap;\\n } else if (material.normalMap) {\\n uvScaleMap = material.normalMap;\\n } else if (material.bumpMap) {\\n uvScaleMap = material.bumpMap;\\n } else if (material.roughnessMap) {\\n uvScaleMap = material.roughnessMap;\\n } else if (material.metalnessMap) {\\n uvScaleMap = material.metalnessMap;\\n } else if (material.alphaMap) {\\n uvScaleMap = material.alphaMap;\\n } else if (material.emissiveMap) {\\n uvScaleMap = material.emissiveMap;\\n } else if (material.clearcoatMap) {\\n uvScaleMap = material.clearcoatMap;\\n } else if (material.clearcoatNormalMap) {\\n uvScaleMap = material.clearcoatNormalMap;\\n } else if (material.clearcoatRoughnessMap) {\\n uvScaleMap = material.clearcoatRoughnessMap;\\n } else if (material.specularIntensityMap) {\\n uvScaleMap = material.specularIntensityMap;\\n } else if (material.specularTintMap) {\\n uvScaleMap = material.specularTintMap;\\n } else if (material.transmissionMap) {\\n uvScaleMap = material.transmissionMap;\\n } else if (material.thicknessMap) {\\n uvScaleMap = material.thicknessMap;\\n }\\n if (uvScaleMap !== void 0) {\\n if (uvScaleMap.isWebGLRenderTarget) {\\n uvScaleMap = uvScaleMap.texture;\\n }\\n if (uvScaleMap.matrixAutoUpdate === true) {\\n uvScaleMap.updateMatrix();\\n }\\n uniforms.uvTransform.value.copy(uvScaleMap.matrix);\\n }\\n let uv2ScaleMap;\\n if (material.aoMap) {\\n uv2ScaleMap = material.aoMap;\\n } else if (material.lightMap) {\\n uv2ScaleMap = material.lightMap;\\n }\\n if (uv2ScaleMap !== void 0) {\\n if (uv2ScaleMap.isWebGLRenderTarget) {\\n uv2ScaleMap = uv2ScaleMap.texture;\\n }\\n if (uv2ScaleMap.matrixAutoUpdate === true) {\\n uv2ScaleMap.updateMatrix();\\n }\\n uniforms.uv2Transform.value.copy(uv2ScaleMap.matrix);\\n }\\n }\\n function refreshUniformsLine(uniforms, material) {\\n uniforms.diffuse.value.copy(material.color);\\n uniforms.opacity.value = material.opacity;\\n }\\n function refreshUniformsDash(uniforms, material) {\\n uniforms.dashSize.value = material.dashSize;\\n uniforms.totalSize.value = material.dashSize + material.gapSize;\\n uniforms.scale.value = material.scale;\\n }\\n function refreshUniformsPoints(uniforms, material, pixelRatio, height) {\\n uniforms.diffuse.value.copy(material.color);\\n uniforms.opacity.value = material.opacity;\\n uniforms.size.value = material.size * pixelRatio;\\n uniforms.scale.value = height * 0.5;\\n if (material.map) {\\n uniforms.map.value = material.map;\\n }\\n if (material.alphaMap) {\\n uniforms.alphaMap.value = material.alphaMap;\\n }\\n if (material.alphaTest > 0) {\\n uniforms.alphaTest.value = material.alphaTest;\\n }\\n let uvScaleMap;\\n if (material.map) {\\n uvScaleMap = material.map;\\n } else if (material.alphaMap) {\\n uvScaleMap = material.alphaMap;\\n }\\n if (uvScaleMap !== void 0) {\\n if (uvScaleMap.matrixAutoUpdate === true) {\\n uvScaleMap.updateMatrix();\\n }\\n uniforms.uvTransform.value.copy(uvScaleMap.matrix);\\n }\\n }\\n function refreshUniformsSprites(uniforms, material) {\\n uniforms.diffuse.value.copy(material.color);\\n uniforms.opacity.value = material.opacity;\\n uniforms.rotation.value = material.rotation;\\n if (material.map) {\\n uniforms.map.value = material.map;\\n }\\n if (material.alphaMap) {\\n uniforms.alphaMap.value = material.alphaMap;\\n }\\n if (material.alphaTest > 0) {\\n uniforms.alphaTest.value = material.alphaTest;\\n }\\n let uvScaleMap;\\n if (material.map) {\\n uvScaleMap = material.map;\\n } else if (material.alphaMap) {\\n uvScaleMap = material.alphaMap;\\n }\\n if (uvScaleMap !== void 0) {\\n if (uvScaleMap.matrixAutoUpdate === true) {\\n uvScaleMap.updateMatrix();\\n }\\n uniforms.uvTransform.value.copy(uvScaleMap.matrix);\\n }\\n }\\n function refreshUniformsLambert(uniforms, material) {\\n if (material.emissiveMap) {\\n uniforms.emissiveMap.value = material.emissiveMap;\\n }\\n }\\n function refreshUniformsPhong(uniforms, material) {\\n uniforms.specular.value.copy(material.specular);\\n uniforms.shininess.value = Math.max(material.shininess, 1e-4);\\n if (material.emissiveMap) {\\n uniforms.emissiveMap.value = material.emissiveMap;\\n }\\n if (material.bumpMap) {\\n uniforms.bumpMap.value = material.bumpMap;\\n uniforms.bumpScale.value = material.bumpScale;\\n if (material.side === BackSide)\\n uniforms.bumpScale.value *= -1;\\n }\\n if (material.normalMap) {\\n uniforms.normalMap.value = material.normalMap;\\n uniforms.normalScale.value.copy(material.normalScale);\\n if (material.side === BackSide)\\n uniforms.normalScale.value.negate();\\n }\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n }\\n function refreshUniformsToon(uniforms, material) {\\n if (material.gradientMap) {\\n uniforms.gradientMap.value = material.gradientMap;\\n }\\n if (material.emissiveMap) {\\n uniforms.emissiveMap.value = material.emissiveMap;\\n }\\n if (material.bumpMap) {\\n uniforms.bumpMap.value = material.bumpMap;\\n uniforms.bumpScale.value = material.bumpScale;\\n if (material.side === BackSide)\\n uniforms.bumpScale.value *= -1;\\n }\\n if (material.normalMap) {\\n uniforms.normalMap.value = material.normalMap;\\n uniforms.normalScale.value.copy(material.normalScale);\\n if (material.side === BackSide)\\n uniforms.normalScale.value.negate();\\n }\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n }\\n function refreshUniformsStandard(uniforms, material) {\\n uniforms.roughness.value = material.roughness;\\n uniforms.metalness.value = material.metalness;\\n if (material.roughnessMap) {\\n uniforms.roughnessMap.value = material.roughnessMap;\\n }\\n if (material.metalnessMap) {\\n uniforms.metalnessMap.value = material.metalnessMap;\\n }\\n if (material.emissiveMap) {\\n uniforms.emissiveMap.value = material.emissiveMap;\\n }\\n if (material.bumpMap) {\\n uniforms.bumpMap.value = material.bumpMap;\\n uniforms.bumpScale.value = material.bumpScale;\\n if (material.side === BackSide)\\n uniforms.bumpScale.value *= -1;\\n }\\n if (material.normalMap) {\\n uniforms.normalMap.value = material.normalMap;\\n uniforms.normalScale.value.copy(material.normalScale);\\n if (material.side === BackSide)\\n uniforms.normalScale.value.negate();\\n }\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n const envMap = properties.get(material).envMap;\\n if (envMap) {\\n uniforms.envMapIntensity.value = material.envMapIntensity;\\n }\\n }\\n function refreshUniformsPhysical(uniforms, material, transmissionRenderTarget) {\\n refreshUniformsStandard(uniforms, material);\\n uniforms.ior.value = material.ior;\\n if (material.sheen > 0) {\\n uniforms.sheenTint.value.copy(material.sheenTint).multiplyScalar(material.sheen);\\n uniforms.sheenRoughness.value = material.sheenRoughness;\\n }\\n if (material.clearcoat > 0) {\\n uniforms.clearcoat.value = material.clearcoat;\\n uniforms.clearcoatRoughness.value = material.clearcoatRoughness;\\n if (material.clearcoatMap) {\\n uniforms.clearcoatMap.value = material.clearcoatMap;\\n }\\n if (material.clearcoatRoughnessMap) {\\n uniforms.clearcoatRoughnessMap.value = material.clearcoatRoughnessMap;\\n }\\n if (material.clearcoatNormalMap) {\\n uniforms.clearcoatNormalScale.value.copy(material.clearcoatNormalScale);\\n uniforms.clearcoatNormalMap.value = material.clearcoatNormalMap;\\n if (material.side === BackSide) {\\n uniforms.clearcoatNormalScale.value.negate();\\n }\\n }\\n }\\n if (material.transmission > 0) {\\n uniforms.transmission.value = material.transmission;\\n uniforms.transmissionSamplerMap.value = transmissionRenderTarget.texture;\\n uniforms.transmissionSamplerSize.value.set(transmissionRenderTarget.width, transmissionRenderTarget.height);\\n if (material.transmissionMap) {\\n uniforms.transmissionMap.value = material.transmissionMap;\\n }\\n uniforms.thickness.value = material.thickness;\\n if (material.thicknessMap) {\\n uniforms.thicknessMap.value = material.thicknessMap;\\n }\\n uniforms.attenuationDistance.value = material.attenuationDistance;\\n uniforms.attenuationTint.value.copy(material.attenuationTint);\\n }\\n uniforms.specularIntensity.value = material.specularIntensity;\\n uniforms.specularTint.value.copy(material.specularTint);\\n if (material.specularIntensityMap) {\\n uniforms.specularIntensityMap.value = material.specularIntensityMap;\\n }\\n if (material.specularTintMap) {\\n uniforms.specularTintMap.value = material.specularTintMap;\\n }\\n }\\n function refreshUniformsMatcap(uniforms, material) {\\n if (material.matcap) {\\n uniforms.matcap.value = material.matcap;\\n }\\n if (material.bumpMap) {\\n uniforms.bumpMap.value = material.bumpMap;\\n uniforms.bumpScale.value = material.bumpScale;\\n if (material.side === BackSide)\\n uniforms.bumpScale.value *= -1;\\n }\\n if (material.normalMap) {\\n uniforms.normalMap.value = material.normalMap;\\n uniforms.normalScale.value.copy(material.normalScale);\\n if (material.side === BackSide)\\n uniforms.normalScale.value.negate();\\n }\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n }\\n function refreshUniformsDepth(uniforms, material) {\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n }\\n function refreshUniformsDistance(uniforms, material) {\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n uniforms.referencePosition.value.copy(material.referencePosition);\\n uniforms.nearDistance.value = material.nearDistance;\\n uniforms.farDistance.value = material.farDistance;\\n }\\n function refreshUniformsNormal(uniforms, material) {\\n if (material.bumpMap) {\\n uniforms.bumpMap.value = material.bumpMap;\\n uniforms.bumpScale.value = material.bumpScale;\\n if (material.side === BackSide)\\n uniforms.bumpScale.value *= -1;\\n }\\n if (material.normalMap) {\\n uniforms.normalMap.value = material.normalMap;\\n uniforms.normalScale.value.copy(material.normalScale);\\n if (material.side === BackSide)\\n uniforms.normalScale.value.negate();\\n }\\n if (material.displacementMap) {\\n uniforms.displacementMap.value = material.displacementMap;\\n uniforms.displacementScale.value = material.displacementScale;\\n uniforms.displacementBias.value = material.displacementBias;\\n }\\n }\\n return {\\n refreshFogUniforms,\\n refreshMaterialUniforms\\n };\\n}\\n\\n// ../../../node_modules/three/src/renderers/WebGLRenderer.js\\nfunction createCanvasElement() {\\n const canvas = createElementNS(\\\\\\\"canvas\\\\\\\");\\n canvas.style.display = \\\\\\\"block\\\\\\\";\\n return canvas;\\n}\\nfunction WebGLRenderer(parameters = {}) {\\n const _canvas2 = parameters.canvas !== void 0 ? parameters.canvas : createCanvasElement(), _context2 = parameters.context !== void 0 ? parameters.context : null, _alpha = parameters.alpha !== void 0 ? parameters.alpha : false, _depth = parameters.depth !== void 0 ? parameters.depth : true, _stencil = parameters.stencil !== void 0 ? parameters.stencil : true, _antialias = parameters.antialias !== void 0 ? parameters.antialias : false, _premultipliedAlpha = parameters.premultipliedAlpha !== void 0 ? parameters.premultipliedAlpha : true, _preserveDrawingBuffer = parameters.preserveDrawingBuffer !== void 0 ? parameters.preserveDrawingBuffer : false, _powerPreference = parameters.powerPreference !== void 0 ? parameters.powerPreference : \\\\\\\"default\\\\\\\", _failIfMajorPerformanceCaveat = parameters.failIfMajorPerformanceCaveat !== void 0 ? parameters.failIfMajorPerformanceCaveat : false;\\n let currentRenderList = null;\\n let currentRenderState = null;\\n const renderListStack = [];\\n const renderStateStack = [];\\n this.domElement = _canvas2;\\n this.debug = {\\n checkShaderErrors: true\\n };\\n this.autoClear = true;\\n this.autoClearColor = true;\\n this.autoClearDepth = true;\\n this.autoClearStencil = true;\\n this.sortObjects = true;\\n this.clippingPlanes = [];\\n this.localClippingEnabled = false;\\n this.gammaFactor = 2;\\n this.outputEncoding = LinearEncoding;\\n this.physicallyCorrectLights = false;\\n this.toneMapping = NoToneMapping;\\n this.toneMappingExposure = 1;\\n const _this = this;\\n let _isContextLost = false;\\n let _currentActiveCubeFace = 0;\\n let _currentActiveMipmapLevel = 0;\\n let _currentRenderTarget = null;\\n let _currentMaterialId = -1;\\n let _currentCamera = null;\\n const _currentViewport = new Vector4();\\n const _currentScissor = new Vector4();\\n let _currentScissorTest = null;\\n let _width = _canvas2.width;\\n let _height = _canvas2.height;\\n let _pixelRatio = 1;\\n let _opaqueSort = null;\\n let _transparentSort = null;\\n const _viewport = new Vector4(0, 0, _width, _height);\\n const _scissor = new Vector4(0, 0, _width, _height);\\n let _scissorTest = false;\\n const _currentDrawBuffers = [];\\n const _frustum = new Frustum();\\n let _clippingEnabled = false;\\n let _localClippingEnabled = false;\\n let _transmissionRenderTarget = null;\\n const _projScreenMatrix = new Matrix4();\\n const _vector32 = new Vector3();\\n const _emptyScene = {background: null, fog: null, environment: null, overrideMaterial: null, isScene: true};\\n function getTargetPixelRatio() {\\n return _currentRenderTarget === null ? _pixelRatio : 1;\\n }\\n let _gl = _context2;\\n function getContext(contextNames, contextAttributes) {\\n for (let i = 0; i < contextNames.length; i++) {\\n const contextName = contextNames[i];\\n const context = _canvas2.getContext(contextName, contextAttributes);\\n if (context !== null)\\n return context;\\n }\\n return null;\\n }\\n try {\\n const contextAttributes = {\\n alpha: _alpha,\\n depth: _depth,\\n stencil: _stencil,\\n antialias: _antialias,\\n premultipliedAlpha: _premultipliedAlpha,\\n preserveDrawingBuffer: _preserveDrawingBuffer,\\n powerPreference: _powerPreference,\\n failIfMajorPerformanceCaveat: _failIfMajorPerformanceCaveat\\n };\\n _canvas2.addEventListener(\\\\\\\"webglcontextlost\\\\\\\", onContextLost, false);\\n _canvas2.addEventListener(\\\\\\\"webglcontextrestored\\\\\\\", onContextRestore, false);\\n if (_gl === null) {\\n const contextNames = [\\\\\\\"webgl2\\\\\\\", \\\\\\\"webgl\\\\\\\", \\\\\\\"experimental-webgl\\\\\\\"];\\n if (_this.isWebGL1Renderer === true) {\\n contextNames.shift();\\n }\\n _gl = getContext(contextNames, contextAttributes);\\n if (_gl === null) {\\n if (getContext(contextNames)) {\\n throw new Error(\\\\\\\"Error creating WebGL context with your selected attributes.\\\\\\\");\\n } else {\\n throw new Error(\\\\\\\"Error creating WebGL context.\\\\\\\");\\n }\\n }\\n }\\n if (_gl.getShaderPrecisionFormat === void 0) {\\n _gl.getShaderPrecisionFormat = function() {\\n return {rangeMin: 1, rangeMax: 1, precision: 1};\\n };\\n }\\n } catch (error) {\\n console.error(\\\\\\\"THREE.WebGLRenderer: \\\\\\\" + error.message);\\n throw error;\\n }\\n let extensions, capabilities, state, info;\\n let properties, textures, cubemaps, cubeuvmaps, attributes, geometries, objects;\\n let programCache, materials2, renderLists, renderStates, clipping, shadowMap;\\n let background2, morphtargets, bufferRenderer, indexedBufferRenderer;\\n let utils, bindingStates;\\n function initGLContext() {\\n extensions = new WebGLExtensions(_gl);\\n capabilities = new WebGLCapabilities(_gl, extensions, parameters);\\n extensions.init(capabilities);\\n utils = new WebGLUtils(_gl, extensions, capabilities);\\n state = new WebGLState(_gl, extensions, capabilities);\\n _currentDrawBuffers[0] = _gl.BACK;\\n info = new WebGLInfo(_gl);\\n properties = new WebGLProperties();\\n textures = new WebGLTextures(_gl, extensions, state, properties, capabilities, utils, info);\\n cubemaps = new WebGLCubeMaps(_this);\\n cubeuvmaps = new WebGLCubeUVMaps(_this);\\n attributes = new WebGLAttributes(_gl, capabilities);\\n bindingStates = new WebGLBindingStates(_gl, extensions, attributes, capabilities);\\n geometries = new WebGLGeometries(_gl, attributes, info, bindingStates);\\n objects = new WebGLObjects(_gl, geometries, attributes, info);\\n morphtargets = new WebGLMorphtargets(_gl, capabilities, textures);\\n clipping = new WebGLClipping(properties);\\n programCache = new WebGLPrograms(_this, cubemaps, cubeuvmaps, extensions, capabilities, bindingStates, clipping);\\n materials2 = new WebGLMaterials(properties);\\n renderLists = new WebGLRenderLists(properties);\\n renderStates = new WebGLRenderStates(extensions, capabilities);\\n background2 = new WebGLBackground(_this, cubemaps, state, objects, _premultipliedAlpha);\\n shadowMap = new WebGLShadowMap(_this, objects, capabilities);\\n bufferRenderer = new WebGLBufferRenderer(_gl, extensions, info, capabilities);\\n indexedBufferRenderer = new WebGLIndexedBufferRenderer(_gl, extensions, info, capabilities);\\n info.programs = programCache.programs;\\n _this.capabilities = capabilities;\\n _this.extensions = extensions;\\n _this.properties = properties;\\n _this.renderLists = renderLists;\\n _this.shadowMap = shadowMap;\\n _this.state = state;\\n _this.info = info;\\n }\\n initGLContext();\\n const xr = new WebXRManager(_this, _gl);\\n this.xr = xr;\\n this.getContext = function() {\\n return _gl;\\n };\\n this.getContextAttributes = function() {\\n return _gl.getContextAttributes();\\n };\\n this.forceContextLoss = function() {\\n const extension = extensions.get(\\\\\\\"WEBGL_lose_context\\\\\\\");\\n if (extension)\\n extension.loseContext();\\n };\\n this.forceContextRestore = function() {\\n const extension = extensions.get(\\\\\\\"WEBGL_lose_context\\\\\\\");\\n if (extension)\\n extension.restoreContext();\\n };\\n this.getPixelRatio = function() {\\n return _pixelRatio;\\n };\\n this.setPixelRatio = function(value) {\\n if (value === void 0)\\n return;\\n _pixelRatio = value;\\n this.setSize(_width, _height, false);\\n };\\n this.getSize = function(target) {\\n return target.set(_width, _height);\\n };\\n this.setSize = function(width, height, updateStyle) {\\n if (xr.isPresenting) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer: Can't change size while VR device is presenting.\\\\\\\");\\n return;\\n }\\n _width = width;\\n _height = height;\\n _canvas2.width = Math.floor(width * _pixelRatio);\\n _canvas2.height = Math.floor(height * _pixelRatio);\\n if (updateStyle !== false) {\\n _canvas2.style.width = width + \\\\\\\"px\\\\\\\";\\n _canvas2.style.height = height + \\\\\\\"px\\\\\\\";\\n }\\n this.setViewport(0, 0, width, height);\\n };\\n this.getDrawingBufferSize = function(target) {\\n return target.set(_width * _pixelRatio, _height * _pixelRatio).floor();\\n };\\n this.setDrawingBufferSize = function(width, height, pixelRatio) {\\n _width = width;\\n _height = height;\\n _pixelRatio = pixelRatio;\\n _canvas2.width = Math.floor(width * pixelRatio);\\n _canvas2.height = Math.floor(height * pixelRatio);\\n this.setViewport(0, 0, width, height);\\n };\\n this.getCurrentViewport = function(target) {\\n return target.copy(_currentViewport);\\n };\\n this.getViewport = function(target) {\\n return target.copy(_viewport);\\n };\\n this.setViewport = function(x, y, width, height) {\\n if (x.isVector4) {\\n _viewport.set(x.x, x.y, x.z, x.w);\\n } else {\\n _viewport.set(x, y, width, height);\\n }\\n state.viewport(_currentViewport.copy(_viewport).multiplyScalar(_pixelRatio).floor());\\n };\\n this.getScissor = function(target) {\\n return target.copy(_scissor);\\n };\\n this.setScissor = function(x, y, width, height) {\\n if (x.isVector4) {\\n _scissor.set(x.x, x.y, x.z, x.w);\\n } else {\\n _scissor.set(x, y, width, height);\\n }\\n state.scissor(_currentScissor.copy(_scissor).multiplyScalar(_pixelRatio).floor());\\n };\\n this.getScissorTest = function() {\\n return _scissorTest;\\n };\\n this.setScissorTest = function(boolean) {\\n state.setScissorTest(_scissorTest = boolean);\\n };\\n this.setOpaqueSort = function(method) {\\n _opaqueSort = method;\\n };\\n this.setTransparentSort = function(method) {\\n _transparentSort = method;\\n };\\n this.getClearColor = function(target) {\\n return target.copy(background2.getClearColor());\\n };\\n this.setClearColor = function() {\\n background2.setClearColor.apply(background2, arguments);\\n };\\n this.getClearAlpha = function() {\\n return background2.getClearAlpha();\\n };\\n this.setClearAlpha = function() {\\n background2.setClearAlpha.apply(background2, arguments);\\n };\\n this.clear = function(color, depth2, stencil) {\\n let bits = 0;\\n if (color === void 0 || color)\\n bits |= _gl.COLOR_BUFFER_BIT;\\n if (depth2 === void 0 || depth2)\\n bits |= _gl.DEPTH_BUFFER_BIT;\\n if (stencil === void 0 || stencil)\\n bits |= _gl.STENCIL_BUFFER_BIT;\\n _gl.clear(bits);\\n };\\n this.clearColor = function() {\\n this.clear(true, false, false);\\n };\\n this.clearDepth = function() {\\n this.clear(false, true, false);\\n };\\n this.clearStencil = function() {\\n this.clear(false, false, true);\\n };\\n this.dispose = function() {\\n _canvas2.removeEventListener(\\\\\\\"webglcontextlost\\\\\\\", onContextLost, false);\\n _canvas2.removeEventListener(\\\\\\\"webglcontextrestored\\\\\\\", onContextRestore, false);\\n renderLists.dispose();\\n renderStates.dispose();\\n properties.dispose();\\n cubemaps.dispose();\\n cubeuvmaps.dispose();\\n objects.dispose();\\n bindingStates.dispose();\\n xr.dispose();\\n xr.removeEventListener(\\\\\\\"sessionstart\\\\\\\", onXRSessionStart);\\n xr.removeEventListener(\\\\\\\"sessionend\\\\\\\", onXRSessionEnd);\\n if (_transmissionRenderTarget) {\\n _transmissionRenderTarget.dispose();\\n _transmissionRenderTarget = null;\\n }\\n animation.stop();\\n };\\n function onContextLost(event) {\\n event.preventDefault();\\n console.log(\\\\\\\"THREE.WebGLRenderer: Context Lost.\\\\\\\");\\n _isContextLost = true;\\n }\\n function onContextRestore() {\\n console.log(\\\\\\\"THREE.WebGLRenderer: Context Restored.\\\\\\\");\\n _isContextLost = false;\\n const infoAutoReset = info.autoReset;\\n const shadowMapEnabled = shadowMap.enabled;\\n const shadowMapAutoUpdate = shadowMap.autoUpdate;\\n const shadowMapNeedsUpdate = shadowMap.needsUpdate;\\n const shadowMapType = shadowMap.type;\\n initGLContext();\\n info.autoReset = infoAutoReset;\\n shadowMap.enabled = shadowMapEnabled;\\n shadowMap.autoUpdate = shadowMapAutoUpdate;\\n shadowMap.needsUpdate = shadowMapNeedsUpdate;\\n shadowMap.type = shadowMapType;\\n }\\n function onMaterialDispose(event) {\\n const material = event.target;\\n material.removeEventListener(\\\\\\\"dispose\\\\\\\", onMaterialDispose);\\n deallocateMaterial(material);\\n }\\n function deallocateMaterial(material) {\\n releaseMaterialProgramReferences(material);\\n properties.remove(material);\\n }\\n function releaseMaterialProgramReferences(material) {\\n const programs = properties.get(material).programs;\\n if (programs !== void 0) {\\n programs.forEach(function(program) {\\n programCache.releaseProgram(program);\\n });\\n }\\n }\\n function renderObjectImmediate(object, program) {\\n object.render(function(object2) {\\n _this.renderBufferImmediate(object2, program);\\n });\\n }\\n this.renderBufferImmediate = function(object, program) {\\n bindingStates.initAttributes();\\n const buffers = properties.get(object);\\n if (object.hasPositions && !buffers.position)\\n buffers.position = _gl.createBuffer();\\n if (object.hasNormals && !buffers.normal)\\n buffers.normal = _gl.createBuffer();\\n if (object.hasUvs && !buffers.uv)\\n buffers.uv = _gl.createBuffer();\\n if (object.hasColors && !buffers.color)\\n buffers.color = _gl.createBuffer();\\n const programAttributes = program.getAttributes();\\n if (object.hasPositions) {\\n _gl.bindBuffer(_gl.ARRAY_BUFFER, buffers.position);\\n _gl.bufferData(_gl.ARRAY_BUFFER, object.positionArray, _gl.DYNAMIC_DRAW);\\n bindingStates.enableAttribute(programAttributes.position.location);\\n _gl.vertexAttribPointer(programAttributes.position.location, 3, _gl.FLOAT, false, 0, 0);\\n }\\n if (object.hasNormals) {\\n _gl.bindBuffer(_gl.ARRAY_BUFFER, buffers.normal);\\n _gl.bufferData(_gl.ARRAY_BUFFER, object.normalArray, _gl.DYNAMIC_DRAW);\\n bindingStates.enableAttribute(programAttributes.normal.location);\\n _gl.vertexAttribPointer(programAttributes.normal.location, 3, _gl.FLOAT, false, 0, 0);\\n }\\n if (object.hasUvs) {\\n _gl.bindBuffer(_gl.ARRAY_BUFFER, buffers.uv);\\n _gl.bufferData(_gl.ARRAY_BUFFER, object.uvArray, _gl.DYNAMIC_DRAW);\\n bindingStates.enableAttribute(programAttributes.uv.location);\\n _gl.vertexAttribPointer(programAttributes.uv.location, 2, _gl.FLOAT, false, 0, 0);\\n }\\n if (object.hasColors) {\\n _gl.bindBuffer(_gl.ARRAY_BUFFER, buffers.color);\\n _gl.bufferData(_gl.ARRAY_BUFFER, object.colorArray, _gl.DYNAMIC_DRAW);\\n bindingStates.enableAttribute(programAttributes.color.location);\\n _gl.vertexAttribPointer(programAttributes.color.location, 3, _gl.FLOAT, false, 0, 0);\\n }\\n bindingStates.disableUnusedAttributes();\\n _gl.drawArrays(_gl.TRIANGLES, 0, object.count);\\n object.count = 0;\\n };\\n this.renderBufferDirect = function(camera, scene, geometry, material, object, group) {\\n if (scene === null)\\n scene = _emptyScene;\\n const frontFaceCW = object.isMesh && object.matrixWorld.determinant() < 0;\\n const program = setProgram(camera, scene, geometry, material, object);\\n state.setMaterial(material, frontFaceCW);\\n let index = geometry.index;\\n const position = geometry.attributes.position;\\n if (index === null) {\\n if (position === void 0 || position.count === 0)\\n return;\\n } else if (index.count === 0) {\\n return;\\n }\\n let rangeFactor = 1;\\n if (material.wireframe === true) {\\n index = geometries.getWireframeAttribute(geometry);\\n rangeFactor = 2;\\n }\\n bindingStates.setup(object, material, program, geometry, index);\\n let attribute;\\n let renderer = bufferRenderer;\\n if (index !== null) {\\n attribute = attributes.get(index);\\n renderer = indexedBufferRenderer;\\n renderer.setIndex(attribute);\\n }\\n const dataCount = index !== null ? index.count : position.count;\\n const rangeStart = geometry.drawRange.start * rangeFactor;\\n const rangeCount = geometry.drawRange.count * rangeFactor;\\n const groupStart = group !== null ? group.start * rangeFactor : 0;\\n const groupCount = group !== null ? group.count * rangeFactor : Infinity;\\n const drawStart = Math.max(rangeStart, groupStart);\\n const drawEnd = Math.min(dataCount, rangeStart + rangeCount, groupStart + groupCount) - 1;\\n const drawCount = Math.max(0, drawEnd - drawStart + 1);\\n if (drawCount === 0)\\n return;\\n if (object.isMesh) {\\n if (material.wireframe === true) {\\n state.setLineWidth(material.wireframeLinewidth * getTargetPixelRatio());\\n renderer.setMode(_gl.LINES);\\n } else {\\n renderer.setMode(_gl.TRIANGLES);\\n }\\n } else if (object.isLine) {\\n let lineWidth = material.linewidth;\\n if (lineWidth === void 0)\\n lineWidth = 1;\\n state.setLineWidth(lineWidth * getTargetPixelRatio());\\n if (object.isLineSegments) {\\n renderer.setMode(_gl.LINES);\\n } else if (object.isLineLoop) {\\n renderer.setMode(_gl.LINE_LOOP);\\n } else {\\n renderer.setMode(_gl.LINE_STRIP);\\n }\\n } else if (object.isPoints) {\\n renderer.setMode(_gl.POINTS);\\n } else if (object.isSprite) {\\n renderer.setMode(_gl.TRIANGLES);\\n }\\n if (object.isInstancedMesh) {\\n renderer.renderInstances(drawStart, drawCount, object.count);\\n } else if (geometry.isInstancedBufferGeometry) {\\n const instanceCount = Math.min(geometry.instanceCount, geometry._maxInstanceCount);\\n renderer.renderInstances(drawStart, drawCount, instanceCount);\\n } else {\\n renderer.render(drawStart, drawCount);\\n }\\n };\\n this.compile = function(scene, camera) {\\n currentRenderState = renderStates.get(scene);\\n currentRenderState.init();\\n renderStateStack.push(currentRenderState);\\n scene.traverseVisible(function(object) {\\n if (object.isLight && object.layers.test(camera.layers)) {\\n currentRenderState.pushLight(object);\\n if (object.castShadow) {\\n currentRenderState.pushShadow(object);\\n }\\n }\\n });\\n currentRenderState.setupLights(_this.physicallyCorrectLights);\\n scene.traverse(function(object) {\\n const material = object.material;\\n if (material) {\\n if (Array.isArray(material)) {\\n for (let i = 0; i < material.length; i++) {\\n const material2 = material[i];\\n getProgram(material2, scene, object);\\n }\\n } else {\\n getProgram(material, scene, object);\\n }\\n }\\n });\\n renderStateStack.pop();\\n currentRenderState = null;\\n };\\n let onAnimationFrameCallback = null;\\n function onAnimationFrame(time) {\\n if (onAnimationFrameCallback)\\n onAnimationFrameCallback(time);\\n }\\n function onXRSessionStart() {\\n animation.stop();\\n }\\n function onXRSessionEnd() {\\n animation.start();\\n }\\n const animation = new WebGLAnimation();\\n animation.setAnimationLoop(onAnimationFrame);\\n if (typeof window !== \\\\\\\"undefined\\\\\\\")\\n animation.setContext(window);\\n this.setAnimationLoop = function(callback) {\\n onAnimationFrameCallback = callback;\\n xr.setAnimationLoop(callback);\\n callback === null ? animation.stop() : animation.start();\\n };\\n xr.addEventListener(\\\\\\\"sessionstart\\\\\\\", onXRSessionStart);\\n xr.addEventListener(\\\\\\\"sessionend\\\\\\\", onXRSessionEnd);\\n this.render = function(scene, camera) {\\n if (camera !== void 0 && camera.isCamera !== true) {\\n console.error(\\\\\\\"THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.\\\\\\\");\\n return;\\n }\\n if (_isContextLost === true)\\n return;\\n if (scene.autoUpdate === true)\\n scene.updateMatrixWorld();\\n if (camera.parent === null)\\n camera.updateMatrixWorld();\\n if (xr.enabled === true && xr.isPresenting === true) {\\n if (xr.cameraAutoUpdate === true)\\n xr.updateCamera(camera);\\n camera = xr.getCamera();\\n }\\n if (scene.isScene === true)\\n scene.onBeforeRender(_this, scene, camera, _currentRenderTarget);\\n currentRenderState = renderStates.get(scene, renderStateStack.length);\\n currentRenderState.init();\\n renderStateStack.push(currentRenderState);\\n _projScreenMatrix.multiplyMatrices(camera.projectionMatrix, camera.matrixWorldInverse);\\n _frustum.setFromProjectionMatrix(_projScreenMatrix);\\n _localClippingEnabled = this.localClippingEnabled;\\n _clippingEnabled = clipping.init(this.clippingPlanes, _localClippingEnabled, camera);\\n currentRenderList = renderLists.get(scene, renderListStack.length);\\n currentRenderList.init();\\n renderListStack.push(currentRenderList);\\n projectObject(scene, camera, 0, _this.sortObjects);\\n currentRenderList.finish();\\n if (_this.sortObjects === true) {\\n currentRenderList.sort(_opaqueSort, _transparentSort);\\n }\\n if (_clippingEnabled === true)\\n clipping.beginShadows();\\n const shadowsArray = currentRenderState.state.shadowsArray;\\n shadowMap.render(shadowsArray, scene, camera);\\n if (_clippingEnabled === true)\\n clipping.endShadows();\\n if (this.info.autoReset === true)\\n this.info.reset();\\n background2.render(currentRenderList, scene);\\n currentRenderState.setupLights(_this.physicallyCorrectLights);\\n if (camera.isArrayCamera) {\\n const cameras = camera.cameras;\\n for (let i = 0, l = cameras.length; i < l; i++) {\\n const camera2 = cameras[i];\\n renderScene(currentRenderList, scene, camera2, camera2.viewport);\\n }\\n } else {\\n renderScene(currentRenderList, scene, camera);\\n }\\n if (_currentRenderTarget !== null) {\\n textures.updateMultisampleRenderTarget(_currentRenderTarget);\\n textures.updateRenderTargetMipmap(_currentRenderTarget);\\n }\\n if (scene.isScene === true)\\n scene.onAfterRender(_this, scene, camera);\\n state.buffers.depth.setTest(true);\\n state.buffers.depth.setMask(true);\\n state.buffers.color.setMask(true);\\n state.setPolygonOffset(false);\\n bindingStates.resetDefaultState();\\n _currentMaterialId = -1;\\n _currentCamera = null;\\n renderStateStack.pop();\\n if (renderStateStack.length > 0) {\\n currentRenderState = renderStateStack[renderStateStack.length - 1];\\n } else {\\n currentRenderState = null;\\n }\\n renderListStack.pop();\\n if (renderListStack.length > 0) {\\n currentRenderList = renderListStack[renderListStack.length - 1];\\n } else {\\n currentRenderList = null;\\n }\\n };\\n function projectObject(object, camera, groupOrder, sortObjects) {\\n if (object.visible === false)\\n return;\\n const visible = object.layers.test(camera.layers);\\n if (visible) {\\n if (object.isGroup) {\\n groupOrder = object.renderOrder;\\n } else if (object.isLOD) {\\n if (object.autoUpdate === true)\\n object.update(camera);\\n } else if (object.isLight) {\\n currentRenderState.pushLight(object);\\n if (object.castShadow) {\\n currentRenderState.pushShadow(object);\\n }\\n } else if (object.isSprite) {\\n if (!object.frustumCulled || _frustum.intersectsSprite(object)) {\\n if (sortObjects) {\\n _vector32.setFromMatrixPosition(object.matrixWorld).applyMatrix4(_projScreenMatrix);\\n }\\n const geometry = objects.update(object);\\n const material = object.material;\\n if (material.visible) {\\n currentRenderList.push(object, geometry, material, groupOrder, _vector32.z, null);\\n }\\n }\\n } else if (object.isImmediateRenderObject) {\\n if (sortObjects) {\\n _vector32.setFromMatrixPosition(object.matrixWorld).applyMatrix4(_projScreenMatrix);\\n }\\n currentRenderList.push(object, null, object.material, groupOrder, _vector32.z, null);\\n } else if (object.isMesh || object.isLine || object.isPoints) {\\n if (object.isSkinnedMesh) {\\n if (object.skeleton.frame !== info.render.frame) {\\n object.skeleton.update();\\n object.skeleton.frame = info.render.frame;\\n }\\n }\\n if (!object.frustumCulled || _frustum.intersectsObject(object)) {\\n if (sortObjects) {\\n _vector32.setFromMatrixPosition(object.matrixWorld).applyMatrix4(_projScreenMatrix);\\n }\\n const geometry = objects.update(object);\\n const material = object.material;\\n if (Array.isArray(material)) {\\n const groups = geometry.groups;\\n for (let i = 0, l = groups.length; i < l; i++) {\\n const group = groups[i];\\n const groupMaterial = material[group.materialIndex];\\n if (groupMaterial && groupMaterial.visible) {\\n currentRenderList.push(object, geometry, groupMaterial, groupOrder, _vector32.z, group);\\n }\\n }\\n } else if (material.visible) {\\n currentRenderList.push(object, geometry, material, groupOrder, _vector32.z, null);\\n }\\n }\\n }\\n }\\n const children = object.children;\\n for (let i = 0, l = children.length; i < l; i++) {\\n projectObject(children[i], camera, groupOrder, sortObjects);\\n }\\n }\\n function renderScene(currentRenderList2, scene, camera, viewport) {\\n const opaqueObjects = currentRenderList2.opaque;\\n const transmissiveObjects = currentRenderList2.transmissive;\\n const transparentObjects = currentRenderList2.transparent;\\n currentRenderState.setupLightsView(camera);\\n if (transmissiveObjects.length > 0)\\n renderTransmissionPass(opaqueObjects, scene, camera);\\n if (viewport)\\n state.viewport(_currentViewport.copy(viewport));\\n if (opaqueObjects.length > 0)\\n renderObjects(opaqueObjects, scene, camera);\\n if (transmissiveObjects.length > 0)\\n renderObjects(transmissiveObjects, scene, camera);\\n if (transparentObjects.length > 0)\\n renderObjects(transparentObjects, scene, camera);\\n }\\n function renderTransmissionPass(opaqueObjects, scene, camera) {\\n if (_transmissionRenderTarget === null) {\\n const needsAntialias = _antialias === true && capabilities.isWebGL2 === true;\\n const renderTargetType = needsAntialias ? WebGLMultisampleRenderTarget : WebGLRenderTarget;\\n _transmissionRenderTarget = new renderTargetType(1024, 1024, {\\n generateMipmaps: true,\\n type: utils.convert(HalfFloatType) !== null ? HalfFloatType : UnsignedByteType,\\n minFilter: LinearMipmapLinearFilter,\\n magFilter: NearestFilter,\\n wrapS: ClampToEdgeWrapping,\\n wrapT: ClampToEdgeWrapping\\n });\\n }\\n const currentRenderTarget = _this.getRenderTarget();\\n _this.setRenderTarget(_transmissionRenderTarget);\\n _this.clear();\\n const currentToneMapping = _this.toneMapping;\\n _this.toneMapping = NoToneMapping;\\n renderObjects(opaqueObjects, scene, camera);\\n _this.toneMapping = currentToneMapping;\\n textures.updateMultisampleRenderTarget(_transmissionRenderTarget);\\n textures.updateRenderTargetMipmap(_transmissionRenderTarget);\\n _this.setRenderTarget(currentRenderTarget);\\n }\\n function renderObjects(renderList, scene, camera) {\\n const overrideMaterial = scene.isScene === true ? scene.overrideMaterial : null;\\n for (let i = 0, l = renderList.length; i < l; i++) {\\n const renderItem = renderList[i];\\n const object = renderItem.object;\\n const geometry = renderItem.geometry;\\n const material = overrideMaterial === null ? renderItem.material : overrideMaterial;\\n const group = renderItem.group;\\n if (object.layers.test(camera.layers)) {\\n renderObject(object, scene, camera, geometry, material, group);\\n }\\n }\\n }\\n function renderObject(object, scene, camera, geometry, material, group) {\\n object.onBeforeRender(_this, scene, camera, geometry, material, group);\\n object.modelViewMatrix.multiplyMatrices(camera.matrixWorldInverse, object.matrixWorld);\\n object.normalMatrix.getNormalMatrix(object.modelViewMatrix);\\n material.onBeforeRender(_this, scene, camera, geometry, object, group);\\n if (object.isImmediateRenderObject) {\\n const program = setProgram(camera, scene, geometry, material, object);\\n state.setMaterial(material);\\n bindingStates.reset();\\n renderObjectImmediate(object, program);\\n } else {\\n if (material.transparent === true && material.side === DoubleSide) {\\n material.side = BackSide;\\n material.needsUpdate = true;\\n _this.renderBufferDirect(camera, scene, geometry, material, object, group);\\n material.side = FrontSide;\\n material.needsUpdate = true;\\n _this.renderBufferDirect(camera, scene, geometry, material, object, group);\\n material.side = DoubleSide;\\n } else {\\n _this.renderBufferDirect(camera, scene, geometry, material, object, group);\\n }\\n }\\n object.onAfterRender(_this, scene, camera, geometry, material, group);\\n }\\n function getProgram(material, scene, object) {\\n if (scene.isScene !== true)\\n scene = _emptyScene;\\n const materialProperties = properties.get(material);\\n const lights = currentRenderState.state.lights;\\n const shadowsArray = currentRenderState.state.shadowsArray;\\n const lightsStateVersion = lights.state.version;\\n const parameters2 = programCache.getParameters(material, lights.state, shadowsArray, scene, object);\\n const programCacheKey = programCache.getProgramCacheKey(parameters2);\\n let programs = materialProperties.programs;\\n materialProperties.environment = material.isMeshStandardMaterial ? scene.environment : null;\\n materialProperties.fog = scene.fog;\\n materialProperties.envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || materialProperties.environment);\\n if (programs === void 0) {\\n material.addEventListener(\\\\\\\"dispose\\\\\\\", onMaterialDispose);\\n programs = new Map();\\n materialProperties.programs = programs;\\n }\\n let program = programs.get(programCacheKey);\\n if (program !== void 0) {\\n if (materialProperties.currentProgram === program && materialProperties.lightsStateVersion === lightsStateVersion) {\\n updateCommonMaterialProperties(material, parameters2);\\n return program;\\n }\\n } else {\\n parameters2.uniforms = programCache.getUniforms(material);\\n material.onBuild(parameters2, _this);\\n material.onBeforeCompile(parameters2, _this);\\n program = programCache.acquireProgram(parameters2, programCacheKey);\\n programs.set(programCacheKey, program);\\n materialProperties.uniforms = parameters2.uniforms;\\n }\\n const uniforms = materialProperties.uniforms;\\n if (!material.isShaderMaterial && !material.isRawShaderMaterial || material.clipping === true) {\\n uniforms.clippingPlanes = clipping.uniform;\\n }\\n updateCommonMaterialProperties(material, parameters2);\\n materialProperties.needsLights = materialNeedsLights(material);\\n materialProperties.lightsStateVersion = lightsStateVersion;\\n if (materialProperties.needsLights) {\\n uniforms.ambientLightColor.value = lights.state.ambient;\\n uniforms.lightProbe.value = lights.state.probe;\\n uniforms.directionalLights.value = lights.state.directional;\\n uniforms.directionalLightShadows.value = lights.state.directionalShadow;\\n uniforms.spotLights.value = lights.state.spot;\\n uniforms.spotLightShadows.value = lights.state.spotShadow;\\n uniforms.rectAreaLights.value = lights.state.rectArea;\\n uniforms.ltc_1.value = lights.state.rectAreaLTC1;\\n uniforms.ltc_2.value = lights.state.rectAreaLTC2;\\n uniforms.pointLights.value = lights.state.point;\\n uniforms.pointLightShadows.value = lights.state.pointShadow;\\n uniforms.hemisphereLights.value = lights.state.hemi;\\n uniforms.directionalShadowMap.value = lights.state.directionalShadowMap;\\n uniforms.directionalShadowMatrix.value = lights.state.directionalShadowMatrix;\\n uniforms.spotShadowMap.value = lights.state.spotShadowMap;\\n uniforms.spotShadowMatrix.value = lights.state.spotShadowMatrix;\\n uniforms.pointShadowMap.value = lights.state.pointShadowMap;\\n uniforms.pointShadowMatrix.value = lights.state.pointShadowMatrix;\\n }\\n const progUniforms = program.getUniforms();\\n const uniformsList = WebGLUniforms.seqWithValue(progUniforms.seq, uniforms);\\n materialProperties.currentProgram = program;\\n materialProperties.uniformsList = uniformsList;\\n return program;\\n }\\n function updateCommonMaterialProperties(material, parameters2) {\\n const materialProperties = properties.get(material);\\n materialProperties.outputEncoding = parameters2.outputEncoding;\\n materialProperties.instancing = parameters2.instancing;\\n materialProperties.skinning = parameters2.skinning;\\n materialProperties.morphTargets = parameters2.morphTargets;\\n materialProperties.morphNormals = parameters2.morphNormals;\\n materialProperties.morphTargetsCount = parameters2.morphTargetsCount;\\n materialProperties.numClippingPlanes = parameters2.numClippingPlanes;\\n materialProperties.numIntersection = parameters2.numClipIntersection;\\n materialProperties.vertexAlphas = parameters2.vertexAlphas;\\n materialProperties.vertexTangents = parameters2.vertexTangents;\\n }\\n function setProgram(camera, scene, geometry, material, object) {\\n if (scene.isScene !== true)\\n scene = _emptyScene;\\n textures.resetTextureUnits();\\n const fog = scene.fog;\\n const environment = material.isMeshStandardMaterial ? scene.environment : null;\\n const encoding = _currentRenderTarget === null ? _this.outputEncoding : _currentRenderTarget.texture.encoding;\\n const envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || environment);\\n const vertexAlphas = material.vertexColors === true && !!geometry && !!geometry.attributes.color && geometry.attributes.color.itemSize === 4;\\n const vertexTangents = !!material.normalMap && !!geometry && !!geometry.attributes.tangent;\\n const morphTargets = !!geometry && !!geometry.morphAttributes.position;\\n const morphNormals = !!geometry && !!geometry.morphAttributes.normal;\\n const morphTargetsCount = !!geometry && !!geometry.morphAttributes.position ? geometry.morphAttributes.position.length : 0;\\n const materialProperties = properties.get(material);\\n const lights = currentRenderState.state.lights;\\n if (_clippingEnabled === true) {\\n if (_localClippingEnabled === true || camera !== _currentCamera) {\\n const useCache = camera === _currentCamera && material.id === _currentMaterialId;\\n clipping.setState(material, camera, useCache);\\n }\\n }\\n let needsProgramChange = false;\\n if (material.version === materialProperties.__version) {\\n if (materialProperties.needsLights && materialProperties.lightsStateVersion !== lights.state.version) {\\n needsProgramChange = true;\\n } else if (materialProperties.outputEncoding !== encoding) {\\n needsProgramChange = true;\\n } else if (object.isInstancedMesh && materialProperties.instancing === false) {\\n needsProgramChange = true;\\n } else if (!object.isInstancedMesh && materialProperties.instancing === true) {\\n needsProgramChange = true;\\n } else if (object.isSkinnedMesh && materialProperties.skinning === false) {\\n needsProgramChange = true;\\n } else if (!object.isSkinnedMesh && materialProperties.skinning === true) {\\n needsProgramChange = true;\\n } else if (materialProperties.envMap !== envMap) {\\n needsProgramChange = true;\\n } else if (material.fog && materialProperties.fog !== fog) {\\n needsProgramChange = true;\\n } else if (materialProperties.numClippingPlanes !== void 0 && (materialProperties.numClippingPlanes !== clipping.numPlanes || materialProperties.numIntersection !== clipping.numIntersection)) {\\n needsProgramChange = true;\\n } else if (materialProperties.vertexAlphas !== vertexAlphas) {\\n needsProgramChange = true;\\n } else if (materialProperties.vertexTangents !== vertexTangents) {\\n needsProgramChange = true;\\n } else if (materialProperties.morphTargets !== morphTargets) {\\n needsProgramChange = true;\\n } else if (materialProperties.morphNormals !== morphNormals) {\\n needsProgramChange = true;\\n } else if (capabilities.isWebGL2 === true && materialProperties.morphTargetsCount !== morphTargetsCount) {\\n needsProgramChange = true;\\n }\\n } else {\\n needsProgramChange = true;\\n materialProperties.__version = material.version;\\n }\\n let program = materialProperties.currentProgram;\\n if (needsProgramChange === true) {\\n program = getProgram(material, scene, object);\\n }\\n let refreshProgram = false;\\n let refreshMaterial = false;\\n let refreshLights = false;\\n const p_uniforms = program.getUniforms(), m_uniforms = materialProperties.uniforms;\\n if (state.useProgram(program.program)) {\\n refreshProgram = true;\\n refreshMaterial = true;\\n refreshLights = true;\\n }\\n if (material.id !== _currentMaterialId) {\\n _currentMaterialId = material.id;\\n refreshMaterial = true;\\n }\\n if (refreshProgram || _currentCamera !== camera) {\\n p_uniforms.setValue(_gl, \\\\\\\"projectionMatrix\\\\\\\", camera.projectionMatrix);\\n if (capabilities.logarithmicDepthBuffer) {\\n p_uniforms.setValue(_gl, \\\\\\\"logDepthBufFC\\\\\\\", 2 / (Math.log(camera.far + 1) / Math.LN2));\\n }\\n if (_currentCamera !== camera) {\\n _currentCamera = camera;\\n refreshMaterial = true;\\n refreshLights = true;\\n }\\n if (material.isShaderMaterial || material.isMeshPhongMaterial || material.isMeshToonMaterial || material.isMeshStandardMaterial || material.envMap) {\\n const uCamPos = p_uniforms.map.cameraPosition;\\n if (uCamPos !== void 0) {\\n uCamPos.setValue(_gl, _vector32.setFromMatrixPosition(camera.matrixWorld));\\n }\\n }\\n if (material.isMeshPhongMaterial || material.isMeshToonMaterial || material.isMeshLambertMaterial || material.isMeshBasicMaterial || material.isMeshStandardMaterial || material.isShaderMaterial) {\\n p_uniforms.setValue(_gl, \\\\\\\"isOrthographic\\\\\\\", camera.isOrthographicCamera === true);\\n }\\n if (material.isMeshPhongMaterial || material.isMeshToonMaterial || material.isMeshLambertMaterial || material.isMeshBasicMaterial || material.isMeshStandardMaterial || material.isShaderMaterial || material.isShadowMaterial || object.isSkinnedMesh) {\\n p_uniforms.setValue(_gl, \\\\\\\"viewMatrix\\\\\\\", camera.matrixWorldInverse);\\n }\\n }\\n if (object.isSkinnedMesh) {\\n p_uniforms.setOptional(_gl, object, \\\\\\\"bindMatrix\\\\\\\");\\n p_uniforms.setOptional(_gl, object, \\\\\\\"bindMatrixInverse\\\\\\\");\\n const skeleton = object.skeleton;\\n if (skeleton) {\\n if (capabilities.floatVertexTextures) {\\n if (skeleton.boneTexture === null)\\n skeleton.computeBoneTexture();\\n p_uniforms.setValue(_gl, \\\\\\\"boneTexture\\\\\\\", skeleton.boneTexture, textures);\\n p_uniforms.setValue(_gl, \\\\\\\"boneTextureSize\\\\\\\", skeleton.boneTextureSize);\\n } else {\\n p_uniforms.setOptional(_gl, skeleton, \\\\\\\"boneMatrices\\\\\\\");\\n }\\n }\\n }\\n if (!!geometry && (geometry.morphAttributes.position !== void 0 || geometry.morphAttributes.normal !== void 0)) {\\n morphtargets.update(object, geometry, material, program);\\n }\\n if (refreshMaterial || materialProperties.receiveShadow !== object.receiveShadow) {\\n materialProperties.receiveShadow = object.receiveShadow;\\n p_uniforms.setValue(_gl, \\\\\\\"receiveShadow\\\\\\\", object.receiveShadow);\\n }\\n if (refreshMaterial) {\\n p_uniforms.setValue(_gl, \\\\\\\"toneMappingExposure\\\\\\\", _this.toneMappingExposure);\\n if (materialProperties.needsLights) {\\n markUniformsLightsNeedsUpdate(m_uniforms, refreshLights);\\n }\\n if (fog && material.fog) {\\n materials2.refreshFogUniforms(m_uniforms, fog);\\n }\\n materials2.refreshMaterialUniforms(m_uniforms, material, _pixelRatio, _height, _transmissionRenderTarget);\\n WebGLUniforms.upload(_gl, materialProperties.uniformsList, m_uniforms, textures);\\n }\\n if (material.isShaderMaterial && material.uniformsNeedUpdate === true) {\\n WebGLUniforms.upload(_gl, materialProperties.uniformsList, m_uniforms, textures);\\n material.uniformsNeedUpdate = false;\\n }\\n if (material.isSpriteMaterial) {\\n p_uniforms.setValue(_gl, \\\\\\\"center\\\\\\\", object.center);\\n }\\n p_uniforms.setValue(_gl, \\\\\\\"modelViewMatrix\\\\\\\", object.modelViewMatrix);\\n p_uniforms.setValue(_gl, \\\\\\\"normalMatrix\\\\\\\", object.normalMatrix);\\n p_uniforms.setValue(_gl, \\\\\\\"modelMatrix\\\\\\\", object.matrixWorld);\\n return program;\\n }\\n function markUniformsLightsNeedsUpdate(uniforms, value) {\\n uniforms.ambientLightColor.needsUpdate = value;\\n uniforms.lightProbe.needsUpdate = value;\\n uniforms.directionalLights.needsUpdate = value;\\n uniforms.directionalLightShadows.needsUpdate = value;\\n uniforms.pointLights.needsUpdate = value;\\n uniforms.pointLightShadows.needsUpdate = value;\\n uniforms.spotLights.needsUpdate = value;\\n uniforms.spotLightShadows.needsUpdate = value;\\n uniforms.rectAreaLights.needsUpdate = value;\\n uniforms.hemisphereLights.needsUpdate = value;\\n }\\n function materialNeedsLights(material) {\\n return material.isMeshLambertMaterial || material.isMeshToonMaterial || material.isMeshPhongMaterial || material.isMeshStandardMaterial || material.isShadowMaterial || material.isShaderMaterial && material.lights === true;\\n }\\n this.getActiveCubeFace = function() {\\n return _currentActiveCubeFace;\\n };\\n this.getActiveMipmapLevel = function() {\\n return _currentActiveMipmapLevel;\\n };\\n this.getRenderTarget = function() {\\n return _currentRenderTarget;\\n };\\n this.setRenderTarget = function(renderTarget, activeCubeFace = 0, activeMipmapLevel = 0) {\\n _currentRenderTarget = renderTarget;\\n _currentActiveCubeFace = activeCubeFace;\\n _currentActiveMipmapLevel = activeMipmapLevel;\\n if (renderTarget && properties.get(renderTarget).__webglFramebuffer === void 0) {\\n textures.setupRenderTarget(renderTarget);\\n }\\n let framebuffer = null;\\n let isCube = false;\\n let isRenderTarget3D = false;\\n if (renderTarget) {\\n const texture = renderTarget.texture;\\n if (texture.isDataTexture3D || texture.isDataTexture2DArray) {\\n isRenderTarget3D = true;\\n }\\n const __webglFramebuffer = properties.get(renderTarget).__webglFramebuffer;\\n if (renderTarget.isWebGLCubeRenderTarget) {\\n framebuffer = __webglFramebuffer[activeCubeFace];\\n isCube = true;\\n } else if (renderTarget.isWebGLMultisampleRenderTarget) {\\n framebuffer = properties.get(renderTarget).__webglMultisampledFramebuffer;\\n } else {\\n framebuffer = __webglFramebuffer;\\n }\\n _currentViewport.copy(renderTarget.viewport);\\n _currentScissor.copy(renderTarget.scissor);\\n _currentScissorTest = renderTarget.scissorTest;\\n } else {\\n _currentViewport.copy(_viewport).multiplyScalar(_pixelRatio).floor();\\n _currentScissor.copy(_scissor).multiplyScalar(_pixelRatio).floor();\\n _currentScissorTest = _scissorTest;\\n }\\n const framebufferBound = state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer);\\n if (framebufferBound && capabilities.drawBuffers) {\\n let needsUpdate = false;\\n if (renderTarget) {\\n if (renderTarget.isWebGLMultipleRenderTargets) {\\n const textures2 = renderTarget.texture;\\n if (_currentDrawBuffers.length !== textures2.length || _currentDrawBuffers[0] !== _gl.COLOR_ATTACHMENT0) {\\n for (let i = 0, il = textures2.length; i < il; i++) {\\n _currentDrawBuffers[i] = _gl.COLOR_ATTACHMENT0 + i;\\n }\\n _currentDrawBuffers.length = textures2.length;\\n needsUpdate = true;\\n }\\n } else {\\n if (_currentDrawBuffers.length !== 1 || _currentDrawBuffers[0] !== _gl.COLOR_ATTACHMENT0) {\\n _currentDrawBuffers[0] = _gl.COLOR_ATTACHMENT0;\\n _currentDrawBuffers.length = 1;\\n needsUpdate = true;\\n }\\n }\\n } else {\\n if (_currentDrawBuffers.length !== 1 || _currentDrawBuffers[0] !== _gl.BACK) {\\n _currentDrawBuffers[0] = _gl.BACK;\\n _currentDrawBuffers.length = 1;\\n needsUpdate = true;\\n }\\n }\\n if (needsUpdate) {\\n if (capabilities.isWebGL2) {\\n _gl.drawBuffers(_currentDrawBuffers);\\n } else {\\n extensions.get(\\\\\\\"WEBGL_draw_buffers\\\\\\\").drawBuffersWEBGL(_currentDrawBuffers);\\n }\\n }\\n }\\n state.viewport(_currentViewport);\\n state.scissor(_currentScissor);\\n state.setScissorTest(_currentScissorTest);\\n if (isCube) {\\n const textureProperties = properties.get(renderTarget.texture);\\n _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_CUBE_MAP_POSITIVE_X + activeCubeFace, textureProperties.__webglTexture, activeMipmapLevel);\\n } else if (isRenderTarget3D) {\\n const textureProperties = properties.get(renderTarget.texture);\\n const layer = activeCubeFace || 0;\\n _gl.framebufferTextureLayer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, textureProperties.__webglTexture, activeMipmapLevel || 0, layer);\\n }\\n _currentMaterialId = -1;\\n };\\n this.readRenderTargetPixels = function(renderTarget, x, y, width, height, buffer, activeCubeFaceIndex) {\\n if (!(renderTarget && renderTarget.isWebGLRenderTarget)) {\\n console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.\\\\\\\");\\n return;\\n }\\n let framebuffer = properties.get(renderTarget).__webglFramebuffer;\\n if (renderTarget.isWebGLCubeRenderTarget && activeCubeFaceIndex !== void 0) {\\n framebuffer = framebuffer[activeCubeFaceIndex];\\n }\\n if (framebuffer) {\\n state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer);\\n try {\\n const texture = renderTarget.texture;\\n const textureFormat = texture.format;\\n const textureType = texture.type;\\n if (textureFormat !== RGBAFormat && utils.convert(textureFormat) !== _gl.getParameter(_gl.IMPLEMENTATION_COLOR_READ_FORMAT)) {\\n console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.\\\\\\\");\\n return;\\n }\\n const halfFloatSupportedByExt = textureType === HalfFloatType && (extensions.has(\\\\\\\"EXT_color_buffer_half_float\\\\\\\") || capabilities.isWebGL2 && extensions.has(\\\\\\\"EXT_color_buffer_float\\\\\\\"));\\n if (textureType !== UnsignedByteType && utils.convert(textureType) !== _gl.getParameter(_gl.IMPLEMENTATION_COLOR_READ_TYPE) && !(textureType === FloatType && (capabilities.isWebGL2 || extensions.has(\\\\\\\"OES_texture_float\\\\\\\") || extensions.has(\\\\\\\"WEBGL_color_buffer_float\\\\\\\"))) && !halfFloatSupportedByExt) {\\n console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.\\\\\\\");\\n return;\\n }\\n if (_gl.checkFramebufferStatus(_gl.FRAMEBUFFER) === _gl.FRAMEBUFFER_COMPLETE) {\\n if (x >= 0 && x <= renderTarget.width - width && (y >= 0 && y <= renderTarget.height - height)) {\\n _gl.readPixels(x, y, width, height, utils.convert(textureFormat), utils.convert(textureType), buffer);\\n }\\n } else {\\n console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\");\\n }\\n } finally {\\n const framebuffer2 = _currentRenderTarget !== null ? properties.get(_currentRenderTarget).__webglFramebuffer : null;\\n state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer2);\\n }\\n }\\n };\\n this.copyFramebufferToTexture = function(position, texture, level = 0) {\\n const levelScale = Math.pow(2, -level);\\n const width = Math.floor(texture.image.width * levelScale);\\n const height = Math.floor(texture.image.height * levelScale);\\n let glFormat = utils.convert(texture.format);\\n if (capabilities.isWebGL2) {\\n if (glFormat === _gl.RGB)\\n glFormat = _gl.RGB8;\\n if (glFormat === _gl.RGBA)\\n glFormat = _gl.RGBA8;\\n }\\n textures.setTexture2D(texture, 0);\\n _gl.copyTexImage2D(_gl.TEXTURE_2D, level, glFormat, position.x, position.y, width, height, 0);\\n state.unbindTexture();\\n };\\n this.copyTextureToTexture = function(position, srcTexture, dstTexture, level = 0) {\\n const width = srcTexture.image.width;\\n const height = srcTexture.image.height;\\n const glFormat = utils.convert(dstTexture.format);\\n const glType = utils.convert(dstTexture.type);\\n textures.setTexture2D(dstTexture, 0);\\n _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, dstTexture.flipY);\\n _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, dstTexture.premultiplyAlpha);\\n _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, dstTexture.unpackAlignment);\\n if (srcTexture.isDataTexture) {\\n _gl.texSubImage2D(_gl.TEXTURE_2D, level, position.x, position.y, width, height, glFormat, glType, srcTexture.image.data);\\n } else {\\n if (srcTexture.isCompressedTexture) {\\n _gl.compressedTexSubImage2D(_gl.TEXTURE_2D, level, position.x, position.y, srcTexture.mipmaps[0].width, srcTexture.mipmaps[0].height, glFormat, srcTexture.mipmaps[0].data);\\n } else {\\n _gl.texSubImage2D(_gl.TEXTURE_2D, level, position.x, position.y, glFormat, glType, srcTexture.image);\\n }\\n }\\n if (level === 0 && dstTexture.generateMipmaps)\\n _gl.generateMipmap(_gl.TEXTURE_2D);\\n state.unbindTexture();\\n };\\n this.copyTextureToTexture3D = function(sourceBox, position, srcTexture, dstTexture, level = 0) {\\n if (_this.isWebGL1Renderer) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");\\n return;\\n }\\n const width = sourceBox.max.x - sourceBox.min.x + 1;\\n const height = sourceBox.max.y - sourceBox.min.y + 1;\\n const depth2 = sourceBox.max.z - sourceBox.min.z + 1;\\n const glFormat = utils.convert(dstTexture.format);\\n const glType = utils.convert(dstTexture.type);\\n let glTarget;\\n if (dstTexture.isDataTexture3D) {\\n textures.setTexture3D(dstTexture, 0);\\n glTarget = _gl.TEXTURE_3D;\\n } else if (dstTexture.isDataTexture2DArray) {\\n textures.setTexture2DArray(dstTexture, 0);\\n glTarget = _gl.TEXTURE_2D_ARRAY;\\n } else {\\n console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");\\n return;\\n }\\n _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, dstTexture.flipY);\\n _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, dstTexture.premultiplyAlpha);\\n _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, dstTexture.unpackAlignment);\\n const unpackRowLen = _gl.getParameter(_gl.UNPACK_ROW_LENGTH);\\n const unpackImageHeight = _gl.getParameter(_gl.UNPACK_IMAGE_HEIGHT);\\n const unpackSkipPixels = _gl.getParameter(_gl.UNPACK_SKIP_PIXELS);\\n const unpackSkipRows = _gl.getParameter(_gl.UNPACK_SKIP_ROWS);\\n const unpackSkipImages = _gl.getParameter(_gl.UNPACK_SKIP_IMAGES);\\n const image = srcTexture.isCompressedTexture ? srcTexture.mipmaps[0] : srcTexture.image;\\n _gl.pixelStorei(_gl.UNPACK_ROW_LENGTH, image.width);\\n _gl.pixelStorei(_gl.UNPACK_IMAGE_HEIGHT, image.height);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_PIXELS, sourceBox.min.x);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_ROWS, sourceBox.min.y);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_IMAGES, sourceBox.min.z);\\n if (srcTexture.isDataTexture || srcTexture.isDataTexture3D) {\\n _gl.texSubImage3D(glTarget, level, position.x, position.y, position.z, width, height, depth2, glFormat, glType, image.data);\\n } else {\\n if (srcTexture.isCompressedTexture) {\\n console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\");\\n _gl.compressedTexSubImage3D(glTarget, level, position.x, position.y, position.z, width, height, depth2, glFormat, image.data);\\n } else {\\n _gl.texSubImage3D(glTarget, level, position.x, position.y, position.z, width, height, depth2, glFormat, glType, image);\\n }\\n }\\n _gl.pixelStorei(_gl.UNPACK_ROW_LENGTH, unpackRowLen);\\n _gl.pixelStorei(_gl.UNPACK_IMAGE_HEIGHT, unpackImageHeight);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_PIXELS, unpackSkipPixels);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_ROWS, unpackSkipRows);\\n _gl.pixelStorei(_gl.UNPACK_SKIP_IMAGES, unpackSkipImages);\\n if (level === 0 && dstTexture.generateMipmaps)\\n _gl.generateMipmap(glTarget);\\n state.unbindTexture();\\n };\\n this.initTexture = function(texture) {\\n textures.setTexture2D(texture, 0);\\n state.unbindTexture();\\n };\\n this.resetState = function() {\\n _currentActiveCubeFace = 0;\\n _currentActiveMipmapLevel = 0;\\n _currentRenderTarget = null;\\n state.reset();\\n bindingStates.reset();\\n };\\n if (typeof __THREE_DEVTOOLS__ !== \\\\\\\"undefined\\\\\\\") {\\n __THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\", {detail: this}));\\n }\\n}\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/RenderersController.ts\\nvar CONTEXT_OPTIONS = {};\\nvar WebGLContext;\\n(function(WebGLContext2) {\\n WebGLContext2[\\\\\\\"WEBGL\\\\\\\"] = \\\\\\\"webgl\\\\\\\";\\n WebGLContext2[\\\\\\\"WEBGL2\\\\\\\"] = \\\\\\\"webgl2\\\\\\\";\\n WebGLContext2[\\\\\\\"EXPERIMENTAL_WEBGL\\\\\\\"] = \\\\\\\"experimental-webgl\\\\\\\";\\n WebGLContext2[\\\\\\\"EXPERIMENTAL_WEBGL2\\\\\\\"] = \\\\\\\"experimental-webgl2\\\\\\\";\\n})(WebGLContext || (WebGLContext = {}));\\nvar RenderersController = class {\\n constructor() {\\n this._next_renderer_id = 0;\\n this._renderers = {};\\n this._printDebug = false;\\n this._require_webgl2 = false;\\n this._resolves = [];\\n }\\n setPrintDebug(state = true) {\\n this._printDebug = state;\\n }\\n printDebug() {\\n return this._printDebug;\\n }\\n printDebugMessage(message) {\\n if (!this._printDebug) {\\n return;\\n }\\n console.warn(\\\\\\\"[Poly debug]\\\\\\\", message);\\n }\\n setRequireWebGL2() {\\n if (!this._require_webgl2) {\\n this._require_webgl2 = true;\\n }\\n }\\n webgl2Available() {\\n if (this._webgl2_available === void 0) {\\n this._webgl2_available = this._set_webgl2_available();\\n }\\n return this._webgl2_available;\\n }\\n _set_webgl2_available() {\\n const canvas = document.createElement(\\\\\\\"canvas\\\\\\\");\\n return (window.WebGL2RenderingContext && canvas.getContext(WebGLContext.WEBGL2)) != null;\\n }\\n createWebGLRenderer(params) {\\n const renderer = new WebGLRenderer(params);\\n this.printDebugMessage([`create renderer:`, params]);\\n return renderer;\\n }\\n createRenderingContext(canvas) {\\n let gl = null;\\n if (this._require_webgl2) {\\n gl = this._getRenderingContextWebgl(canvas, true);\\n if (!gl) {\\n console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\");\\n }\\n }\\n if (!gl) {\\n gl = this._getRenderingContextWebgl(canvas, false);\\n }\\n return gl;\\n }\\n _getRenderingContextWebgl(canvas, webgl2) {\\n let context_name;\\n if (this.webgl2Available()) {\\n context_name = WebGLContext.WEBGL2;\\n } else {\\n context_name = webgl2 ? WebGLContext.WEBGL2 : WebGLContext.WEBGL;\\n }\\n let gl = canvas.getContext(context_name, CONTEXT_OPTIONS);\\n if (gl) {\\n this.printDebugMessage(`create gl context: ${context_name}.`);\\n } else {\\n context_name = webgl2 ? WebGLContext.EXPERIMENTAL_WEBGL2 : WebGLContext.EXPERIMENTAL_WEBGL;\\n this.printDebugMessage(`create gl context: ${context_name}.`);\\n gl = canvas.getContext(context_name, CONTEXT_OPTIONS);\\n }\\n return gl;\\n }\\n registerRenderer(renderer) {\\n if (renderer._polygon_id) {\\n throw new Error(\\\\\\\"render already registered\\\\\\\");\\n }\\n renderer._polygon_id = this._next_renderer_id += 1;\\n this._renderers[renderer._polygon_id] = renderer;\\n if (Object.keys(this._renderers).length == 1) {\\n this.flush_callbacks_with_renderer(renderer);\\n }\\n }\\n deregisterRenderer(renderer) {\\n delete this._renderers[renderer._polygon_id];\\n renderer.dispose();\\n }\\n firstRenderer() {\\n const first_id = Object.keys(this._renderers)[0];\\n if (first_id) {\\n return this._renderers[first_id];\\n }\\n return null;\\n }\\n renderers() {\\n return Object.values(this._renderers);\\n }\\n flush_callbacks_with_renderer(renderer) {\\n let callback;\\n while (callback = this._resolves.pop()) {\\n callback(renderer);\\n }\\n }\\n async waitForRenderer() {\\n const renderer = this.firstRenderer();\\n if (renderer) {\\n return renderer;\\n } else {\\n return new Promise((resolve, reject) => {\\n this._resolves.push(resolve);\\n });\\n }\\n }\\n renderTarget(width, height, parameters) {\\n if (this.webgl2Available()) {\\n return new WebGLMultisampleRenderTarget(width, height, parameters);\\n } else {\\n return new WebGLRenderTarget(width, height, parameters);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/PolyLibsController.ts\\nvar PolyLibsController = class {\\n constructor() {\\n this._root = \\\\\\\"/three/js/libs\\\\\\\";\\n this._BASISPath = \\\\\\\"/basis\\\\\\\";\\n this._DRACOPath = \\\\\\\"/draco\\\\\\\";\\n this._DRACOGLTFPath = \\\\\\\"/draco/gltf\\\\\\\";\\n }\\n root() {\\n return this._root;\\n }\\n setRoot(url) {\\n this._root = url;\\n }\\n BASISPath() {\\n return this._BASISPath;\\n }\\n DRACOPath() {\\n return this._DRACOPath;\\n }\\n DRACOGLTFPath() {\\n return this._DRACOGLTFPath;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/nodes/NodesRegister.ts\\nvar NodesRegister = class {\\n constructor(poly) {\\n this.poly = poly;\\n this._node_register = new Map();\\n this._node_register_categories = new Map();\\n this._node_register_options = new Map();\\n }\\n static type(node) {\\n return this.filterType(node.type());\\n }\\n static filterType(nodeType) {\\n return nodeType.toLowerCase();\\n }\\n register(node, tab_menu_category, options) {\\n const context = node.context();\\n const nodeType = NodesRegister.type(node);\\n let current_nodes_for_context = this._node_register.get(context);\\n if (!current_nodes_for_context) {\\n current_nodes_for_context = new Map();\\n this._node_register.set(context, current_nodes_for_context);\\n }\\n const already_registered_node = current_nodes_for_context.get(nodeType);\\n if (already_registered_node) {\\n console.error(`node ${context}/${nodeType} already registered`);\\n return;\\n }\\n current_nodes_for_context.set(nodeType, node);\\n if (tab_menu_category) {\\n let current_categories = this._node_register_categories.get(context);\\n if (!current_categories) {\\n current_categories = new Map();\\n this._node_register_categories.set(context, current_categories);\\n }\\n current_categories.set(nodeType, tab_menu_category);\\n }\\n if (options) {\\n let current_options = this._node_register_options.get(context);\\n if (!current_options) {\\n current_options = new Map();\\n this._node_register_options.set(context, current_options);\\n }\\n current_options.set(nodeType, options);\\n }\\n this.poly.pluginsRegister.registerNode(node);\\n }\\n deregister(context, nodeType) {\\n nodeType = NodesRegister.filterType(nodeType);\\n this._node_register.get(context)?.delete(nodeType);\\n this._node_register_categories.get(context)?.delete(nodeType);\\n this._node_register_options.get(context)?.delete(nodeType);\\n }\\n isRegistered(context, nodeType) {\\n const nodes_for_context = this._node_register.get(context);\\n if (!nodes_for_context) {\\n return false;\\n }\\n nodeType = NodesRegister.filterType(nodeType);\\n return nodes_for_context.get(nodeType) != null;\\n }\\n nodeOptions(context, nodeType) {\\n nodeType = NodesRegister.filterType(nodeType);\\n return this._node_register_options.get(context)?.get(nodeType);\\n }\\n registeredNodesForContextAndParentType(context, parent_node_type) {\\n const map = this._node_register.get(context);\\n if (map) {\\n const nodes_for_context = [];\\n this._node_register.get(context)?.forEach((node, type) => {\\n nodes_for_context.push(node);\\n });\\n return nodes_for_context.filter((node) => {\\n const nodeType = NodesRegister.type(node);\\n const options = this._node_register_options.get(context)?.get(nodeType);\\n if (!options) {\\n return true;\\n } else {\\n const option_only = options[\\\\\\\"only\\\\\\\"];\\n const option_except = options[\\\\\\\"except\\\\\\\"];\\n const context_and_type = `${context}/${parent_node_type}`;\\n if (option_only) {\\n return option_only.includes(context_and_type);\\n }\\n if (option_except) {\\n return !option_except.includes(context_and_type);\\n }\\n return true;\\n }\\n });\\n } else {\\n return [];\\n }\\n }\\n registeredNodes(context, parentNodeType) {\\n const nodesByType = {};\\n const nodes = this.registeredNodesForContextAndParentType(context, parentNodeType);\\n for (let node of nodes) {\\n const nodeType = NodesRegister.type(node);\\n nodesByType[nodeType] = node;\\n }\\n return nodesByType;\\n }\\n registeredCategory(context, nodeType) {\\n nodeType = NodesRegister.filterType(nodeType);\\n return this._node_register_categories.get(context)?.get(nodeType);\\n }\\n map() {\\n return this._node_register;\\n }\\n};\\nvar OperationsRegister = class {\\n constructor(poly) {\\n this.poly = poly;\\n this._operation_register = new Map();\\n }\\n static type(node) {\\n return this.filterType(node.type());\\n }\\n static filterType(nodeType) {\\n return nodeType.toLowerCase();\\n }\\n register(operation) {\\n const context = operation.context();\\n let current_operations_for_context = this._operation_register.get(context);\\n if (!current_operations_for_context) {\\n current_operations_for_context = new Map();\\n this._operation_register.set(context, current_operations_for_context);\\n }\\n const operationType = OperationsRegister.type(operation);\\n const already_registered_operation = current_operations_for_context.get(operationType);\\n if (already_registered_operation) {\\n const message = `operation ${context}/${operationType} already registered`;\\n console.error(message);\\n return;\\n }\\n current_operations_for_context.set(operationType, operation);\\n this.poly.pluginsRegister.registerOperation(operation);\\n }\\n registeredOperationsForContextAndParentType(context, parentNodeType) {\\n const map = this._operation_register.get(context);\\n if (map) {\\n const nodes_for_context = [];\\n this._operation_register.get(context)?.forEach((operation, type) => {\\n nodes_for_context.push(operation);\\n });\\n return nodes_for_context;\\n } else {\\n return [];\\n }\\n }\\n registeredOperation(context, operationType) {\\n const current_operations_for_context = this._operation_register.get(context);\\n if (current_operations_for_context) {\\n operationType = OperationsRegister.filterType(operationType);\\n return current_operations_for_context.get(operationType);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/expressions/_BaseRegister.ts\\nvar BaseExpressionRegister = class {\\n constructor() {\\n this._methods_names = [];\\n this._methods_by_name = new Map();\\n }\\n register(expression, name) {\\n this._methods_names.push(name);\\n this._methods_by_name.set(name, expression);\\n }\\n getMethod(name) {\\n return this._methods_by_name.get(name);\\n }\\n availableMethods() {\\n return this._methods_names;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/expressions/ExpressionRegister.ts\\nvar ExpressionRegister = class extends BaseExpressionRegister {\\n getMethod(name) {\\n return super.getMethod(name);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/modules/_BaseRegister.ts\\nvar BaseModulesRegister = class {\\n constructor() {\\n this._module_by_name = new Map();\\n }\\n register(name, module) {\\n this._module_by_name.set(name, module);\\n }\\n moduleNames() {\\n const list = [];\\n this._module_by_name.forEach((module, moduleName) => {\\n list.push(moduleName);\\n });\\n return list;\\n }\\n module(moduleName) {\\n return this._module_by_name.get(moduleName);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/modules/DynamicModulesRegister.ts\\nvar DynamicModulesRegister = class extends BaseModulesRegister {\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/assemblers/_BaseRegister.ts\\nvar AssemblerName;\\n(function(AssemblerName3) {\\n AssemblerName3[\\\\\\\"GL_MESH_BASIC\\\\\\\"] = \\\\\\\"GL_MESH_BASIC\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_MESH_LAMBERT\\\\\\\"] = \\\\\\\"GL_MESH_LAMBERT\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_MESH_STANDARD\\\\\\\"] = \\\\\\\"GL_MESH_STANDARD\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_MESH_PHONG\\\\\\\"] = \\\\\\\"GL_MESH_PHONG\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_MESH_PHYSICAL\\\\\\\"] = \\\\\\\"GL_MESH_PHYSICAL\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_PARTICLES\\\\\\\"] = \\\\\\\"GL_PARTICLES\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_POINTS\\\\\\\"] = \\\\\\\"GL_POINTS\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_LINE\\\\\\\"] = \\\\\\\"GL_LINE\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_TEXTURE\\\\\\\"] = \\\\\\\"GL_TEXTURE\\\\\\\";\\n AssemblerName3[\\\\\\\"GL_VOLUME\\\\\\\"] = \\\\\\\"GL_VOLUME\\\\\\\";\\n})(AssemblerName || (AssemblerName = {}));\\nvar BaseAssemblersRegister = class {\\n constructor() {\\n this._controller_assembler_by_name = new Map();\\n }\\n register(name, controller, assembler) {\\n this._controller_assembler_by_name.set(name, {\\n controller,\\n assembler\\n });\\n }\\n unregister(name) {\\n this._controller_assembler_by_name.delete(name);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/assemblers/AssemblersRegistry.ts\\nvar AssemblersRegister = class extends BaseAssemblersRegister {\\n assembler(node, name) {\\n const pair = this._controller_assembler_by_name.get(name);\\n if (pair) {\\n const controller = pair.controller;\\n const assembler = pair.assembler;\\n return new controller(node, assembler);\\n }\\n return pair;\\n }\\n unregister(name) {\\n const pair = this._controller_assembler_by_name.get(name);\\n super.unregister(name);\\n return pair;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/plugins/PluginsRegister.ts\\nvar PluginsRegister = class {\\n constructor(poly) {\\n this.poly = poly;\\n this._plugins_by_name = new Map();\\n this._plugin_name_by_node_context_by_type = new Map();\\n this._plugin_name_by_operation_context_by_type = new Map();\\n }\\n register(plugin) {\\n this._current_plugin = plugin;\\n this._plugins_by_name.set(plugin.name(), plugin);\\n plugin.init(this.poly);\\n this._current_plugin = void 0;\\n }\\n pluginByName(pluginName) {\\n return this._plugins_by_name.get(pluginName);\\n }\\n registerNode(node) {\\n if (!this._current_plugin) {\\n return;\\n }\\n const context = node.context();\\n const type = node.type();\\n let map_for_context = this._plugin_name_by_node_context_by_type.get(context);\\n if (!map_for_context) {\\n map_for_context = new Map();\\n this._plugin_name_by_node_context_by_type.set(context, map_for_context);\\n }\\n map_for_context.set(type, this._current_plugin.name());\\n }\\n registerOperation(operation) {\\n if (!this._current_plugin) {\\n return;\\n }\\n const context = operation.context();\\n const type = operation.type();\\n let map_for_context = this._plugin_name_by_operation_context_by_type.get(context);\\n if (!map_for_context) {\\n map_for_context = new Map();\\n this._plugin_name_by_operation_context_by_type.set(context, map_for_context);\\n }\\n map_for_context.set(type, this._current_plugin.name());\\n }\\n toJson() {\\n const data = {\\n plugins: {},\\n nodes: {},\\n operations: {}\\n };\\n this._plugins_by_name.forEach((plugin, name) => {\\n data.plugins[name] = plugin.toJSON();\\n });\\n this._plugin_name_by_node_context_by_type.forEach((map_for_context, context) => {\\n data.nodes[context] = {};\\n map_for_context.forEach((plugin_name, type) => {\\n data.nodes[context][type] = plugin_name;\\n });\\n });\\n this._plugin_name_by_operation_context_by_type.forEach((map_for_context, context) => {\\n data.operations[context] = {};\\n map_for_context.forEach((plugin_name, type) => {\\n data.operations[context][type] = plugin_name;\\n });\\n });\\n return data;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/cameras/CamerasRegister.ts\\nvar CamerasRegister = class {\\n constructor(poly) {\\n this._camera_types = [];\\n }\\n register(node) {\\n const node_type = node.type();\\n if (!this._camera_types.includes(node_type)) {\\n this._camera_types.push(node_type);\\n }\\n }\\n registeredTypes() {\\n return this._camera_types;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/BlobUtils.ts\\nfunction createObjectURL(fileOrBlob) {\\n const urlCreator = window.URL || window.webkitURL;\\n return urlCreator.createObjectURL(fileOrBlob);\\n}\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/BlobsController.ts\\nvar BlobsController = class {\\n constructor() {\\n this._blobUrlsByStoredUrl = new Map();\\n this._blobsByStoredUrl = new Map();\\n this._blobDataByNodeId = new Map();\\n this._globalBlobsByStoredUrl = new Map();\\n }\\n registerBlobUrl(data) {\\n if (!Poly.playerMode()) {\\n return;\\n }\\n this._blobUrlsByStoredUrl.set(data.storedUrl, data.blobUrl);\\n }\\n deregisterUrl(url) {\\n this._blobUrlsByStoredUrl.delete(url);\\n }\\n blobUrl(storedUrl) {\\n return this._blobUrlsByStoredUrl.get(storedUrl);\\n }\\n clear() {\\n this._blobUrlsByStoredUrl.clear();\\n this._blobsByStoredUrl.clear();\\n this._blobDataByNodeId.clear();\\n }\\n _clearBlobForNode(node) {\\n const blobData = this._blobDataByNodeId.get(node.graphNodeId());\\n if (blobData) {\\n this._blobsByStoredUrl.delete(blobData.storedUrl);\\n this._blobUrlsByStoredUrl.delete(blobData.storedUrl);\\n }\\n this._blobDataByNodeId.delete(node.graphNodeId());\\n }\\n _assignBlobToNode(node, blobData) {\\n this._clearBlobForNode(node);\\n this._blobDataByNodeId.set(node.graphNodeId(), {\\n storedUrl: blobData.storedUrl,\\n fullUrl: blobData.fullUrl\\n });\\n }\\n async fetchBlobGlobal(options) {\\n if (Poly.playerMode()) {\\n return {};\\n }\\n try {\\n const existingBlob = this._blobUrlsByStoredUrl.get(options.storedUrl);\\n if (existingBlob) {\\n return {};\\n }\\n const remapedUrl = Poly.assetUrls.remapedUrl(options.fullUrl);\\n const response = await fetch(remapedUrl || options.fullUrl);\\n if (response.ok) {\\n const blob = await response.blob();\\n this._blobsByStoredUrl.set(options.storedUrl, blob);\\n this._blobUrlsByStoredUrl.set(options.storedUrl, this.createBlobUrl(blob));\\n this._globalBlobsByStoredUrl.set(options.storedUrl, blob);\\n return {\\n blobData: {\\n storedUrl: options.storedUrl,\\n fullUrl: options.fullUrl\\n }\\n };\\n } else {\\n return {error: `failed to fetch ${options.fullUrl}`};\\n }\\n } catch (err) {\\n return {error: `failed to fetch ${options.fullUrl}`};\\n }\\n }\\n async fetchBlobForNode(options) {\\n if (Poly.playerMode()) {\\n return {};\\n }\\n try {\\n const existingBlob = this._blobUrlsByStoredUrl.get(options.storedUrl);\\n if (existingBlob) {\\n return {};\\n }\\n const remapedUrl = Poly.assetUrls.remapedUrl(options.fullUrl);\\n const response = await fetch(remapedUrl || options.fullUrl);\\n if (response.ok) {\\n const blob = await response.blob();\\n this._blobsByStoredUrl.set(options.storedUrl, blob);\\n this._blobUrlsByStoredUrl.set(options.storedUrl, this.createBlobUrl(blob));\\n this._scene = options.node.scene();\\n this._assignBlobToNode(options.node, {\\n storedUrl: options.storedUrl,\\n fullUrl: options.fullUrl\\n });\\n return {\\n blobData: {\\n storedUrl: options.storedUrl,\\n fullUrl: options.fullUrl\\n }\\n };\\n } else {\\n return {error: `failed to fetch ${options.fullUrl}`};\\n }\\n } catch (err) {\\n return {error: `failed to fetch ${options.fullUrl}`};\\n }\\n }\\n forEachBlob(callback) {\\n this._blobDataByNodeId.forEach((blobData, nodeGraphNodeId) => {\\n if (this._scene) {\\n const node = this._scene.graph.nodeFromId(nodeGraphNodeId);\\n if (node) {\\n const {storedUrl} = blobData;\\n const blob = this._blobsByStoredUrl.get(storedUrl);\\n if (blob) {\\n callback(blob, storedUrl);\\n }\\n }\\n }\\n });\\n let storedUrls = [];\\n const blobsByStoreUrl = new Map();\\n this._globalBlobsByStoredUrl.forEach((blob, storedUrl) => {\\n storedUrls.push(storedUrl);\\n blobsByStoreUrl.set(storedUrl, blob);\\n });\\n storedUrls = storedUrls.sort();\\n storedUrls.forEach((storedUrl) => {\\n const blob = this._globalBlobsByStoredUrl.get(storedUrl);\\n if (blob) {\\n callback(blob, storedUrl);\\n }\\n });\\n }\\n createBlobUrl(blob) {\\n return createObjectURL(blob);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/AssetUrlsController.ts\\nvar AssetUrlsController = class {\\n setMap(map) {\\n this._map = map;\\n }\\n remapedUrl(url) {\\n if (!this._map) {\\n return;\\n }\\n const elements = url.split(\\\\\\\"?\\\\\\\");\\n const preQuery = elements[0];\\n const query = elements[1];\\n const remapedUrl = this._map[preQuery];\\n if (remapedUrl) {\\n if (query) {\\n return `${remapedUrl}?${query}`;\\n } else {\\n return remapedUrl;\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/DomEffects.ts\\nvar DomEffects = class {\\n static fadeOut(element) {\\n return new Promise((resolve) => {\\n const fadeEffect = setInterval(() => {\\n if (!element.style.opacity) {\\n element.style.opacity = \\\\\\\"1\\\\\\\";\\n }\\n const opacity = parseFloat(element.style.opacity);\\n if (opacity > 0) {\\n element.style.opacity = `${opacity - 0.05}`;\\n } else {\\n resolve();\\n clearInterval(fadeEffect);\\n }\\n }, 20);\\n });\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/io/self_contained/Common.ts\\nvar SelfContainedFileName;\\n(function(SelfContainedFileName2) {\\n SelfContainedFileName2[\\\\\\\"CODE\\\\\\\"] = \\\\\\\"code.json\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"EDITOR\\\\\\\"] = \\\\\\\"editor.json\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"ASSETS\\\\\\\"] = \\\\\\\"assets.json\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"POLYGONJS\\\\\\\"] = \\\\\\\"js/all.js\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"POLY_CONFIG\\\\\\\"] = \\\\\\\"js/polyConfig.js\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"JS_FILES\\\\\\\"] = \\\\\\\"js_files.json\\\\\\\";\\n SelfContainedFileName2[\\\\\\\"POSTER\\\\\\\"] = \\\\\\\"poster.png\\\\\\\";\\n})(SelfContainedFileName || (SelfContainedFileName = {}));\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/SelfContainedSceneLoader.ts\\nvar SelfContainedScenesLoader = class {\\n markAsLoaded(callback, sceneJsonImporterContructor) {\\n this._sceneJsonImporterContructor = sceneJsonImporterContructor;\\n callback();\\n }\\n load(map) {\\n if (!this._sceneJsonImporterContructor) {\\n return;\\n }\\n const elements = [];\\n map.forEach((data, element) => {\\n elements.push(element);\\n });\\n for (let element of elements) {\\n const data = map.get(element);\\n if (data) {\\n this._loadElement(element, data, this._sceneJsonImporterContructor);\\n map.delete(element);\\n }\\n }\\n }\\n async _loadElement(element, viewerData, sceneJsonImporterContructor) {\\n const {sceneData, assetsManifest, unzippedData} = viewerData;\\n const storedUrls = Object.keys(assetsManifest);\\n for (let storedUrl of storedUrls) {\\n const assetUrl = assetsManifest[storedUrl];\\n const assetUintArray = unzippedData[`assets/${assetUrl}`];\\n if (!assetUintArray) {\\n console.error(storedUrl, assetUintArray);\\n return;\\n }\\n const assetBlob = new Blob([assetUintArray]);\\n const blobUrl = Poly.blobs.createBlobUrl(assetBlob);\\n const blobUrlData = {\\n storedUrl,\\n blobUrl\\n };\\n Poly.blobs.registerBlobUrl(blobUrlData);\\n }\\n Poly.setPlayerMode(true);\\n Poly.libs.setRoot(null);\\n const polyConfigId = `${Math.random()}`.replace(\\\\\\\".\\\\\\\", \\\\\\\"_\\\\\\\");\\n const ids = {\\n Poly: `___POLY_polyConfig_configurePolygonjs_${polyConfigId}`,\\n scriptElementId: `___POLY_polyConfig_scriptElement_${polyConfigId}`,\\n loadSceneArgs: `___POLY_polyConfig_loadSceneArgs_${polyConfigId}`\\n };\\n window[ids.Poly] = Poly;\\n const method = this._loadScene.bind(this);\\n const loadSceneArgs = {\\n method,\\n element,\\n sceneData,\\n sceneJsonImporterContructor\\n };\\n window[ids.loadSceneArgs] = loadSceneArgs;\\n const polyConfigLoaded = this._loadPolyConfig(ids, unzippedData);\\n if (polyConfigLoaded) {\\n return;\\n }\\n this._loadScene(element, sceneData, sceneJsonImporterContructor);\\n }\\n _loadPolyConfig(ids, unzippedData) {\\n const polyConfigArray = unzippedData[SelfContainedFileName.POLY_CONFIG];\\n if (!polyConfigArray) {\\n return false;\\n }\\n const polyConfigUrl = this._createJsBlob(polyConfigArray, \\\\\\\"polyConfig\\\\\\\");\\n let script = document.getElementById(ids.scriptElementId);\\n const lines = [];\\n lines.push(`import {configurePolygonjs, configureScene} from '${polyConfigUrl}';`);\\n lines.push(`configurePolygonjs(window.${ids.Poly});`);\\n lines.push(`window.${ids.loadSceneArgs}.method(window.${ids.loadSceneArgs}.element, window.${ids.loadSceneArgs}.sceneData, window.${ids.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`);\\n lines.push(`delete window.${ids.loadSceneArgs};`);\\n if (!script) {\\n script = document.createElement(\\\\\\\"script\\\\\\\");\\n script.setAttribute(\\\\\\\"type\\\\\\\", \\\\\\\"module\\\\\\\");\\n script.text = lines.join(\\\\\\\"\\\\n\\\\\\\");\\n document.body.append(script);\\n }\\n return true;\\n }\\n async _loadScene(element, sceneData, sceneJsonImporterContructor, configureScene2) {\\n this._fadeOutPoster(element);\\n const importer = new sceneJsonImporterContructor(sceneData);\\n const scene = await importer.scene();\\n if (configureScene2) {\\n configureScene2(scene);\\n }\\n const cameraNode = scene.mainCameraNode();\\n if (!cameraNode) {\\n console.warn(\\\\\\\"no master camera found\\\\\\\");\\n return;\\n }\\n const viewer = cameraNode.createViewer(element);\\n scene.play();\\n element.scene = scene;\\n element.viewer = viewer;\\n }\\n _fadeOutPoster(element) {\\n const posterElement = element.firstElementChild;\\n if (posterElement) {\\n posterElement.style.pointerEvents = \\\\\\\"none\\\\\\\";\\n DomEffects.fadeOut(posterElement).then(() => {\\n posterElement.parentElement?.removeChild(posterElement);\\n });\\n }\\n }\\n _createJsBlob(array, filename) {\\n const blob = new Blob([array]);\\n const file = new File([blob], `${filename}.js`, {type: \\\\\\\"application/javascript\\\\\\\"});\\n return createObjectURL(file);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/PerformanceController.ts\\nvar PolyPerformanceformanceController = class {\\n setPerformanceManager(p) {\\n this._performanceManager = p;\\n }\\n performanceManager() {\\n return this._performanceManager || window.performance;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/Poly.ts\\nvar PolyEngine = class {\\n constructor() {\\n this.renderersController = new RenderersController();\\n this.nodesRegister = new NodesRegister(this);\\n this.operationsRegister = new OperationsRegister(this);\\n this.expressionsRegister = new ExpressionRegister();\\n this.modulesRegister = new DynamicModulesRegister();\\n this.assemblersRegister = new AssemblersRegister();\\n this.pluginsRegister = new PluginsRegister(this);\\n this.camerasRegister = new CamerasRegister(this);\\n this.blobs = new BlobsController();\\n this.assetUrls = new AssetUrlsController();\\n this.selfContainedScenesLoader = new SelfContainedScenesLoader();\\n this.performance = new PolyPerformanceformanceController();\\n this.scenesByUuid = {};\\n this._player_mode = true;\\n this._logger = null;\\n }\\n static _instance_() {\\n if (window.__POLYGONJS_POLY_INSTANCE__) {\\n return window.__POLYGONJS_POLY_INSTANCE__;\\n } else {\\n const instance = new PolyEngine();\\n window.__POLYGONJS_POLY_INSTANCE__ = instance;\\n return window.__POLYGONJS_POLY_INSTANCE__;\\n }\\n }\\n setPlayerMode(mode) {\\n this._player_mode = mode;\\n }\\n playerMode() {\\n return this._player_mode;\\n }\\n registerNode(node, tab_menu_category, options) {\\n this.nodesRegister.register(node, tab_menu_category, options);\\n }\\n registerOperation(operation) {\\n this.operationsRegister.register(operation);\\n }\\n registerCamera(node) {\\n this.camerasRegister.register(node);\\n }\\n registerPlugin(plugin) {\\n this.pluginsRegister.register(plugin);\\n }\\n registeredNodes(parent_context, type) {\\n return this.nodesRegister.registeredNodes(parent_context, type);\\n }\\n registeredOperation(parent_context, operation_type) {\\n return this.operationsRegister.registeredOperation(parent_context, operation_type);\\n }\\n registeredCameraTypes() {\\n return this.camerasRegister.registeredTypes();\\n }\\n inWorkerThread() {\\n return false;\\n }\\n desktopController() {\\n }\\n get libs() {\\n return this._libs_controller = this._libs_controller || new PolyLibsController();\\n }\\n setEnv(env) {\\n this._env = env;\\n }\\n env() {\\n return this._env;\\n }\\n setLogger(logger) {\\n this._logger = logger;\\n }\\n log(message, ...optionalParams) {\\n this._logger?.log(...[message, ...optionalParams]);\\n }\\n warn(message, ...optionalParams) {\\n this._logger?.warn(...[message, ...optionalParams]);\\n }\\n error(message, ...optionalParams) {\\n this._logger?.error(...[message, ...optionalParams]);\\n }\\n};\\nvar Poly = PolyEngine._instance_();\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/graph/DirtyController.ts\\nvar DirtyController = class {\\n constructor(node) {\\n this.node = node;\\n this._dirty_count = 0;\\n this._dirty = true;\\n }\\n dispose() {\\n this._cached_successors = void 0;\\n this._post_dirty_hooks = void 0;\\n this._post_dirty_hook_names = void 0;\\n }\\n isDirty() {\\n return this._dirty === true;\\n }\\n dirtyTimestamp() {\\n return this._dirty_timestamp;\\n }\\n dirtyCount() {\\n return this._dirty_count;\\n }\\n addPostDirtyHook(name, method) {\\n this._post_dirty_hook_names = this._post_dirty_hook_names || [];\\n this._post_dirty_hooks = this._post_dirty_hooks || [];\\n if (!this._post_dirty_hook_names.includes(name)) {\\n this._post_dirty_hook_names.push(name);\\n this._post_dirty_hooks.push(method);\\n } else {\\n console.warn(`hook with name ${name} already exists`, this.node);\\n }\\n }\\n removePostDirtyHook(name) {\\n if (this._post_dirty_hook_names && this._post_dirty_hooks) {\\n const index = this._post_dirty_hook_names.indexOf(name);\\n if (index >= 0) {\\n this._post_dirty_hook_names.splice(index, 1);\\n this._post_dirty_hooks.splice(index, 1);\\n }\\n }\\n }\\n hasHook(name) {\\n if (this._post_dirty_hook_names) {\\n return this._post_dirty_hook_names.includes(name);\\n }\\n return false;\\n }\\n removeDirtyState() {\\n this._dirty = false;\\n }\\n setForbiddenTriggerNodes(nodes) {\\n this._forbidden_trigger_nodes = nodes.map((n) => n.graphNodeId());\\n }\\n setDirty(original_trigger_graph_node, propagate) {\\n if (propagate == null) {\\n propagate = true;\\n }\\n if (original_trigger_graph_node && this._forbidden_trigger_nodes && this._forbidden_trigger_nodes.includes(original_trigger_graph_node.graphNodeId())) {\\n return;\\n }\\n if (original_trigger_graph_node == null) {\\n original_trigger_graph_node = this.node;\\n }\\n this._dirty = true;\\n const performance2 = Poly.performance.performanceManager();\\n this._dirty_timestamp = performance2.now();\\n this._dirty_count += 1;\\n this.runPostDirtyHooks(original_trigger_graph_node);\\n if (propagate === true) {\\n this.setSuccessorsDirty(original_trigger_graph_node);\\n }\\n }\\n runPostDirtyHooks(original_trigger_graph_node) {\\n if (this._post_dirty_hooks) {\\n const cooker = this.node.scene().cooker;\\n if (cooker.blocked) {\\n cooker.enqueue(this.node, original_trigger_graph_node);\\n } else {\\n for (let hook of this._post_dirty_hooks) {\\n hook(original_trigger_graph_node);\\n }\\n }\\n }\\n }\\n setSuccessorsDirty(original_trigger_graph_node) {\\n const propagate = false;\\n this._cached_successors = this._cached_successors || this.node.graphAllSuccessors();\\n for (let successor of this._cached_successors) {\\n successor.dirtyController.setDirty(original_trigger_graph_node, propagate);\\n }\\n }\\n clearSuccessorsCache() {\\n this._cached_successors = void 0;\\n }\\n clearSuccessorsCacheWithPredecessors() {\\n this.clearSuccessorsCache();\\n for (let predecessor of this.node.graphAllPredecessors()) {\\n predecessor.dirtyController.clearSuccessorsCache();\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/graph/CoreGraphNode.ts\\nvar CoreGraphNode = class {\\n constructor(_scene, _name) {\\n this._scene = _scene;\\n this._name = _name;\\n this._dirty_controller = new DirtyController(this);\\n this._graph_node_id = _scene.graph.nextId();\\n _scene.graph.addNode(this);\\n this._graph = _scene.graph;\\n }\\n dispose() {\\n this._dirty_controller.dispose();\\n this.graphRemove();\\n }\\n name() {\\n return this._name;\\n }\\n setName(name) {\\n this._name = name;\\n }\\n scene() {\\n return this._scene;\\n }\\n graphNodeId() {\\n return this._graph_node_id;\\n }\\n get dirtyController() {\\n return this._dirty_controller;\\n }\\n setDirty(trigger) {\\n trigger = trigger || this;\\n this._dirty_controller.setDirty(trigger);\\n }\\n setSuccessorsDirty(trigger) {\\n this._dirty_controller.setSuccessorsDirty(trigger);\\n }\\n removeDirtyState() {\\n this._dirty_controller.removeDirtyState();\\n }\\n isDirty() {\\n return this._dirty_controller.isDirty();\\n }\\n addPostDirtyHook(name, callback) {\\n this._dirty_controller.addPostDirtyHook(name, callback);\\n }\\n graphRemove() {\\n this._graph.removeNode(this);\\n }\\n addGraphInput(src, check_if_graph_has_cycle = true) {\\n return this._graph.connect(src, this, check_if_graph_has_cycle);\\n }\\n removeGraphInput(src) {\\n this._graph.disconnect(src, this);\\n }\\n graphDisconnectPredecessors() {\\n this._graph.disconnectPredecessors(this);\\n }\\n graphDisconnectSuccessors() {\\n this._graph.disconnectSuccessors(this);\\n }\\n graphPredecessorIds() {\\n return this._graph.predecessorIds(this._graph_node_id) || [];\\n }\\n graphPredecessors() {\\n return this._graph.predecessors(this);\\n }\\n graphSuccessors() {\\n return this._graph.successors(this);\\n }\\n graphAllPredecessors() {\\n return this._graph.allPredecessors(this);\\n }\\n graphAllSuccessors() {\\n return this._graph.allSuccessors(this);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/NodeEvent.ts\\nvar NodeEvent;\\n(function(NodeEvent2) {\\n NodeEvent2[\\\\\\\"CREATED\\\\\\\"] = \\\\\\\"node_created\\\\\\\";\\n NodeEvent2[\\\\\\\"DELETED\\\\\\\"] = \\\\\\\"node_deleted\\\\\\\";\\n NodeEvent2[\\\\\\\"NAME_UPDATED\\\\\\\"] = \\\\\\\"node_name_update\\\\\\\";\\n NodeEvent2[\\\\\\\"OVERRIDE_CLONABLE_STATE_UPDATE\\\\\\\"] = \\\\\\\"node_override_clonable_state_update\\\\\\\";\\n NodeEvent2[\\\\\\\"NAMED_OUTPUTS_UPDATED\\\\\\\"] = \\\\\\\"node_named_outputs_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"NAMED_INPUTS_UPDATED\\\\\\\"] = \\\\\\\"node_named_inputs_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"INPUTS_UPDATED\\\\\\\"] = \\\\\\\"node_inputs_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"PARAMS_UPDATED\\\\\\\"] = \\\\\\\"node_params_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"UI_DATA_POSITION_UPDATED\\\\\\\"] = \\\\\\\"node_ui_data_position_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"UI_DATA_COMMENT_UPDATED\\\\\\\"] = \\\\\\\"node_ui_data_comment_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"ERROR_UPDATED\\\\\\\"] = \\\\\\\"node_error_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"FLAG_BYPASS_UPDATED\\\\\\\"] = \\\\\\\"bypass_flag_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"FLAG_DISPLAY_UPDATED\\\\\\\"] = \\\\\\\"display_flag_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"FLAG_OPTIMIZE_UPDATED\\\\\\\"] = \\\\\\\"optimize_flag_updated\\\\\\\";\\n NodeEvent2[\\\\\\\"SELECTION_UPDATED\\\\\\\"] = \\\\\\\"selection_updated\\\\\\\";\\n})(NodeEvent || (NodeEvent = {}));\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/Type.ts\\nvar CoreType = class {\\n static isNumber(value) {\\n return typeof value == \\\\\\\"number\\\\\\\";\\n }\\n static isVector(value) {\\n return value instanceof Vector2 || value instanceof Vector3 || value instanceof Vector4;\\n }\\n static isString(value) {\\n return typeof value == \\\\\\\"string\\\\\\\";\\n }\\n static isBoolean(value) {\\n return value === true || value === false;\\n }\\n static isNaN(value) {\\n return isNaN(value);\\n }\\n static isArray(value) {\\n return Array.isArray(value);\\n }\\n static isObject(value) {\\n var type = typeof value;\\n return value != null && (type == \\\\\\\"object\\\\\\\" || type == \\\\\\\"function\\\\\\\");\\n }\\n};\\nfunction isBooleanTrue(value) {\\n return value;\\n}\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/UIData.ts\\nvar UIData = class {\\n constructor(node, x = 0, y = 0) {\\n this.node = node;\\n this._position = new Vector2();\\n this._width = 50;\\n this._color = new Color(0.75, 0.75, 0.75);\\n this._layout_vertical = true;\\n this._json = {\\n x: 0,\\n y: 0\\n };\\n this._position.x = x;\\n this._position.y = y;\\n }\\n setComment(comment) {\\n this._comment = comment;\\n this.node.emit(NodeEvent.UI_DATA_COMMENT_UPDATED);\\n }\\n comment() {\\n return this._comment;\\n }\\n setColor(color) {\\n this._color = color;\\n }\\n color() {\\n return this._color;\\n }\\n setLayoutHorizontal() {\\n this._layout_vertical = false;\\n }\\n isLayoutVertical() {\\n return this._layout_vertical;\\n }\\n copy(ui_data) {\\n this._position.copy(ui_data.position());\\n this._color.copy(ui_data.color());\\n }\\n position() {\\n return this._position;\\n }\\n setPosition(new_position, y = 0) {\\n if (CoreType.isNumber(new_position)) {\\n const x = new_position;\\n this._position.set(x, y);\\n } else {\\n this._position.copy(new_position);\\n }\\n this.node.emit(NodeEvent.UI_DATA_POSITION_UPDATED);\\n }\\n translate(offset, snap = false) {\\n this._position.add(offset);\\n if (snap) {\\n this._position.x = Math.round(this._position.x);\\n this._position.y = Math.round(this._position.y);\\n }\\n this.node.emit(NodeEvent.UI_DATA_POSITION_UPDATED);\\n }\\n toJSON() {\\n this._json.x = this._position.x;\\n this._json.y = this._position.y;\\n this._json.comment = this._comment;\\n return this._json;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/flags/Base.ts\\nvar BaseFlag = class {\\n constructor(node) {\\n this.node = node;\\n this._state = true;\\n this._hooks = null;\\n }\\n onUpdate(hook) {\\n this._hooks = this._hooks || [];\\n this._hooks.push(hook);\\n }\\n _on_update() {\\n }\\n set(new_state) {\\n if (this._state != new_state) {\\n this._state = new_state;\\n this._on_update();\\n this.runHooks();\\n }\\n }\\n active() {\\n return this._state;\\n }\\n toggle() {\\n this.set(!this._state);\\n }\\n runHooks() {\\n if (this._hooks) {\\n for (let hook of this._hooks) {\\n hook();\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/flags/Bypass.ts\\nvar BypassFlag = class extends BaseFlag {\\n constructor() {\\n super(...arguments);\\n this._state = false;\\n }\\n _on_update() {\\n this.node.emit(NodeEvent.FLAG_BYPASS_UPDATED);\\n this.node.setDirty();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/flags/Display.ts\\nvar DisplayFlag = class extends BaseFlag {\\n _on_update() {\\n this.node.emit(NodeEvent.FLAG_DISPLAY_UPDATED);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/flags/Optimize.ts\\nvar OptimizeFlag = class extends BaseFlag {\\n constructor() {\\n super(...arguments);\\n this._state = false;\\n }\\n _on_update() {\\n this.node.emit(NodeEvent.FLAG_OPTIMIZE_UPDATED);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/FlagsController.ts\\nvar FlagsController = class {\\n constructor(node) {\\n this.node = node;\\n }\\n hasDisplay() {\\n return false;\\n }\\n hasBypass() {\\n return false;\\n }\\n hasOptimize() {\\n return false;\\n }\\n};\\nfunction Display(Base) {\\n return class Mixin extends Base {\\n constructor() {\\n super(...arguments);\\n this.display = new DisplayFlag(this.node);\\n }\\n hasDisplay() {\\n return true;\\n }\\n };\\n}\\nfunction Bypass(Base) {\\n return class Mixin extends Base {\\n constructor() {\\n super(...arguments);\\n this.bypass = new BypassFlag(this.node);\\n }\\n hasBypass() {\\n return true;\\n }\\n };\\n}\\nfunction Optimize(Base) {\\n return class Mixin extends Base {\\n constructor() {\\n super(...arguments);\\n this.optimize = new OptimizeFlag(this.node);\\n }\\n hasOptimize() {\\n return true;\\n }\\n };\\n}\\nvar FlagsControllerD = class extends Display(FlagsController) {\\n};\\nvar FlagsControllerB = class extends Bypass(FlagsController) {\\n};\\nvar FlagsControllerDB = class extends Bypass(Display(FlagsController)) {\\n};\\nvar FlagsControllerBO = class extends Optimize(Bypass(FlagsController)) {\\n};\\nvar FlagsControllerDBO = class extends Optimize(Bypass(Display(FlagsController))) {\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/states/Base.ts\\nvar BaseState = class {\\n constructor(node) {\\n this.node = node;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/states/TimeDependent.ts\\nvar TimeDependentState = class extends BaseState {\\n active() {\\n return this.paramsTimeDependent() || this.inputsTimeDependent();\\n }\\n paramsTimeDependent() {\\n const param_names = this.node.params.names;\\n for (let param_name of param_names) {\\n const param = this.node.params.get(param_name);\\n if (param && param.states.timeDependent.active()) {\\n return true;\\n }\\n }\\n return false;\\n }\\n inputsTimeDependent() {\\n const inputs = this.node.io.inputs.inputs();\\n for (let input of inputs) {\\n if (input && input.states.timeDependent.active()) {\\n return true;\\n }\\n }\\n return false;\\n }\\n forceTimeDependent() {\\n const predecessor_ids = this.node.graphPredecessors().map((n) => n.graphNodeId());\\n const frame_node = this.node.scene().timeController.graphNode;\\n if (!predecessor_ids.includes(frame_node.graphNodeId())) {\\n this.node.addGraphInput(frame_node, false);\\n }\\n }\\n unforceTimeDependent() {\\n const frame_node = this.node.scene().timeController.graphNode;\\n this.node.removeGraphInput(frame_node);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/states/Error.ts\\nvar NodeErrorState = class extends BaseState {\\n set(message) {\\n if (this._message != message) {\\n if (message) {\\n Poly.error(`[${this.node.path()}] error: '${message}'`);\\n }\\n this._message = message;\\n this.onUpdate();\\n }\\n }\\n message() {\\n return this._message;\\n }\\n clear() {\\n this.set(void 0);\\n }\\n active() {\\n return this._message != null;\\n }\\n onUpdate() {\\n if (this._message != null) {\\n this.node._setContainer(null, `from error '${this._message}'`);\\n }\\n this.node.emit(NodeEvent.ERROR_UPDATED);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/StatesController.ts\\nvar StatesController = class {\\n constructor(node) {\\n this.node = node;\\n this.timeDependent = new TimeDependentState(this.node);\\n this.error = new NodeErrorState(this.node);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/NameController.ts\\nvar NameController = class {\\n constructor(node) {\\n this.node = node;\\n this._graph_node = new CoreGraphNode(node.scene(), \\\\\\\"node_name_controller\\\\\\\");\\n }\\n dispose() {\\n this._graph_node.dispose();\\n this._on_set_name_hooks = void 0;\\n this._on_set_fullPath_hooks = void 0;\\n }\\n get graph_node() {\\n return this._graph_node;\\n }\\n static base_name(node) {\\n let base = node.type();\\n const last_char = base[base.length - 1];\\n if (!CoreType.isNaN(parseInt(last_char))) {\\n base += \\\\\\\"_\\\\\\\";\\n }\\n return `${base}1`;\\n }\\n request_name_to_parent(new_name) {\\n const parent = this.node.parent();\\n if (parent && parent.childrenAllowed() && parent.childrenController) {\\n parent.childrenController.set_child_name(this.node, new_name);\\n } else {\\n console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\");\\n }\\n }\\n setName(new_name) {\\n if (new_name != this.node.name()) {\\n this.request_name_to_parent(new_name);\\n }\\n }\\n update_name_from_parent(new_name) {\\n this.node._set_core_name(new_name);\\n this.post_setName();\\n this.run_post_set_fullPath_hooks();\\n if (this.node.childrenAllowed()) {\\n const children = this.node.childrenController?.children();\\n if (children) {\\n for (let child_node of children) {\\n child_node.nameController.run_post_set_fullPath_hooks();\\n }\\n }\\n }\\n if (this.node.lifecycle.creationCompleted()) {\\n this.node.scene().missingExpressionReferencesController.checkForMissingReferences(this.node);\\n this.node.scene().expressionsController.regenerateReferringExpressions(this.node);\\n }\\n this.node.scene().referencesController.notifyNameUpdated(this.node);\\n this.node.emit(NodeEvent.NAME_UPDATED);\\n }\\n add_post_set_name_hook(hook) {\\n this._on_set_name_hooks = this._on_set_name_hooks || [];\\n this._on_set_name_hooks.push(hook);\\n }\\n add_post_set_fullPath_hook(hook) {\\n this._on_set_fullPath_hooks = this._on_set_fullPath_hooks || [];\\n this._on_set_fullPath_hooks.push(hook);\\n }\\n post_setName() {\\n if (this._on_set_name_hooks) {\\n for (let hook of this._on_set_name_hooks) {\\n hook();\\n }\\n }\\n }\\n run_post_set_fullPath_hooks() {\\n if (this._on_set_fullPath_hooks) {\\n for (let hook of this._on_set_fullPath_hooks) {\\n hook();\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/Walker.ts\\nvar TypedNodePathParamValue = class {\\n constructor(_path = \\\\\\\"\\\\\\\") {\\n this._path = _path;\\n this._node = null;\\n }\\n set_path(path) {\\n this._path = path;\\n }\\n set_node(node) {\\n this._node = node;\\n }\\n path() {\\n return this._path;\\n }\\n node() {\\n return this._node;\\n }\\n resolve(node_start) {\\n this._node = CoreWalker.findNode(node_start, this._path);\\n }\\n clone() {\\n const cloned = new TypedNodePathParamValue(this._path);\\n cloned.set_node(this._node);\\n return cloned;\\n }\\n nodeWithContext(context, error_state) {\\n const found_node = this.node();\\n if (!found_node) {\\n error_state?.set(`no node found at ${this.path()}`);\\n return;\\n }\\n const node_context = found_node.context();\\n if (node_context == context) {\\n return found_node;\\n } else {\\n error_state?.set(`expected ${context} node, but got a ${node_context}`);\\n return;\\n }\\n }\\n};\\nvar TypedParamPathParamValue = class {\\n constructor(_path = \\\\\\\"\\\\\\\") {\\n this._path = _path;\\n this._param = null;\\n }\\n set_path(path) {\\n this._path = path;\\n }\\n set_param(param) {\\n this._param = param;\\n }\\n path() {\\n return this._path;\\n }\\n param() {\\n return this._param;\\n }\\n resolve(node_start) {\\n this._param = CoreWalker.findParam(node_start, this._path);\\n }\\n clone() {\\n const cloned = new TypedParamPathParamValue(this._path);\\n cloned.set_param(this._param);\\n return cloned;\\n }\\n paramWithType(paramType, error_state) {\\n const foundParam = this.param();\\n if (!foundParam) {\\n error_state?.set(`no param found at ${this.path()}`);\\n return;\\n }\\n if (foundParam.type() == paramType) {\\n return foundParam;\\n } else {\\n error_state?.set(`expected ${paramType} node, but got a ${foundParam.type()}`);\\n return;\\n }\\n }\\n};\\nvar CoreWalker2 = class {\\n static split_parent_child(path) {\\n const elements = path.split(CoreWalker2.SEPARATOR).filter((e) => e.length > 0);\\n const child_path = elements.pop();\\n const parent_path = elements.join(CoreWalker2.SEPARATOR);\\n return {parent: parent_path, child: child_path};\\n }\\n static findNode(node_src, path, decomposed_path) {\\n if (!node_src) {\\n return null;\\n }\\n const elements = path.split(CoreWalker2.SEPARATOR).filter((e) => e.length > 0);\\n const first_element = elements[0];\\n let next_node = null;\\n if (path[0] === CoreWalker2.SEPARATOR) {\\n const path_from_root = path.substr(1);\\n next_node = this.findNode(node_src.root(), path_from_root, decomposed_path);\\n } else {\\n switch (first_element) {\\n case CoreWalker2.PARENT:\\n decomposed_path?.add_path_element(first_element);\\n next_node = node_src.parent();\\n break;\\n case CoreWalker2.CURRENT:\\n decomposed_path?.add_path_element(first_element);\\n next_node = node_src;\\n break;\\n default:\\n next_node = node_src.node(first_element);\\n if (next_node) {\\n decomposed_path?.add_node(first_element, next_node);\\n }\\n }\\n if (next_node != null && elements.length > 1) {\\n const remainder = elements.slice(1).join(CoreWalker2.SEPARATOR);\\n next_node = this.findNode(next_node, remainder, decomposed_path);\\n }\\n return next_node;\\n }\\n return next_node;\\n }\\n static findParam(node_src, path, decomposed_path) {\\n if (!node_src) {\\n return null;\\n }\\n const elements = path.split(CoreWalker2.SEPARATOR);\\n if (elements.length === 1) {\\n return node_src.params.get(elements[0]);\\n } else {\\n const node_path = elements.slice(0, +(elements.length - 2) + 1 || void 0).join(CoreWalker2.SEPARATOR);\\n const node = this.findNode(node_src, node_path, decomposed_path);\\n if (node != null) {\\n const param_name = elements[elements.length - 1];\\n const param = node.params.get(param_name);\\n if (decomposed_path && param) {\\n decomposed_path.add_node(param_name, param);\\n }\\n return param;\\n } else {\\n return null;\\n }\\n }\\n }\\n static relativePath(src_graph_node, dest_graph_node) {\\n const parent = this.closestCommonParent(src_graph_node, dest_graph_node);\\n if (!parent) {\\n return dest_graph_node.path();\\n } else {\\n const distance = this.distanceToParent(src_graph_node, parent);\\n let up = \\\\\\\"\\\\\\\";\\n if (distance > 0) {\\n let i = 0;\\n const ups = [];\\n while (i++ < distance) {\\n ups.push(CoreWalker2.PARENT);\\n }\\n up = ups.join(CoreWalker2.SEPARATOR) + CoreWalker2.SEPARATOR;\\n }\\n const parent_path_elements = parent.path().split(CoreWalker2.SEPARATOR).filter((e) => e.length > 0);\\n const dest_path_elements = dest_graph_node.path().split(CoreWalker2.SEPARATOR).filter((e) => e.length > 0);\\n const remaining_elements = [];\\n let cmptr = 0;\\n for (let dest_path_element of dest_path_elements) {\\n if (!parent_path_elements[cmptr]) {\\n remaining_elements.push(dest_path_element);\\n }\\n cmptr++;\\n }\\n const down = remaining_elements.join(CoreWalker2.SEPARATOR);\\n return `${up}${down}`;\\n }\\n }\\n static closestCommonParent(graph_node1, graph_node2) {\\n const parents1 = this.parents(graph_node1).reverse().concat([graph_node1]);\\n const parents2 = this.parents(graph_node2).reverse().concat([graph_node2]);\\n const min_depth = Math.min(parents1.length, parents2.length);\\n let found_parent = null;\\n for (let i = 0; i < min_depth; i++) {\\n if (parents1[i].graphNodeId() == parents2[i].graphNodeId()) {\\n found_parent = parents1[i];\\n }\\n }\\n return found_parent;\\n }\\n static parents(graph_node) {\\n const parents = [];\\n let parent = graph_node.parent();\\n while (parent) {\\n parents.push(parent);\\n parent = parent.parent();\\n }\\n return parents;\\n }\\n static distanceToParent(graph_node, dest) {\\n let distance = 0;\\n let current = graph_node;\\n const dest_id = dest.graphNodeId();\\n while (current && current.graphNodeId() != dest_id) {\\n distance += 1;\\n current = current.parent();\\n }\\n if (current && current.graphNodeId() == dest_id) {\\n return distance;\\n } else {\\n return -1;\\n }\\n }\\n static makeAbsolutePath(nodeSrc, path) {\\n if (path[0] == CoreWalker2.SEPARATOR) {\\n return path;\\n }\\n const pathElements = path.split(CoreWalker2.SEPARATOR);\\n const firstElement = pathElements.shift();\\n if (firstElement) {\\n switch (firstElement) {\\n case \\\\\\\"..\\\\\\\": {\\n const parent = nodeSrc.parent();\\n if (parent) {\\n if (parent == nodeSrc.scene().root()) {\\n return CoreWalker2.SEPARATOR + pathElements.join(CoreWalker2.SEPARATOR);\\n } else {\\n return this.makeAbsolutePath(parent, pathElements.join(CoreWalker2.SEPARATOR));\\n }\\n } else {\\n return null;\\n }\\n }\\n case \\\\\\\".\\\\\\\": {\\n return this.makeAbsolutePath(nodeSrc, pathElements.join(CoreWalker2.SEPARATOR));\\n }\\n default: {\\n return [nodeSrc.path(), path].join(CoreWalker2.SEPARATOR);\\n }\\n }\\n } else {\\n return nodeSrc.path();\\n }\\n }\\n};\\nvar CoreWalker = CoreWalker2;\\nCoreWalker.SEPARATOR = \\\\\\\"/\\\\\\\";\\nCoreWalker.DOT = \\\\\\\".\\\\\\\";\\nCoreWalker.CURRENT = CoreWalker2.DOT;\\nCoreWalker.PARENT = \\\\\\\"..\\\\\\\";\\nCoreWalker.CURRENT_WITH_SLASH = `${CoreWalker2.CURRENT}/`;\\nCoreWalker.PARENT_WITH_SLASH = `${CoreWalker2.PARENT}/`;\\nCoreWalker.NON_LETTER_PREFIXES = [CoreWalker2.SEPARATOR, CoreWalker2.DOT];\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/hierarchy/ParentController.ts\\nvar HierarchyParentController = class {\\n constructor(node) {\\n this.node = node;\\n this._parent = null;\\n }\\n parent() {\\n return this._parent;\\n }\\n setParent(parent) {\\n if (parent != this.node.parentController.parent()) {\\n this._parent = parent;\\n if (this._parent) {\\n this.node.nameController.request_name_to_parent(NameController.base_name(this.node));\\n }\\n }\\n }\\n firstAncestorWithContext(context) {\\n if (this._parent) {\\n if (this._parent.context() == context) {\\n return this._parent;\\n } else {\\n return this._parent.parentController.firstAncestorWithContext(context);\\n }\\n }\\n return null;\\n }\\n path(relative_to_parent) {\\n const separator = CoreWalker.SEPARATOR;\\n if (this._parent != null) {\\n if (this._parent == relative_to_parent) {\\n return this.node.name();\\n } else {\\n const parent_fullPath = this._parent.path(relative_to_parent);\\n if (parent_fullPath === separator) {\\n return parent_fullPath + this.node.name();\\n } else {\\n return parent_fullPath + separator + this.node.name();\\n }\\n }\\n } else {\\n return separator;\\n }\\n }\\n onSetParent() {\\n if (this._on_set_parent_hooks) {\\n for (let hook of this._on_set_parent_hooks) {\\n hook();\\n }\\n }\\n }\\n findNode(path) {\\n if (path == null) {\\n return null;\\n }\\n if (path == CoreWalker.CURRENT || path == CoreWalker.CURRENT_WITH_SLASH) {\\n return this.node;\\n }\\n if (path == CoreWalker.PARENT || path == CoreWalker.PARENT_WITH_SLASH) {\\n return this.node.parent();\\n }\\n const separator = CoreWalker.SEPARATOR;\\n if (path === separator) {\\n return this.node.scene().root();\\n }\\n if (path[0] === separator) {\\n path = path.substring(1, path.length);\\n return this.node.scene().root().node(path);\\n }\\n if (path.split) {\\n const elements = path.split(separator);\\n if (elements.length === 1) {\\n const name = elements[0];\\n if (this.node.childrenController) {\\n return this.node.childrenController.child_by_name(name);\\n } else {\\n return null;\\n }\\n } else {\\n return CoreWalker.findNode(this.node, path);\\n }\\n } else {\\n console.error(\\\\\\\"unexpected path given:\\\\\\\", path);\\n return null;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/MapUtils.ts\\nvar MapUtils = class {\\n static pushOnArrayAtEntry(map, key, new_element) {\\n let has_entry = map.has(key);\\n if (has_entry) {\\n map.get(key).push(new_element);\\n } else {\\n map.set(key, [new_element]);\\n }\\n }\\n static popFromArrayAtEntry(map, key, element_to_remove) {\\n let has_entry = map.has(key);\\n if (has_entry) {\\n const array = map.get(key);\\n const index = array.indexOf(element_to_remove);\\n if (index >= 0) {\\n array.splice(index, 1);\\n }\\n }\\n }\\n static unshiftOnArrayAtEntry(map, key, new_element) {\\n let has_entry = map.has(key);\\n if (has_entry) {\\n map.get(key).unshift(new_element);\\n } else {\\n map.set(key, [new_element]);\\n }\\n }\\n static concatOnArrayAtEntry(map, key, new_elements) {\\n let has_entry = map.has(key);\\n if (has_entry) {\\n let array = map.get(key);\\n for (let element of new_elements) {\\n array.push(element);\\n }\\n } else {\\n map.set(key, new_elements);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/SetUtils.ts\\nvar SetUtils = class {\\n static union(set0, set1) {\\n const newSet = new Set();\\n set0.forEach((val) => newSet.add(val));\\n set1.forEach((val) => newSet.add(val));\\n return newSet;\\n }\\n static intersection(set0, set1) {\\n const newSet = new Set();\\n set0.forEach((val) => {\\n if (set1.has(val)) {\\n newSet.add(val);\\n }\\n });\\n set1.forEach((val) => {\\n if (set0.has(val)) {\\n newSet.add(val);\\n }\\n });\\n return newSet;\\n }\\n static difference(set0, set1) {\\n const newSet = new Set();\\n set0.forEach((val) => {\\n if (!set1.has(val)) {\\n newSet.add(val);\\n }\\n });\\n set1.forEach((val) => {\\n if (!set0.has(val)) {\\n newSet.add(val);\\n }\\n });\\n return newSet;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/ArrayUtils.ts\\nvar ArrayUtils = class {\\n static shallowClone(array) {\\n return [...array];\\n }\\n static min(array) {\\n let min = array[0];\\n for (let element of array) {\\n if (element < min) {\\n min = element;\\n }\\n }\\n return min;\\n }\\n static max(array) {\\n let min = array[0];\\n for (let element of array) {\\n if (element > min) {\\n min = element;\\n }\\n }\\n return min;\\n }\\n static sum(array) {\\n let sum = 0;\\n for (let element of array) {\\n sum += element;\\n }\\n return sum;\\n }\\n static compact(array) {\\n const newArray = [];\\n for (let elem of array) {\\n if (elem != null) {\\n newArray.push(elem);\\n }\\n }\\n return newArray;\\n }\\n static uniq(array) {\\n const tmpSet = new Set();\\n for (let elem of array) {\\n tmpSet.add(elem);\\n }\\n const newArray = new Array(tmpSet.size);\\n let i = 0;\\n tmpSet.forEach((elem) => {\\n newArray[i] = elem;\\n i++;\\n });\\n return newArray;\\n }\\n static chunk(array, chunkSize) {\\n const newArray = [];\\n let newSubArray = [];\\n newArray.push(newSubArray);\\n for (let i = 0; i < array.length; i++) {\\n if (newSubArray.length == chunkSize) {\\n newSubArray = [];\\n newArray.push(newSubArray);\\n }\\n newSubArray.push(array[i]);\\n }\\n return newArray;\\n }\\n static union(array0, array1) {\\n const newArray = [];\\n const unionSet = SetUtils.union(this.toSet(array0), this.toSet(array1));\\n unionSet.forEach((val) => newArray.push(val));\\n return newArray;\\n }\\n static intersection(array0, array1) {\\n const newArray = [];\\n const intersectionSet = SetUtils.intersection(this.toSet(array0), this.toSet(array1));\\n intersectionSet.forEach((val) => newArray.push(val));\\n return newArray;\\n }\\n static difference(array0, array1) {\\n const newArray = [];\\n const differenceSet = SetUtils.difference(this.toSet(array0), this.toSet(array1));\\n differenceSet.forEach((val) => newArray.push(val));\\n return newArray;\\n }\\n static toSet(array) {\\n const set = new Set();\\n for (let elem of array) {\\n set.add(elem);\\n }\\n return set;\\n }\\n static isEqual(array0, array1) {\\n if (array0.length != array1.length) {\\n return false;\\n }\\n const count = array0.length;\\n for (let i = 0; i < count; i++) {\\n if (array0[i] != array1[i]) {\\n return false;\\n }\\n }\\n return true;\\n }\\n static sortBy(array, callback) {\\n if (array.length == 0) {\\n return [];\\n }\\n const elementsByValue = new Map();\\n const valuesSet = new Set();\\n for (let elem of array) {\\n const value = callback(elem);\\n valuesSet.add(value);\\n MapUtils.pushOnArrayAtEntry(elementsByValue, value, elem);\\n }\\n const values = new Array(valuesSet.size);\\n let i = 0;\\n valuesSet.forEach((value) => {\\n values[i] = value;\\n i++;\\n });\\n if (CoreType.isString(values[0])) {\\n values.sort();\\n } else {\\n values.sort((a, b) => a - b);\\n }\\n const sorted_elements = new Array(array.length);\\n i = 0;\\n for (let value of values) {\\n const elements_for_value = elementsByValue.get(value);\\n if (elements_for_value) {\\n for (let element of elements_for_value) {\\n sorted_elements[i] = element;\\n i++;\\n }\\n }\\n }\\n return sorted_elements;\\n }\\n static range(start, end, step = 1) {\\n if (end == null) {\\n end = start;\\n start = 0;\\n }\\n const length = Math.floor((end - start) / step);\\n const array = new Array(length);\\n for (let i = 0; i < array.length; i++) {\\n array[i] = start + i * step;\\n }\\n return array;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/String.ts\\nvar ATTRIB_NAMES_SEPARATOR = /[, ]/;\\nvar TAIL_DIGIT_MATCH_REGEXP = /\\\\d+$/;\\nvar LEADING_ZEROS_MATCH_REGEXP = /^0+/;\\nvar INDICES_LIST_SEPARATOR = /,| /;\\nvar ZERO = \\\\\\\"0\\\\\\\";\\nvar SPACE = \\\\\\\" \\\\\\\";\\nvar NUM_REGEXP = /^-?\\\\d+\\\\.?\\\\d*$/;\\nvar BooleanString;\\n(function(BooleanString2) {\\n BooleanString2[\\\\\\\"TRUE\\\\\\\"] = \\\\\\\"true\\\\\\\";\\n BooleanString2[\\\\\\\"FALSE\\\\\\\"] = \\\\\\\"false\\\\\\\";\\n})(BooleanString || (BooleanString = {}));\\nvar CoreString = class {\\n static isBoolean(word) {\\n return word == BooleanString.TRUE || word == BooleanString.FALSE;\\n }\\n static toBoolean(word) {\\n return word == BooleanString.TRUE;\\n }\\n static isNumber(word) {\\n return NUM_REGEXP.test(word);\\n }\\n static tailDigits(word) {\\n const match = word.match(TAIL_DIGIT_MATCH_REGEXP);\\n if (match) {\\n return parseInt(match[0]);\\n } else {\\n return 0;\\n }\\n }\\n static increment(word) {\\n const match = word.match(TAIL_DIGIT_MATCH_REGEXP);\\n if (match) {\\n let numbers_as_str = match[0];\\n let zeros_prefix = \\\\\\\"\\\\\\\";\\n const leading_zeros_match = numbers_as_str.match(LEADING_ZEROS_MATCH_REGEXP);\\n if (leading_zeros_match) {\\n zeros_prefix = leading_zeros_match[0];\\n }\\n const digits = parseInt(numbers_as_str);\\n if (digits == 0) {\\n if (zeros_prefix.length > 0) {\\n if (zeros_prefix[zeros_prefix.length - 1] == ZERO) {\\n zeros_prefix = zeros_prefix.slice(0, -1);\\n }\\n }\\n }\\n const prefix = word.substring(0, word.length - match[0].length);\\n return `${prefix}${zeros_prefix}${digits + 1}`;\\n } else {\\n return `${word}1`;\\n }\\n }\\n static pluralize(word) {\\n const last_char = word[word.length - 1];\\n if (last_char !== \\\\\\\"s\\\\\\\") {\\n return `${word}s`;\\n } else {\\n return word;\\n }\\n }\\n static camelCase(str) {\\n const elements = str.replace(/_/g, \\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");\\n let newWord = \\\\\\\"\\\\\\\";\\n for (let i = 0; i < elements.length; i++) {\\n let element = elements[i].toLowerCase();\\n if (i > 0) {\\n element = this.upperFirst(element);\\n }\\n newWord += element;\\n }\\n return newWord;\\n }\\n static upperFirst(word) {\\n const newString = word[0].toUpperCase() + word.substr(1);\\n return newString;\\n }\\n static titleize(word) {\\n const elements = word.split(/\\\\s|_/g);\\n const newElements = elements.map((elem) => this.upperFirst(elem));\\n return newElements.join(\\\\\\\" \\\\\\\");\\n }\\n static precision(val, decimals = 2) {\\n decimals = Math.max(decimals, 0);\\n const elements = `${val}`.split(\\\\\\\".\\\\\\\");\\n if (decimals <= 0) {\\n return elements[0];\\n }\\n let frac = elements[1];\\n if (frac !== void 0) {\\n if (frac.length > decimals) {\\n frac = frac.substring(0, decimals);\\n }\\n frac = frac.padEnd(decimals, \\\\\\\"0\\\\\\\");\\n return `${elements[0]}.${frac}`;\\n } else {\\n const string_to_pad = `${val}.`;\\n const pad = string_to_pad.length + decimals;\\n return string_to_pad.padEnd(pad, \\\\\\\"0\\\\\\\");\\n }\\n }\\n static ensureFloat(num) {\\n const num_as_string = `${num}`;\\n const dot_pos = num_as_string.indexOf(\\\\\\\".\\\\\\\");\\n if (dot_pos >= 0) {\\n return num_as_string;\\n } else {\\n return `${num_as_string}.0`;\\n }\\n }\\n static ensureInteger(num) {\\n const num_as_string = `${num}`;\\n const dot_pos = num_as_string.indexOf(\\\\\\\".\\\\\\\");\\n if (dot_pos >= 0) {\\n return num_as_string.split(\\\\\\\".\\\\\\\")[0];\\n } else {\\n return num_as_string;\\n }\\n }\\n static matchMask(word, mask) {\\n if (mask === \\\\\\\"*\\\\\\\") {\\n return true;\\n }\\n if (word == mask) {\\n return true;\\n }\\n const elements = mask.split(SPACE);\\n if (elements.length > 1) {\\n for (let element of elements) {\\n const match = this.matchMask(word, element);\\n if (match) {\\n return true;\\n }\\n }\\n return false;\\n }\\n mask = mask.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\");\\n mask = `^${mask}$`;\\n const regex = new RegExp(mask);\\n return regex.test(word);\\n }\\n static matchesOneMask(word, masks) {\\n let matches_one_mask = false;\\n for (let mask of masks) {\\n if (CoreString.matchMask(word, mask)) {\\n matches_one_mask = true;\\n }\\n }\\n return matches_one_mask;\\n }\\n static attribNames(word) {\\n const elements = word.split(ATTRIB_NAMES_SEPARATOR);\\n const names_set = new Set();\\n for (let element of elements) {\\n element = element.trim();\\n if (element.length > 0) {\\n names_set.add(element);\\n }\\n }\\n const names = new Array(names_set.size);\\n let i = 0;\\n names_set.forEach((name) => {\\n names[i] = name;\\n i++;\\n });\\n return names;\\n }\\n static indices(indices_string) {\\n const elements = indices_string.split(INDICES_LIST_SEPARATOR);\\n if (elements.length > 1) {\\n const indices = elements.flatMap((element) => this.indices(element));\\n return ArrayUtils.uniq(indices).sort((a, b) => a - b);\\n } else {\\n const element = elements[0];\\n if (element) {\\n const range_separator = \\\\\\\"-\\\\\\\";\\n if (element.indexOf(range_separator) > 0) {\\n const range_elements = element.split(range_separator);\\n return ArrayUtils.range(parseInt(range_elements[0]), parseInt(range_elements[1]) + 1);\\n } else {\\n const parsed = parseInt(element);\\n if (CoreType.isNumber(parsed)) {\\n return [parsed];\\n } else {\\n return [];\\n }\\n }\\n } else {\\n return [];\\n }\\n }\\n }\\n static escapeLineBreaks(word) {\\n return word.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm, \\\\\\\"\\\\\\\\n\\\\\\\");\\n }\\n static sanitizeName(name) {\\n name = name.replace(/[^A-Za-z0-9]/g, \\\\\\\"_\\\\\\\");\\n name = name.replace(/^[0-9]/, \\\\\\\"_\\\\\\\");\\n return name;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/NodeSelection.ts\\nvar CoreNodeSelection = class {\\n constructor(_node) {\\n this._node = _node;\\n this._node_ids = [];\\n this._json = [];\\n }\\n node() {\\n return this._node;\\n }\\n nodes() {\\n return this._node.scene().graph.nodesFromIds(this._node_ids);\\n }\\n contains(node) {\\n return this._node_ids.includes(node.graphNodeId());\\n }\\n equals(nodes) {\\n const node_ids = nodes.map((node) => node.graphNodeId()).sort();\\n return ArrayUtils.isEqual(node_ids, this._node_ids);\\n }\\n clear() {\\n this._node_ids = [];\\n this.send_update_event();\\n }\\n set(nodes) {\\n this._node_ids = [];\\n this.add(nodes);\\n }\\n add(nodes_to_add) {\\n const node_ids_to_add = nodes_to_add.map((node) => node.graphNodeId());\\n this._node_ids = ArrayUtils.union(this._node_ids, node_ids_to_add);\\n this.send_update_event();\\n }\\n remove(nodes_to_remove) {\\n const node_ids_to_remove = nodes_to_remove.map((node) => node.graphNodeId());\\n this._node_ids = ArrayUtils.difference(this._node_ids, node_ids_to_remove);\\n this.send_update_event();\\n }\\n send_update_event() {\\n this._node.emit(NodeEvent.SELECTION_UPDATED);\\n }\\n toJSON() {\\n this._json = this._json || [];\\n this._json = this._node_ids.map((id) => id);\\n return this._json;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/InputCloneMode.ts\\nvar InputCloneMode;\\n(function(InputCloneMode2) {\\n InputCloneMode2[\\\\\\\"ALWAYS\\\\\\\"] = \\\\\\\"always\\\\\\\";\\n InputCloneMode2[\\\\\\\"NEVER\\\\\\\"] = \\\\\\\"never\\\\\\\";\\n InputCloneMode2[\\\\\\\"FROM_NODE\\\\\\\"] = \\\\\\\"from_node\\\\\\\";\\n})(InputCloneMode || (InputCloneMode = {}));\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/Assert.ts\\nvar TypeAssert = class {\\n static unreachable(x) {\\n throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\");\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/container/utils/inputs/ClonedStatesController.ts\\nvar ClonedStatesController = class {\\n constructor(inputs_controller) {\\n this.inputs_controller = inputs_controller;\\n this._clone_required_states = [];\\n this._overridden = false;\\n }\\n init_inputs_cloned_state(states) {\\n if (CoreType.isArray(states)) {\\n this._cloned_states = states;\\n } else {\\n this._cloned_state = states;\\n }\\n this._update_clone_required_state();\\n }\\n override_cloned_state_allowed() {\\n if (this._cloned_states) {\\n for (let state of this._cloned_states) {\\n if (state == InputCloneMode.FROM_NODE) {\\n return true;\\n }\\n }\\n }\\n if (this._cloned_state) {\\n return this._cloned_state == InputCloneMode.FROM_NODE;\\n }\\n return false;\\n }\\n clone_required_state(index) {\\n return this._clone_required_states[index];\\n }\\n clone_required_states() {\\n return this._clone_required_states;\\n }\\n _get_clone_required_state(index) {\\n const states = this._cloned_states;\\n if (states) {\\n const state = states[index];\\n if (state != null) {\\n return this.clone_required_from_state(state);\\n }\\n }\\n if (this._cloned_state) {\\n return this.clone_required_from_state(this._cloned_state);\\n }\\n return true;\\n }\\n clone_required_from_state(state) {\\n switch (state) {\\n case InputCloneMode.ALWAYS:\\n return true;\\n case InputCloneMode.NEVER:\\n return false;\\n case InputCloneMode.FROM_NODE:\\n return !this._overridden;\\n }\\n return TypeAssert.unreachable(state);\\n }\\n override_cloned_state(state) {\\n this._overridden = state;\\n this._update_clone_required_state();\\n }\\n overriden() {\\n return this._overridden;\\n }\\n _update_clone_required_state() {\\n if (this._cloned_states) {\\n const states = [];\\n for (let i = 0; i < this._cloned_states.length; i++) {\\n states[i] = this._get_clone_required_state(i);\\n }\\n this._clone_required_states = states;\\n return;\\n }\\n if (this._cloned_state) {\\n const max_inputs = this.inputs_controller.inputs_count();\\n const states = [];\\n for (let i = 0; i < max_inputs; i++) {\\n states[i] = this._get_clone_required_state(i);\\n }\\n this._clone_required_states = states;\\n return;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/container/utils/InputsController.ts\\nvar InputsController = class {\\n constructor(operation_container) {\\n this.operation_container = operation_container;\\n }\\n inputs_count() {\\n return this.operation_container.inputs_count();\\n }\\n init_inputs_cloned_state(states) {\\n if (!this._cloned_states_controller) {\\n this._cloned_states_controller = new ClonedStatesController(this);\\n this._cloned_states_controller.init_inputs_cloned_state(states);\\n }\\n }\\n clone_required(index) {\\n const state = this._cloned_states_controller?.clone_required_state(index);\\n if (state != null) {\\n return state;\\n }\\n return true;\\n }\\n override_cloned_state(state) {\\n this._cloned_states_controller?.override_cloned_state(state);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/container/_Base.ts\\nvar BaseOperationContainer = class {\\n constructor(operation, name, init_params) {\\n this.operation = operation;\\n this.name = name;\\n this.params = {};\\n this._apply_default_params();\\n this._apply_init_params(init_params);\\n this._init_cloned_states();\\n }\\n path_param_resolve_required() {\\n return this._path_params != null;\\n }\\n resolve_path_params(node_start) {\\n if (!this._path_params) {\\n return;\\n }\\n for (let path_param of this._path_params) {\\n path_param.resolve(node_start);\\n }\\n }\\n _apply_default_params() {\\n const default_params = this.operation.constructor.DEFAULT_PARAMS;\\n const param_names = Object.keys(default_params);\\n for (let param_name of param_names) {\\n const param_data = default_params[param_name];\\n const clone_param_data = this._convert_param_data(param_name, param_data);\\n if (clone_param_data != void 0) {\\n this.params[param_name] = clone_param_data;\\n }\\n }\\n }\\n _apply_init_params(init_params) {\\n const param_names = Object.keys(init_params);\\n for (let param_name of param_names) {\\n const param_data = init_params[param_name];\\n if (param_data.simple_data != null) {\\n const simple_data = param_data.simple_data;\\n const clone_param_data = this._convert_export_param_data(param_name, simple_data);\\n if (clone_param_data != void 0) {\\n this.params[param_name] = clone_param_data;\\n }\\n }\\n }\\n }\\n _convert_param_data(param_name, param_data) {\\n if (CoreType.isNumber(param_data) || CoreType.isBoolean(param_data) || CoreType.isString(param_data)) {\\n return param_data;\\n }\\n if (param_data instanceof TypedNodePathParamValue) {\\n const cloned = param_data.clone();\\n if (!this._path_params) {\\n this._path_params = [];\\n }\\n this._path_params.push(cloned);\\n return cloned;\\n }\\n if (param_data instanceof Color || param_data instanceof Vector2 || param_data instanceof Vector3 || param_data instanceof Vector4) {\\n return param_data.clone();\\n }\\n }\\n _convert_export_param_data(param_name, param_data) {\\n const default_param = this.params[param_name];\\n if (CoreType.isBoolean(param_data)) {\\n return param_data;\\n }\\n if (CoreType.isNumber(param_data)) {\\n if (CoreType.isBoolean(default_param)) {\\n return param_data >= 1 ? true : false;\\n } else {\\n return param_data;\\n }\\n }\\n if (CoreType.isString(param_data)) {\\n if (default_param) {\\n if (default_param instanceof TypedNodePathParamValue) {\\n return default_param.set_path(param_data);\\n }\\n if (default_param instanceof TypedParamPathParamValue) {\\n return default_param.set_path(param_data);\\n }\\n }\\n return param_data;\\n }\\n if (CoreType.isArray(param_data)) {\\n this.params[param_name].fromArray(param_data);\\n }\\n }\\n setInput(index, input) {\\n this._inputs = this._inputs || [];\\n this._inputs[index] = input;\\n }\\n inputs_count() {\\n if (this._inputs) {\\n return this._inputs.length;\\n } else {\\n return 0;\\n }\\n }\\n inputsController() {\\n return this._inputs_controller = this._inputs_controller || new InputsController(this);\\n }\\n _init_cloned_states() {\\n const default_cloned_states = this.operation.constructor.INPUT_CLONED_STATE;\\n this.inputsController().init_inputs_cloned_state(default_cloned_states);\\n }\\n input_clone_required(index) {\\n if (!this._inputs_controller) {\\n return true;\\n }\\n return this._inputs_controller.clone_required(index);\\n }\\n override_input_clone_state(state) {\\n this.inputsController().override_cloned_state(state);\\n }\\n cook(input_contents) {\\n return this.operation.cook(input_contents, this.params);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/container/sop.ts\\nvar SopOperationContainer = class extends BaseOperationContainer {\\n constructor(operation, name, init_params) {\\n super(operation, name, init_params);\\n this.operation = operation;\\n this.name = name;\\n this.init_params = init_params;\\n this._inputs = [];\\n this._current_input_index = 0;\\n this._dirty = true;\\n }\\n add_input(input) {\\n super.setInput(this._current_input_index, input);\\n this.increment_input_index();\\n }\\n increment_input_index() {\\n this._current_input_index++;\\n }\\n current_input_index() {\\n return this._current_input_index;\\n }\\n setDirty() {\\n if (this._dirty) {\\n return;\\n }\\n this._compute_result = void 0;\\n for (let i = 0; i < this._inputs.length; i++) {\\n const input_operation = this._inputs[i];\\n input_operation.setDirty();\\n }\\n }\\n async compute(input_contents, operation_inputs_map) {\\n if (this._compute_result) {\\n return this._compute_result;\\n }\\n const operation_input_contents = [];\\n const node_inputs_map = operation_inputs_map.get(this);\\n if (node_inputs_map) {\\n node_inputs_map.forEach((node_input_index, operation_input_index) => {\\n operation_input_contents[operation_input_index] = input_contents[node_input_index];\\n });\\n }\\n for (let i = 0; i < this._inputs.length; i++) {\\n const input_operation = this._inputs[i];\\n let result2 = await input_operation.compute(input_contents, operation_inputs_map);\\n if (result2) {\\n if (this.input_clone_required(i)) {\\n result2 = result2.clone();\\n }\\n operation_input_contents[i] = result2;\\n }\\n }\\n const result = this.operation.cook(operation_input_contents, this.params);\\n if (result) {\\n if (result instanceof Promise) {\\n this._compute_result = await result;\\n } else {\\n this._compute_result = result;\\n }\\n } else {\\n this._compute_result = void 0;\\n }\\n this._dirty = false;\\n return this._compute_result;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/hierarchy/ChildrenController.ts\\nvar HierarchyChildrenController = class {\\n constructor(node, _context2) {\\n this.node = node;\\n this._context = _context2;\\n this._children = {};\\n this._children_by_type = {};\\n this._children_and_grandchildren_by_context = {};\\n }\\n get selection() {\\n return this._selection = this._selection || new CoreNodeSelection(this.node);\\n }\\n dispose() {\\n const children = this.children();\\n for (let child of children) {\\n this.node.removeNode(child);\\n }\\n this._selection = void 0;\\n }\\n get context() {\\n return this._context;\\n }\\n set_output_node_find_method(method) {\\n this._output_node_find_method = method;\\n }\\n output_node() {\\n if (this._output_node_find_method) {\\n return this._output_node_find_method();\\n }\\n }\\n set_child_name(node, new_name) {\\n let current_child_with_name;\\n new_name = CoreString.sanitizeName(new_name);\\n if ((current_child_with_name = this._children[new_name]) != null) {\\n if (node.name() === new_name && current_child_with_name.graphNodeId() === node.graphNodeId()) {\\n return;\\n }\\n new_name = CoreString.increment(new_name);\\n return this.set_child_name(node, new_name);\\n } else {\\n const current_name = node.name();\\n const current_child = this._children[current_name];\\n if (current_child) {\\n delete this._children[current_name];\\n }\\n this._children[new_name] = node;\\n node.nameController.update_name_from_parent(new_name);\\n this._add_to_nodesByType(node);\\n this.node.scene().nodesController.addToInstanciatedNode(node);\\n }\\n }\\n node_context_signature() {\\n return `${this.node.context()}/${this.node.type()}`;\\n }\\n available_children_classes() {\\n return Poly.registeredNodes(this._context, this.node.type());\\n }\\n is_valid_child_type(node_type) {\\n const node_class = this.available_children_classes()[node_type];\\n return node_class != null;\\n }\\n createNode(node_class_or_string, params_init_value_overrides, node_type = \\\\\\\"\\\\\\\") {\\n if (typeof node_class_or_string == \\\\\\\"string\\\\\\\") {\\n const node_class = this._find_node_class(node_class_or_string);\\n return this._create_and_init_node(node_class, params_init_value_overrides, node_type);\\n } else {\\n return this._create_and_init_node(node_class_or_string, params_init_value_overrides, node_type);\\n }\\n }\\n _create_and_init_node(node_class, params_init_value_overrides, node_type = \\\\\\\"\\\\\\\") {\\n const child_node = new node_class(this.node.scene(), `child_node_${node_type}`, params_init_value_overrides);\\n child_node.initialize_base_and_node();\\n this.add_node(child_node);\\n child_node.lifecycle.setCreationCompleted();\\n return child_node;\\n }\\n _find_node_class(node_type) {\\n const node_class = this.available_children_classes()[node_type.toLowerCase()];\\n if (node_class == null) {\\n const message = `child node type '${node_type}' not found for node '${this.node.path()}'. Available types are: ${Object.keys(this.available_children_classes()).join(\\\\\\\", \\\\\\\")}, ${this._context}, ${this.node.type()}`;\\n console.error(message);\\n throw message;\\n }\\n return node_class;\\n }\\n create_operation_container(operation_type, operation_container_name, params_init_value_overrides) {\\n const operation_class = Poly.registeredOperation(this._context, operation_type);\\n if (operation_class == null) {\\n const message = `no operation found with context ${this._context}/${operation_type}`;\\n console.error(message);\\n throw message;\\n } else {\\n const operation = new operation_class(this.node.scene());\\n const operation_container = new SopOperationContainer(operation, operation_container_name, params_init_value_overrides || {});\\n return operation_container;\\n }\\n }\\n add_node(child_node) {\\n child_node.setParent(this.node);\\n child_node.params.init();\\n child_node.parentController.onSetParent();\\n child_node.nameController.run_post_set_fullPath_hooks();\\n if (child_node.childrenAllowed() && child_node.childrenController) {\\n for (let child of child_node.childrenController.children()) {\\n child.nameController.run_post_set_fullPath_hooks();\\n }\\n }\\n this.node.emit(NodeEvent.CREATED, {child_node_json: child_node.toJSON()});\\n if (this.node.scene().lifecycleController.onCreateHookAllowed()) {\\n child_node.lifecycle.run_on_create_hooks();\\n }\\n child_node.lifecycle.run_on_add_hooks();\\n this.set_child_name(child_node, NameController.base_name(child_node));\\n this.node.lifecycle.run_on_child_add_hooks(child_node);\\n if (child_node.require_webgl2()) {\\n this.node.scene().webgl_controller.set_require_webgl2();\\n }\\n this.node.scene().missingExpressionReferencesController.checkForMissingReferences(child_node);\\n return child_node;\\n }\\n removeNode(child_node) {\\n if (child_node.parent() != this.node) {\\n return console.warn(`node ${child_node.name()} not under parent ${this.node.path()}`);\\n } else {\\n if (this.selection.contains(child_node)) {\\n this.selection.remove([child_node]);\\n }\\n const first_connection = child_node.io.connections.firstInputConnection();\\n const input_connections = child_node.io.connections.inputConnections();\\n const output_connections = child_node.io.connections.outputConnections();\\n if (input_connections) {\\n for (let input_connection of input_connections) {\\n if (input_connection) {\\n input_connection.disconnect({setInput: true});\\n }\\n }\\n }\\n if (output_connections) {\\n for (let output_connection of output_connections) {\\n if (output_connection) {\\n output_connection.disconnect({setInput: true});\\n if (first_connection) {\\n const old_src = first_connection.node_src;\\n const old_output_index = output_connection.output_index;\\n const old_dest = output_connection.node_dest;\\n const old_input_index = output_connection.input_index;\\n old_dest.io.inputs.setInput(old_input_index, old_src, old_output_index);\\n }\\n }\\n }\\n }\\n child_node.setParent(null);\\n delete this._children[child_node.name()];\\n this._remove_from_nodesByType(child_node);\\n this.node.scene().nodesController.removeFromInstanciatedNode(child_node);\\n child_node.setSuccessorsDirty(this.node);\\n child_node.graphDisconnectSuccessors();\\n this.node.lifecycle.run_on_child_remove_hooks(child_node);\\n child_node.lifecycle.run_on_delete_hooks();\\n child_node.dispose();\\n child_node.emit(NodeEvent.DELETED, {parent_id: this.node.graphNodeId()});\\n }\\n }\\n _add_to_nodesByType(node) {\\n const node_id = node.graphNodeId();\\n const type = node.type();\\n this._children_by_type[type] = this._children_by_type[type] || [];\\n if (!this._children_by_type[type].includes(node_id)) {\\n this._children_by_type[type].push(node_id);\\n }\\n this.add_to_children_and_grandchildren_by_context(node);\\n }\\n _remove_from_nodesByType(node) {\\n const node_id = node.graphNodeId();\\n const type = node.type();\\n if (this._children_by_type[type]) {\\n const index = this._children_by_type[type].indexOf(node_id);\\n if (index >= 0) {\\n this._children_by_type[type].splice(index, 1);\\n if (this._children_by_type[type].length == 0) {\\n delete this._children_by_type[type];\\n }\\n }\\n }\\n this.remove_from_children_and_grandchildren_by_context(node);\\n }\\n add_to_children_and_grandchildren_by_context(node) {\\n const node_id = node.graphNodeId();\\n const type = node.context();\\n this._children_and_grandchildren_by_context[type] = this._children_and_grandchildren_by_context[type] || [];\\n if (!this._children_and_grandchildren_by_context[type].includes(node_id)) {\\n this._children_and_grandchildren_by_context[type].push(node_id);\\n }\\n const parent = this.node.parent();\\n if (parent && parent.childrenAllowed()) {\\n parent.childrenController?.add_to_children_and_grandchildren_by_context(node);\\n }\\n }\\n remove_from_children_and_grandchildren_by_context(node) {\\n const node_id = node.graphNodeId();\\n const type = node.context();\\n if (this._children_and_grandchildren_by_context[type]) {\\n const index = this._children_and_grandchildren_by_context[type].indexOf(node_id);\\n if (index >= 0) {\\n this._children_and_grandchildren_by_context[type].splice(index, 1);\\n if (this._children_and_grandchildren_by_context[type].length == 0) {\\n delete this._children_and_grandchildren_by_context[type];\\n }\\n }\\n }\\n const parent = this.node.parent();\\n if (parent && parent.childrenAllowed()) {\\n parent.childrenController?.remove_from_children_and_grandchildren_by_context(node);\\n }\\n }\\n nodesByType(type) {\\n const node_ids = this._children_by_type[type] || [];\\n const graph = this.node.scene().graph;\\n const nodes = [];\\n for (let node_id of node_ids) {\\n const node = graph.nodeFromId(node_id);\\n if (node) {\\n nodes.push(node);\\n }\\n }\\n return nodes;\\n }\\n child_by_name(name) {\\n return this._children[name];\\n }\\n has_children_and_grandchildren_with_context(context) {\\n return this._children_and_grandchildren_by_context[context] != null;\\n }\\n children() {\\n return Object.values(this._children);\\n }\\n children_names() {\\n return Object.keys(this._children).sort();\\n }\\n traverse_children(callback) {\\n for (let child of this.children()) {\\n callback(child);\\n child.childrenController?.traverse_children(callback);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/LifeCycleController.ts\\nvar LifeCycleController = class {\\n constructor(node) {\\n this.node = node;\\n this._creation_completed = false;\\n }\\n dispose() {\\n this._on_child_add_hooks = void 0;\\n this._on_child_remove_hooks = void 0;\\n this._on_create_hooks = void 0;\\n this._on_add_hooks = void 0;\\n this._on_delete_hooks = void 0;\\n }\\n setCreationCompleted() {\\n if (!this._creation_completed) {\\n this._creation_completed = true;\\n }\\n }\\n creationCompleted() {\\n return this.node.scene().loadingController.loaded() && this._creation_completed;\\n }\\n add_on_child_add_hook(callback) {\\n this._on_child_add_hooks = this._on_child_add_hooks || [];\\n this._on_child_add_hooks.push(callback);\\n }\\n run_on_child_add_hooks(node) {\\n this.execute_hooks_with_child_node(this._on_child_add_hooks, node);\\n }\\n add_on_child_remove_hook(callback) {\\n this._on_child_remove_hooks = this._on_child_remove_hooks || [];\\n this._on_child_remove_hooks.push(callback);\\n }\\n run_on_child_remove_hooks(node) {\\n this.execute_hooks_with_child_node(this._on_child_remove_hooks, node);\\n }\\n add_on_create_hook(callback) {\\n this._on_create_hooks = this._on_create_hooks || [];\\n this._on_create_hooks.push(callback);\\n }\\n run_on_create_hooks() {\\n this.execute_hooks(this._on_create_hooks);\\n }\\n add_on_add_hook(callback) {\\n this._on_add_hooks = this._on_add_hooks || [];\\n this._on_add_hooks.push(callback);\\n }\\n run_on_add_hooks() {\\n this.execute_hooks(this._on_add_hooks);\\n }\\n add_delete_hook(callback) {\\n this._on_delete_hooks = this._on_delete_hooks || [];\\n this._on_delete_hooks.push(callback);\\n }\\n run_on_delete_hooks() {\\n this.execute_hooks(this._on_delete_hooks);\\n }\\n execute_hooks(hooks) {\\n if (hooks) {\\n let hook;\\n for (hook of hooks) {\\n hook();\\n }\\n }\\n }\\n execute_hooks_with_child_node(hooks, child_node) {\\n if (hooks) {\\n let hook;\\n for (hook of hooks) {\\n hook(child_node);\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/NodeContext.ts\\nvar NodeContext;\\n(function(NodeContext3) {\\n NodeContext3[\\\\\\\"ANIM\\\\\\\"] = \\\\\\\"anim\\\\\\\";\\n NodeContext3[\\\\\\\"COP\\\\\\\"] = \\\\\\\"cop\\\\\\\";\\n NodeContext3[\\\\\\\"EVENT\\\\\\\"] = \\\\\\\"event\\\\\\\";\\n NodeContext3[\\\\\\\"GL\\\\\\\"] = \\\\\\\"gl\\\\\\\";\\n NodeContext3[\\\\\\\"JS\\\\\\\"] = \\\\\\\"js\\\\\\\";\\n NodeContext3[\\\\\\\"MANAGER\\\\\\\"] = \\\\\\\"manager\\\\\\\";\\n NodeContext3[\\\\\\\"MAT\\\\\\\"] = \\\\\\\"mat\\\\\\\";\\n NodeContext3[\\\\\\\"OBJ\\\\\\\"] = \\\\\\\"obj\\\\\\\";\\n NodeContext3[\\\\\\\"POST\\\\\\\"] = \\\\\\\"post\\\\\\\";\\n NodeContext3[\\\\\\\"ROP\\\\\\\"] = \\\\\\\"rop\\\\\\\";\\n NodeContext3[\\\\\\\"SOP\\\\\\\"] = \\\\\\\"sop\\\\\\\";\\n})(NodeContext || (NodeContext = {}));\\nvar NetworkNodeType;\\n(function(NetworkNodeType2) {\\n NetworkNodeType2[\\\\\\\"ANIM\\\\\\\"] = \\\\\\\"animationsNetwork\\\\\\\";\\n NetworkNodeType2[\\\\\\\"COP\\\\\\\"] = \\\\\\\"copNetwork\\\\\\\";\\n NetworkNodeType2[\\\\\\\"EVENT\\\\\\\"] = \\\\\\\"eventsNetwork\\\\\\\";\\n NetworkNodeType2[\\\\\\\"MAT\\\\\\\"] = \\\\\\\"materialsNetwork\\\\\\\";\\n NetworkNodeType2[\\\\\\\"POST\\\\\\\"] = \\\\\\\"postProcessNetwork\\\\\\\";\\n NetworkNodeType2[\\\\\\\"ROP\\\\\\\"] = \\\\\\\"renderersNetwork\\\\\\\";\\n})(NetworkNodeType || (NetworkNodeType = {}));\\nvar NetworkChildNodeType;\\n(function(NetworkChildNodeType2) {\\n NetworkChildNodeType2[\\\\\\\"INPUT\\\\\\\"] = \\\\\\\"subnetInput\\\\\\\";\\n NetworkChildNodeType2[\\\\\\\"OUTPUT\\\\\\\"] = \\\\\\\"subnetOutput\\\\\\\";\\n})(NetworkChildNodeType || (NetworkChildNodeType = {}));\\nvar CameraNodeType;\\n(function(CameraNodeType2) {\\n CameraNodeType2[\\\\\\\"PERSPECTIVE\\\\\\\"] = \\\\\\\"perspectiveCamera\\\\\\\";\\n CameraNodeType2[\\\\\\\"ORTHOGRAPHIC\\\\\\\"] = \\\\\\\"orthographicCamera\\\\\\\";\\n})(CameraNodeType || (CameraNodeType = {}));\\nvar CAMERA_TYPES = [CameraNodeType.ORTHOGRAPHIC, CameraNodeType.PERSPECTIVE];\\nvar GlNodeType;\\n(function(GlNodeType2) {\\n GlNodeType2[\\\\\\\"ATTRIBUTE\\\\\\\"] = \\\\\\\"attribute\\\\\\\";\\n})(GlNodeType || (GlNodeType = {}));\\nvar CameraControlsNodeType;\\n(function(CameraControlsNodeType2) {\\n CameraControlsNodeType2[\\\\\\\"DEVICE_ORIENTATION\\\\\\\"] = \\\\\\\"cameraDeviceOrientationControls\\\\\\\";\\n CameraControlsNodeType2[\\\\\\\"MAP\\\\\\\"] = \\\\\\\"cameraMapControls\\\\\\\";\\n CameraControlsNodeType2[\\\\\\\"ORBIT\\\\\\\"] = \\\\\\\"cameraOrbitControls\\\\\\\";\\n CameraControlsNodeType2[\\\\\\\"FIRST_PERSON\\\\\\\"] = \\\\\\\"firstPersonControls\\\\\\\";\\n CameraControlsNodeType2[\\\\\\\"PLAYER\\\\\\\"] = \\\\\\\"playerControls\\\\\\\";\\n CameraControlsNodeType2[\\\\\\\"MOBILE_JOYSTICK\\\\\\\"] = \\\\\\\"mobileJoystickControls\\\\\\\";\\n})(CameraControlsNodeType || (CameraControlsNodeType = {}));\\nvar CAMERA_CONTROLS_NODE_TYPES = [\\n CameraControlsNodeType.DEVICE_ORIENTATION,\\n CameraControlsNodeType.MAP,\\n CameraControlsNodeType.ORBIT,\\n CameraControlsNodeType.FIRST_PERSON,\\n CameraControlsNodeType.MOBILE_JOYSTICK\\n];\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/_Base.ts\\nvar TypedContainer = class {\\n constructor(_node) {\\n this._node = _node;\\n }\\n set_node(node) {\\n this._node = node;\\n }\\n node() {\\n return this._node;\\n }\\n set_content(content) {\\n this._content = content;\\n this._post_set_content();\\n }\\n has_content() {\\n return this._content != null;\\n }\\n content() {\\n return this._content;\\n }\\n _post_set_content() {\\n }\\n coreContent() {\\n return this._content;\\n }\\n coreContentCloned() {\\n return this._content;\\n }\\n infos() {\\n return [];\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Animation.ts\\nvar AnimationContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n setTimelineBuilder(timeline_builder) {\\n return this.set_content(timeline_builder);\\n }\\n timeline_builder() {\\n return this.content();\\n }\\n coreContentCloned() {\\n if (this._content) {\\n return this._content.clone();\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Event.ts\\nvar EventContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n};\\n\\n// ../../../node_modules/three/src/objects/SkinnedMesh.js\\nvar _basePosition = /* @__PURE__ */ new Vector3();\\nvar _skinIndex = /* @__PURE__ */ new Vector4();\\nvar _skinWeight = /* @__PURE__ */ new Vector4();\\nvar _vector7 = /* @__PURE__ */ new Vector3();\\nvar _matrix2 = /* @__PURE__ */ new Matrix4();\\nvar SkinnedMesh = class extends Mesh {\\n constructor(geometry, material) {\\n super(geometry, material);\\n this.type = \\\\\\\"SkinnedMesh\\\\\\\";\\n this.bindMode = \\\\\\\"attached\\\\\\\";\\n this.bindMatrix = new Matrix4();\\n this.bindMatrixInverse = new Matrix4();\\n }\\n copy(source) {\\n super.copy(source);\\n this.bindMode = source.bindMode;\\n this.bindMatrix.copy(source.bindMatrix);\\n this.bindMatrixInverse.copy(source.bindMatrixInverse);\\n this.skeleton = source.skeleton;\\n return this;\\n }\\n bind(skeleton, bindMatrix) {\\n this.skeleton = skeleton;\\n if (bindMatrix === void 0) {\\n this.updateMatrixWorld(true);\\n this.skeleton.calculateInverses();\\n bindMatrix = this.matrixWorld;\\n }\\n this.bindMatrix.copy(bindMatrix);\\n this.bindMatrixInverse.copy(bindMatrix).invert();\\n }\\n pose() {\\n this.skeleton.pose();\\n }\\n normalizeSkinWeights() {\\n const vector = new Vector4();\\n const skinWeight = this.geometry.attributes.skinWeight;\\n for (let i = 0, l = skinWeight.count; i < l; i++) {\\n vector.x = skinWeight.getX(i);\\n vector.y = skinWeight.getY(i);\\n vector.z = skinWeight.getZ(i);\\n vector.w = skinWeight.getW(i);\\n const scale = 1 / vector.manhattanLength();\\n if (scale !== Infinity) {\\n vector.multiplyScalar(scale);\\n } else {\\n vector.set(1, 0, 0, 0);\\n }\\n skinWeight.setXYZW(i, vector.x, vector.y, vector.z, vector.w);\\n }\\n }\\n updateMatrixWorld(force) {\\n super.updateMatrixWorld(force);\\n if (this.bindMode === \\\\\\\"attached\\\\\\\") {\\n this.bindMatrixInverse.copy(this.matrixWorld).invert();\\n } else if (this.bindMode === \\\\\\\"detached\\\\\\\") {\\n this.bindMatrixInverse.copy(this.bindMatrix).invert();\\n } else {\\n console.warn(\\\\\\\"THREE.SkinnedMesh: Unrecognized bindMode: \\\\\\\" + this.bindMode);\\n }\\n }\\n boneTransform(index, target) {\\n const skeleton = this.skeleton;\\n const geometry = this.geometry;\\n _skinIndex.fromBufferAttribute(geometry.attributes.skinIndex, index);\\n _skinWeight.fromBufferAttribute(geometry.attributes.skinWeight, index);\\n _basePosition.copy(target).applyMatrix4(this.bindMatrix);\\n target.set(0, 0, 0);\\n for (let i = 0; i < 4; i++) {\\n const weight = _skinWeight.getComponent(i);\\n if (weight !== 0) {\\n const boneIndex = _skinIndex.getComponent(i);\\n _matrix2.multiplyMatrices(skeleton.bones[boneIndex].matrixWorld, skeleton.boneInverses[boneIndex]);\\n target.addScaledVector(_vector7.copy(_basePosition).applyMatrix4(_matrix2), weight);\\n }\\n }\\n return target.applyMatrix4(this.bindMatrixInverse);\\n }\\n};\\nSkinnedMesh.prototype.isSkinnedMesh = true;\\n\\n// ../../../node_modules/three/src/scenes/Scene.js\\nvar Scene = class extends Object3D {\\n constructor() {\\n super();\\n this.type = \\\\\\\"Scene\\\\\\\";\\n this.background = null;\\n this.environment = null;\\n this.fog = null;\\n this.overrideMaterial = null;\\n this.autoUpdate = true;\\n if (typeof __THREE_DEVTOOLS__ !== \\\\\\\"undefined\\\\\\\") {\\n __THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\", {detail: this}));\\n }\\n }\\n copy(source, recursive) {\\n super.copy(source, recursive);\\n if (source.background !== null)\\n this.background = source.background.clone();\\n if (source.environment !== null)\\n this.environment = source.environment.clone();\\n if (source.fog !== null)\\n this.fog = source.fog.clone();\\n if (source.overrideMaterial !== null)\\n this.overrideMaterial = source.overrideMaterial.clone();\\n this.autoUpdate = source.autoUpdate;\\n this.matrixAutoUpdate = source.matrixAutoUpdate;\\n return this;\\n }\\n toJSON(meta) {\\n const data = super.toJSON(meta);\\n if (this.fog !== null)\\n data.object.fog = this.fog.toJSON();\\n return data;\\n }\\n};\\nScene.prototype.isScene = true;\\n\\n// ../../../node_modules/three/src/materials/PointsMaterial.js\\nvar PointsMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"PointsMaterial\\\\\\\";\\n this.color = new Color(16777215);\\n this.map = null;\\n this.alphaMap = null;\\n this.size = 1;\\n this.sizeAttenuation = true;\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.color.copy(source.color);\\n this.map = source.map;\\n this.alphaMap = source.alphaMap;\\n this.size = source.size;\\n this.sizeAttenuation = source.sizeAttenuation;\\n return this;\\n }\\n};\\nPointsMaterial.prototype.isPointsMaterial = true;\\n\\n// ../../../node_modules/three/src/objects/Points.js\\nvar _inverseMatrix2 = /* @__PURE__ */ new Matrix4();\\nvar _ray2 = /* @__PURE__ */ new Ray();\\nvar _sphere3 = /* @__PURE__ */ new Sphere();\\nvar _position2 = /* @__PURE__ */ new Vector3();\\nvar Points = class extends Object3D {\\n constructor(geometry = new BufferGeometry(), material = new PointsMaterial()) {\\n super();\\n this.type = \\\\\\\"Points\\\\\\\";\\n this.geometry = geometry;\\n this.material = material;\\n this.updateMorphTargets();\\n }\\n copy(source) {\\n super.copy(source);\\n this.material = source.material;\\n this.geometry = source.geometry;\\n return this;\\n }\\n raycast(raycaster, intersects) {\\n const geometry = this.geometry;\\n const matrixWorld = this.matrixWorld;\\n const threshold = raycaster.params.Points.threshold;\\n const drawRange = geometry.drawRange;\\n if (geometry.boundingSphere === null)\\n geometry.computeBoundingSphere();\\n _sphere3.copy(geometry.boundingSphere);\\n _sphere3.applyMatrix4(matrixWorld);\\n _sphere3.radius += threshold;\\n if (raycaster.ray.intersectsSphere(_sphere3) === false)\\n return;\\n _inverseMatrix2.copy(matrixWorld).invert();\\n _ray2.copy(raycaster.ray).applyMatrix4(_inverseMatrix2);\\n const localThreshold = threshold / ((this.scale.x + this.scale.y + this.scale.z) / 3);\\n const localThresholdSq = localThreshold * localThreshold;\\n if (geometry.isBufferGeometry) {\\n const index = geometry.index;\\n const attributes = geometry.attributes;\\n const positionAttribute = attributes.position;\\n if (index !== null) {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(index.count, drawRange.start + drawRange.count);\\n for (let i = start, il = end; i < il; i++) {\\n const a = index.getX(i);\\n _position2.fromBufferAttribute(positionAttribute, a);\\n testPoint(_position2, a, localThresholdSq, matrixWorld, raycaster, intersects, this);\\n }\\n } else {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(positionAttribute.count, drawRange.start + drawRange.count);\\n for (let i = start, l = end; i < l; i++) {\\n _position2.fromBufferAttribute(positionAttribute, i);\\n testPoint(_position2, i, localThresholdSq, matrixWorld, raycaster, intersects, this);\\n }\\n }\\n } else {\\n console.error(\\\\\\\"THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n updateMorphTargets() {\\n const geometry = this.geometry;\\n if (geometry.isBufferGeometry) {\\n const morphAttributes = geometry.morphAttributes;\\n const keys = Object.keys(morphAttributes);\\n if (keys.length > 0) {\\n const morphAttribute = morphAttributes[keys[0]];\\n if (morphAttribute !== void 0) {\\n this.morphTargetInfluences = [];\\n this.morphTargetDictionary = {};\\n for (let m = 0, ml = morphAttribute.length; m < ml; m++) {\\n const name = morphAttribute[m].name || String(m);\\n this.morphTargetInfluences.push(0);\\n this.morphTargetDictionary[name] = m;\\n }\\n }\\n }\\n } else {\\n const morphTargets = geometry.morphTargets;\\n if (morphTargets !== void 0 && morphTargets.length > 0) {\\n console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n }\\n};\\nPoints.prototype.isPoints = true;\\nfunction testPoint(point, index, localThresholdSq, matrixWorld, raycaster, intersects, object) {\\n const rayPointDistanceSq = _ray2.distanceSqToPoint(point);\\n if (rayPointDistanceSq < localThresholdSq) {\\n const intersectPoint = new Vector3();\\n _ray2.closestPointToPoint(point, intersectPoint);\\n intersectPoint.applyMatrix4(matrixWorld);\\n const distance = raycaster.ray.origin.distanceTo(intersectPoint);\\n if (distance < raycaster.near || distance > raycaster.far)\\n return;\\n intersects.push({\\n distance,\\n distanceToRay: Math.sqrt(rayPointDistanceSq),\\n point: intersectPoint,\\n index,\\n face: null,\\n object\\n });\\n }\\n}\\n\\n// ../../../node_modules/three/src/objects/Bone.js\\nvar Bone = class extends Object3D {\\n constructor() {\\n super();\\n this.type = \\\\\\\"Bone\\\\\\\";\\n }\\n};\\nBone.prototype.isBone = true;\\n\\n// ../../../node_modules/three/src/materials/MeshStandardMaterial.js\\nvar MeshStandardMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.defines = {STANDARD: \\\\\\\"\\\\\\\"};\\n this.type = \\\\\\\"MeshStandardMaterial\\\\\\\";\\n this.color = new Color(16777215);\\n this.roughness = 1;\\n this.metalness = 0;\\n this.map = null;\\n this.lightMap = null;\\n this.lightMapIntensity = 1;\\n this.aoMap = null;\\n this.aoMapIntensity = 1;\\n this.emissive = new Color(0);\\n this.emissiveIntensity = 1;\\n this.emissiveMap = null;\\n this.bumpMap = null;\\n this.bumpScale = 1;\\n this.normalMap = null;\\n this.normalMapType = TangentSpaceNormalMap;\\n this.normalScale = new Vector2(1, 1);\\n this.displacementMap = null;\\n this.displacementScale = 1;\\n this.displacementBias = 0;\\n this.roughnessMap = null;\\n this.metalnessMap = null;\\n this.alphaMap = null;\\n this.envMap = null;\\n this.envMapIntensity = 1;\\n this.refractionRatio = 0.98;\\n this.wireframe = false;\\n this.wireframeLinewidth = 1;\\n this.wireframeLinecap = \\\\\\\"round\\\\\\\";\\n this.wireframeLinejoin = \\\\\\\"round\\\\\\\";\\n this.flatShading = false;\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.defines = {STANDARD: \\\\\\\"\\\\\\\"};\\n this.color.copy(source.color);\\n this.roughness = source.roughness;\\n this.metalness = source.metalness;\\n this.map = source.map;\\n this.lightMap = source.lightMap;\\n this.lightMapIntensity = source.lightMapIntensity;\\n this.aoMap = source.aoMap;\\n this.aoMapIntensity = source.aoMapIntensity;\\n this.emissive.copy(source.emissive);\\n this.emissiveMap = source.emissiveMap;\\n this.emissiveIntensity = source.emissiveIntensity;\\n this.bumpMap = source.bumpMap;\\n this.bumpScale = source.bumpScale;\\n this.normalMap = source.normalMap;\\n this.normalMapType = source.normalMapType;\\n this.normalScale.copy(source.normalScale);\\n this.displacementMap = source.displacementMap;\\n this.displacementScale = source.displacementScale;\\n this.displacementBias = source.displacementBias;\\n this.roughnessMap = source.roughnessMap;\\n this.metalnessMap = source.metalnessMap;\\n this.alphaMap = source.alphaMap;\\n this.envMap = source.envMap;\\n this.envMapIntensity = source.envMapIntensity;\\n this.refractionRatio = source.refractionRatio;\\n this.wireframe = source.wireframe;\\n this.wireframeLinewidth = source.wireframeLinewidth;\\n this.wireframeLinecap = source.wireframeLinecap;\\n this.wireframeLinejoin = source.wireframeLinejoin;\\n this.flatShading = source.flatShading;\\n return this;\\n }\\n};\\nMeshStandardMaterial.prototype.isMeshStandardMaterial = true;\\n\\n// ../../../node_modules/three/src/materials/MeshLambertMaterial.js\\nvar MeshLambertMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"MeshLambertMaterial\\\\\\\";\\n this.color = new Color(16777215);\\n this.map = null;\\n this.lightMap = null;\\n this.lightMapIntensity = 1;\\n this.aoMap = null;\\n this.aoMapIntensity = 1;\\n this.emissive = new Color(0);\\n this.emissiveIntensity = 1;\\n this.emissiveMap = null;\\n this.specularMap = null;\\n this.alphaMap = null;\\n this.envMap = null;\\n this.combine = MultiplyOperation;\\n this.reflectivity = 1;\\n this.refractionRatio = 0.98;\\n this.wireframe = false;\\n this.wireframeLinewidth = 1;\\n this.wireframeLinecap = \\\\\\\"round\\\\\\\";\\n this.wireframeLinejoin = \\\\\\\"round\\\\\\\";\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.color.copy(source.color);\\n this.map = source.map;\\n this.lightMap = source.lightMap;\\n this.lightMapIntensity = source.lightMapIntensity;\\n this.aoMap = source.aoMap;\\n this.aoMapIntensity = source.aoMapIntensity;\\n this.emissive.copy(source.emissive);\\n this.emissiveMap = source.emissiveMap;\\n this.emissiveIntensity = source.emissiveIntensity;\\n this.specularMap = source.specularMap;\\n this.alphaMap = source.alphaMap;\\n this.envMap = source.envMap;\\n this.combine = source.combine;\\n this.reflectivity = source.reflectivity;\\n this.refractionRatio = source.refractionRatio;\\n this.wireframe = source.wireframe;\\n this.wireframeLinewidth = source.wireframeLinewidth;\\n this.wireframeLinecap = source.wireframeLinecap;\\n this.wireframeLinejoin = source.wireframeLinejoin;\\n return this;\\n }\\n};\\nMeshLambertMaterial.prototype.isMeshLambertMaterial = true;\\n\\n// ../../../node_modules/three/src/materials/LineBasicMaterial.js\\nvar LineBasicMaterial = class extends Material {\\n constructor(parameters) {\\n super();\\n this.type = \\\\\\\"LineBasicMaterial\\\\\\\";\\n this.color = new Color(16777215);\\n this.linewidth = 1;\\n this.linecap = \\\\\\\"round\\\\\\\";\\n this.linejoin = \\\\\\\"round\\\\\\\";\\n this.setValues(parameters);\\n }\\n copy(source) {\\n super.copy(source);\\n this.color.copy(source.color);\\n this.linewidth = source.linewidth;\\n this.linecap = source.linecap;\\n this.linejoin = source.linejoin;\\n return this;\\n }\\n};\\nLineBasicMaterial.prototype.isLineBasicMaterial = true;\\n\\n// ../../../node_modules/three/src/objects/Line.js\\nvar _start = /* @__PURE__ */ new Vector3();\\nvar _end = /* @__PURE__ */ new Vector3();\\nvar _inverseMatrix3 = /* @__PURE__ */ new Matrix4();\\nvar _ray3 = /* @__PURE__ */ new Ray();\\nvar _sphere4 = /* @__PURE__ */ new Sphere();\\nvar Line = class extends Object3D {\\n constructor(geometry = new BufferGeometry(), material = new LineBasicMaterial()) {\\n super();\\n this.type = \\\\\\\"Line\\\\\\\";\\n this.geometry = geometry;\\n this.material = material;\\n this.updateMorphTargets();\\n }\\n copy(source) {\\n super.copy(source);\\n this.material = source.material;\\n this.geometry = source.geometry;\\n return this;\\n }\\n computeLineDistances() {\\n const geometry = this.geometry;\\n if (geometry.isBufferGeometry) {\\n if (geometry.index === null) {\\n const positionAttribute = geometry.attributes.position;\\n const lineDistances = [0];\\n for (let i = 1, l = positionAttribute.count; i < l; i++) {\\n _start.fromBufferAttribute(positionAttribute, i - 1);\\n _end.fromBufferAttribute(positionAttribute, i);\\n lineDistances[i] = lineDistances[i - 1];\\n lineDistances[i] += _start.distanceTo(_end);\\n }\\n geometry.setAttribute(\\\\\\\"lineDistance\\\\\\\", new Float32BufferAttribute(lineDistances, 1));\\n } else {\\n console.warn(\\\\\\\"THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");\\n }\\n } else if (geometry.isGeometry) {\\n console.error(\\\\\\\"THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n return this;\\n }\\n raycast(raycaster, intersects) {\\n const geometry = this.geometry;\\n const matrixWorld = this.matrixWorld;\\n const threshold = raycaster.params.Line.threshold;\\n const drawRange = geometry.drawRange;\\n if (geometry.boundingSphere === null)\\n geometry.computeBoundingSphere();\\n _sphere4.copy(geometry.boundingSphere);\\n _sphere4.applyMatrix4(matrixWorld);\\n _sphere4.radius += threshold;\\n if (raycaster.ray.intersectsSphere(_sphere4) === false)\\n return;\\n _inverseMatrix3.copy(matrixWorld).invert();\\n _ray3.copy(raycaster.ray).applyMatrix4(_inverseMatrix3);\\n const localThreshold = threshold / ((this.scale.x + this.scale.y + this.scale.z) / 3);\\n const localThresholdSq = localThreshold * localThreshold;\\n const vStart = new Vector3();\\n const vEnd = new Vector3();\\n const interSegment = new Vector3();\\n const interRay = new Vector3();\\n const step = this.isLineSegments ? 2 : 1;\\n if (geometry.isBufferGeometry) {\\n const index = geometry.index;\\n const attributes = geometry.attributes;\\n const positionAttribute = attributes.position;\\n if (index !== null) {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(index.count, drawRange.start + drawRange.count);\\n for (let i = start, l = end - 1; i < l; i += step) {\\n const a = index.getX(i);\\n const b = index.getX(i + 1);\\n vStart.fromBufferAttribute(positionAttribute, a);\\n vEnd.fromBufferAttribute(positionAttribute, b);\\n const distSq = _ray3.distanceSqToSegment(vStart, vEnd, interRay, interSegment);\\n if (distSq > localThresholdSq)\\n continue;\\n interRay.applyMatrix4(this.matrixWorld);\\n const distance = raycaster.ray.origin.distanceTo(interRay);\\n if (distance < raycaster.near || distance > raycaster.far)\\n continue;\\n intersects.push({\\n distance,\\n point: interSegment.clone().applyMatrix4(this.matrixWorld),\\n index: i,\\n face: null,\\n faceIndex: null,\\n object: this\\n });\\n }\\n } else {\\n const start = Math.max(0, drawRange.start);\\n const end = Math.min(positionAttribute.count, drawRange.start + drawRange.count);\\n for (let i = start, l = end - 1; i < l; i += step) {\\n vStart.fromBufferAttribute(positionAttribute, i);\\n vEnd.fromBufferAttribute(positionAttribute, i + 1);\\n const distSq = _ray3.distanceSqToSegment(vStart, vEnd, interRay, interSegment);\\n if (distSq > localThresholdSq)\\n continue;\\n interRay.applyMatrix4(this.matrixWorld);\\n const distance = raycaster.ray.origin.distanceTo(interRay);\\n if (distance < raycaster.near || distance > raycaster.far)\\n continue;\\n intersects.push({\\n distance,\\n point: interSegment.clone().applyMatrix4(this.matrixWorld),\\n index: i,\\n face: null,\\n faceIndex: null,\\n object: this\\n });\\n }\\n }\\n } else if (geometry.isGeometry) {\\n console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n updateMorphTargets() {\\n const geometry = this.geometry;\\n if (geometry.isBufferGeometry) {\\n const morphAttributes = geometry.morphAttributes;\\n const keys = Object.keys(morphAttributes);\\n if (keys.length > 0) {\\n const morphAttribute = morphAttributes[keys[0]];\\n if (morphAttribute !== void 0) {\\n this.morphTargetInfluences = [];\\n this.morphTargetDictionary = {};\\n for (let m = 0, ml = morphAttribute.length; m < ml; m++) {\\n const name = morphAttribute[m].name || String(m);\\n this.morphTargetInfluences.push(0);\\n this.morphTargetDictionary[name] = m;\\n }\\n }\\n }\\n } else {\\n const morphTargets = geometry.morphTargets;\\n if (morphTargets !== void 0 && morphTargets.length > 0) {\\n console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n }\\n }\\n};\\nLine.prototype.isLine = true;\\n\\n// ../../../node_modules/three/src/objects/LineSegments.js\\nvar _start2 = /* @__PURE__ */ new Vector3();\\nvar _end2 = /* @__PURE__ */ new Vector3();\\nvar LineSegments = class extends Line {\\n constructor(geometry, material) {\\n super(geometry, material);\\n this.type = \\\\\\\"LineSegments\\\\\\\";\\n }\\n computeLineDistances() {\\n const geometry = this.geometry;\\n if (geometry.isBufferGeometry) {\\n if (geometry.index === null) {\\n const positionAttribute = geometry.attributes.position;\\n const lineDistances = [];\\n for (let i = 0, l = positionAttribute.count; i < l; i += 2) {\\n _start2.fromBufferAttribute(positionAttribute, i);\\n _end2.fromBufferAttribute(positionAttribute, i + 1);\\n lineDistances[i] = i === 0 ? 0 : lineDistances[i - 1];\\n lineDistances[i + 1] = lineDistances[i] + _start2.distanceTo(_end2);\\n }\\n geometry.setAttribute(\\\\\\\"lineDistance\\\\\\\", new Float32BufferAttribute(lineDistances, 1));\\n } else {\\n console.warn(\\\\\\\"THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");\\n }\\n } else if (geometry.isGeometry) {\\n console.error(\\\\\\\"THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");\\n }\\n return this;\\n }\\n};\\nLineSegments.prototype.isLineSegments = true;\\n\\n// ../../../node_modules/three/src/objects/LOD.js\\nvar _v16 = /* @__PURE__ */ new Vector3();\\nvar _v23 = /* @__PURE__ */ new Vector3();\\nvar LOD = class extends Object3D {\\n constructor() {\\n super();\\n this._currentLevel = 0;\\n this.type = \\\\\\\"LOD\\\\\\\";\\n Object.defineProperties(this, {\\n levels: {\\n enumerable: true,\\n value: []\\n },\\n isLOD: {\\n value: true\\n }\\n });\\n this.autoUpdate = true;\\n }\\n copy(source) {\\n super.copy(source, false);\\n const levels = source.levels;\\n for (let i = 0, l = levels.length; i < l; i++) {\\n const level = levels[i];\\n this.addLevel(level.object.clone(), level.distance);\\n }\\n this.autoUpdate = source.autoUpdate;\\n return this;\\n }\\n addLevel(object, distance = 0) {\\n distance = Math.abs(distance);\\n const levels = this.levels;\\n let l;\\n for (l = 0; l < levels.length; l++) {\\n if (distance < levels[l].distance) {\\n break;\\n }\\n }\\n levels.splice(l, 0, {distance, object});\\n this.add(object);\\n return this;\\n }\\n getCurrentLevel() {\\n return this._currentLevel;\\n }\\n getObjectForDistance(distance) {\\n const levels = this.levels;\\n if (levels.length > 0) {\\n let i, l;\\n for (i = 1, l = levels.length; i < l; i++) {\\n if (distance < levels[i].distance) {\\n break;\\n }\\n }\\n return levels[i - 1].object;\\n }\\n return null;\\n }\\n raycast(raycaster, intersects) {\\n const levels = this.levels;\\n if (levels.length > 0) {\\n _v16.setFromMatrixPosition(this.matrixWorld);\\n const distance = raycaster.ray.origin.distanceTo(_v16);\\n this.getObjectForDistance(distance).raycast(raycaster, intersects);\\n }\\n }\\n update(camera) {\\n const levels = this.levels;\\n if (levels.length > 1) {\\n _v16.setFromMatrixPosition(camera.matrixWorld);\\n _v23.setFromMatrixPosition(this.matrixWorld);\\n const distance = _v16.distanceTo(_v23) / camera.zoom;\\n levels[0].object.visible = true;\\n let i, l;\\n for (i = 1, l = levels.length; i < l; i++) {\\n if (distance >= levels[i].distance) {\\n levels[i - 1].object.visible = false;\\n levels[i].object.visible = true;\\n } else {\\n break;\\n }\\n }\\n this._currentLevel = i - 1;\\n for (; i < l; i++) {\\n levels[i].object.visible = false;\\n }\\n }\\n }\\n toJSON(meta) {\\n const data = super.toJSON(meta);\\n if (this.autoUpdate === false)\\n data.object.autoUpdate = false;\\n data.object.levels = [];\\n const levels = this.levels;\\n for (let i = 0, l = levels.length; i < l; i++) {\\n const level = levels[i];\\n data.object.levels.push({\\n object: level.object.uuid,\\n distance: level.distance\\n });\\n }\\n return data;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Constant.ts\\nvar ObjectType;\\n(function(ObjectType2) {\\n ObjectType2[\\\\\\\"OBJECT3D\\\\\\\"] = \\\\\\\"Object3D\\\\\\\";\\n ObjectType2[\\\\\\\"GROUP\\\\\\\"] = \\\\\\\"Group\\\\\\\";\\n ObjectType2[\\\\\\\"MESH\\\\\\\"] = \\\\\\\"Mesh\\\\\\\";\\n ObjectType2[\\\\\\\"POINTS\\\\\\\"] = \\\\\\\"Points\\\\\\\";\\n ObjectType2[\\\\\\\"LINE_SEGMENTS\\\\\\\"] = \\\\\\\"LineSegments\\\\\\\";\\n ObjectType2[\\\\\\\"LOD\\\\\\\"] = \\\\\\\"LOD\\\\\\\";\\n})(ObjectType || (ObjectType = {}));\\nvar OBJECT_CONSTRUCTOR_BY_OBJECT_TYPE = {\\n [ObjectType.MESH]: Mesh,\\n [ObjectType.GROUP]: Group,\\n [ObjectType.POINTS]: Points,\\n [ObjectType.LINE_SEGMENTS]: LineSegments,\\n [ObjectType.OBJECT3D]: Object3D,\\n [ObjectType.LOD]: LOD\\n};\\nfunction objectTypeFromConstructor(constructor) {\\n switch (constructor) {\\n case Object3D:\\n return ObjectType.OBJECT3D;\\n case Group:\\n return ObjectType.GROUP;\\n case Mesh:\\n return ObjectType.MESH;\\n case Points:\\n return ObjectType.POINTS;\\n case LineSegments:\\n return ObjectType.LINE_SEGMENTS;\\n case LOD:\\n return ObjectType.LOD;\\n default:\\n Poly.warn(\\\\\\\"object type not supported\\\\\\\", constructor);\\n return ObjectType.MESH;\\n }\\n}\\nvar ObjectTypes = [ObjectType.MESH, ObjectType.POINTS, ObjectType.LINE_SEGMENTS];\\nvar ObjectTypeMenuEntries = [\\n {name: \\\\\\\"Mesh\\\\\\\", value: ObjectTypes.indexOf(ObjectType.MESH)},\\n {name: \\\\\\\"Points\\\\\\\", value: ObjectTypes.indexOf(ObjectType.POINTS)},\\n {name: \\\\\\\"LineSegments\\\\\\\", value: ObjectTypes.indexOf(ObjectType.LINE_SEGMENTS)}\\n];\\nvar materials = {\\n MeshStandard: new MeshStandardMaterial({\\n color: 16777215,\\n side: FrontSide,\\n metalness: 0.5,\\n roughness: 0.9\\n }),\\n [ObjectType.MESH]: new MeshLambertMaterial({\\n color: new Color(1, 1, 1),\\n side: FrontSide,\\n vertexColors: false,\\n transparent: true,\\n depthTest: true\\n }),\\n [ObjectType.POINTS]: new PointsMaterial({\\n color: 16777215,\\n size: 0.1,\\n depthTest: true\\n }),\\n [ObjectType.LINE_SEGMENTS]: new LineBasicMaterial({\\n color: 16777215,\\n linewidth: 1\\n })\\n};\\nvar AttribClass;\\n(function(AttribClass3) {\\n AttribClass3[AttribClass3[\\\\\\\"VERTEX\\\\\\\"] = 0] = \\\\\\\"VERTEX\\\\\\\";\\n AttribClass3[AttribClass3[\\\\\\\"OBJECT\\\\\\\"] = 1] = \\\\\\\"OBJECT\\\\\\\";\\n})(AttribClass || (AttribClass = {}));\\nvar AttribType;\\n(function(AttribType2) {\\n AttribType2[AttribType2[\\\\\\\"NUMERIC\\\\\\\"] = 0] = \\\\\\\"NUMERIC\\\\\\\";\\n AttribType2[AttribType2[\\\\\\\"STRING\\\\\\\"] = 1] = \\\\\\\"STRING\\\\\\\";\\n})(AttribType || (AttribType = {}));\\nvar AttribSize;\\n(function(AttribSize5) {\\n AttribSize5[AttribSize5[\\\\\\\"FLOAT\\\\\\\"] = 1] = \\\\\\\"FLOAT\\\\\\\";\\n AttribSize5[AttribSize5[\\\\\\\"VECTOR2\\\\\\\"] = 2] = \\\\\\\"VECTOR2\\\\\\\";\\n AttribSize5[AttribSize5[\\\\\\\"VECTOR3\\\\\\\"] = 3] = \\\\\\\"VECTOR3\\\\\\\";\\n AttribSize5[AttribSize5[\\\\\\\"VECTOR4\\\\\\\"] = 4] = \\\\\\\"VECTOR4\\\\\\\";\\n})(AttribSize || (AttribSize = {}));\\nvar CoreConstant = {\\n ATTRIB_CLASS: {\\n VERTEX: 0,\\n OBJECT: 1\\n },\\n OBJECT_TYPES: ObjectTypes,\\n CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME: {\\n [Scene.name]: \\\\\\\"Scene\\\\\\\",\\n [Group.name]: \\\\\\\"Group\\\\\\\",\\n [Object3D.name]: \\\\\\\"Object3D\\\\\\\",\\n [Mesh.name]: \\\\\\\"Mesh\\\\\\\",\\n [Points.name]: \\\\\\\"Points\\\\\\\",\\n [LineSegments.name]: \\\\\\\"LineSegments\\\\\\\",\\n [Bone.name]: \\\\\\\"Bone\\\\\\\",\\n [SkinnedMesh.name]: \\\\\\\"SkinnedMesh\\\\\\\"\\n },\\n CONSTRUCTORS_BY_NAME: {\\n [ObjectType.MESH]: Mesh,\\n [ObjectType.POINTS]: Points,\\n [ObjectType.LINE_SEGMENTS]: LineSegments\\n },\\n MATERIALS: materials\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Attribute.ts\\nvar Attribute;\\n(function(Attribute2) {\\n Attribute2[\\\\\\\"POSITION\\\\\\\"] = \\\\\\\"position\\\\\\\";\\n Attribute2[\\\\\\\"NORMAL\\\\\\\"] = \\\\\\\"normal\\\\\\\";\\n Attribute2[\\\\\\\"TANGENT\\\\\\\"] = \\\\\\\"tangent\\\\\\\";\\n})(Attribute || (Attribute = {}));\\nvar ATTRIB_NAME_MAP = {\\n P: \\\\\\\"position\\\\\\\",\\n N: \\\\\\\"normal\\\\\\\",\\n Cd: \\\\\\\"color\\\\\\\"\\n};\\nvar CoreAttribute = class {\\n static remapName(name) {\\n return ATTRIB_NAME_MAP[name] || name;\\n }\\n static arrayToIndexedArrays(array) {\\n const index_by_value = {};\\n let current_index = 0;\\n const indices = [];\\n const values = [];\\n let i = 0;\\n while (i < array.length) {\\n const value = array[i];\\n const index = index_by_value[value];\\n if (index != null) {\\n indices.push(index);\\n } else {\\n values.push(value);\\n indices.push(current_index);\\n index_by_value[value] = current_index;\\n current_index += 1;\\n }\\n i++;\\n }\\n return {\\n indices,\\n values\\n };\\n }\\n static default_value(size) {\\n switch (size) {\\n case 1:\\n return 0;\\n case 2:\\n return new Vector2(0, 0);\\n case 3:\\n return new Vector3(0, 0, 0);\\n default:\\n throw `size ${size} not yet implemented`;\\n }\\n }\\n static copy(src, dest, mark_as_needs_update = true) {\\n const src_array = src?.array;\\n const dest_array = dest?.array;\\n if (src_array && dest_array) {\\n const min_length = Math.min(src_array.length, dest_array.length);\\n for (let i = 0; i < min_length; i++) {\\n dest_array[i] = src_array[i];\\n }\\n if (mark_as_needs_update) {\\n dest.needsUpdate = true;\\n }\\n }\\n }\\n static attribSizeFromValue(val) {\\n if (CoreType.isString(val) || CoreType.isNumber(val)) {\\n return AttribSize.FLOAT;\\n }\\n if (CoreType.isArray(val)) {\\n return val.length;\\n }\\n switch (val.constructor) {\\n case Vector2:\\n return AttribSize.VECTOR2;\\n case Vector3:\\n return AttribSize.VECTOR3;\\n case Vector4:\\n return AttribSize.VECTOR4;\\n }\\n return 0;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Entity.ts\\nvar CoreEntity = class {\\n constructor(_index) {\\n this._index = _index;\\n }\\n index() {\\n return this._index;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Point.ts\\nvar ATTRIB_NAMES = {\\n POSITION: \\\\\\\"position\\\\\\\",\\n NORMAL: \\\\\\\"normal\\\\\\\"\\n};\\nvar ComponentName;\\n(function(ComponentName2) {\\n ComponentName2[\\\\\\\"x\\\\\\\"] = \\\\\\\"x\\\\\\\";\\n ComponentName2[\\\\\\\"y\\\\\\\"] = \\\\\\\"y\\\\\\\";\\n ComponentName2[\\\\\\\"z\\\\\\\"] = \\\\\\\"z\\\\\\\";\\n ComponentName2[\\\\\\\"w\\\\\\\"] = \\\\\\\"w\\\\\\\";\\n ComponentName2[\\\\\\\"r\\\\\\\"] = \\\\\\\"r\\\\\\\";\\n ComponentName2[\\\\\\\"g\\\\\\\"] = \\\\\\\"g\\\\\\\";\\n ComponentName2[\\\\\\\"b\\\\\\\"] = \\\\\\\"b\\\\\\\";\\n})(ComponentName || (ComponentName = {}));\\nvar COMPONENT_INDICES = {\\n x: 0,\\n y: 1,\\n z: 2,\\n w: 3,\\n r: 0,\\n g: 1,\\n b: 2\\n};\\nvar PTNUM = \\\\\\\"ptnum\\\\\\\";\\nvar DOT = \\\\\\\".\\\\\\\";\\nvar CorePoint = class extends CoreEntity {\\n constructor(_coreGeometry, _index) {\\n super(_index);\\n this._coreGeometry = _coreGeometry;\\n this._geometry = this._coreGeometry.geometry();\\n }\\n applyMatrix4(matrix) {\\n this.position().applyMatrix4(matrix);\\n }\\n coreGeometry() {\\n return this._coreGeometry;\\n }\\n geometry() {\\n return this._geometry = this._geometry || this._coreGeometry.geometry();\\n }\\n attribSize(name) {\\n name = CoreAttribute.remapName(name);\\n return this._geometry.getAttribute(name).itemSize;\\n }\\n hasAttrib(name) {\\n const remapped_name = CoreAttribute.remapName(name);\\n return this._coreGeometry.hasAttrib(remapped_name);\\n }\\n attribValue(name, target) {\\n if (name === PTNUM) {\\n return this.index();\\n } else {\\n let component_name = null;\\n let component_index = null;\\n if (name[name.length - 2] === DOT) {\\n component_name = name[name.length - 1];\\n component_index = COMPONENT_INDICES[component_name];\\n name = name.substring(0, name.length - 2);\\n }\\n const remaped_name = CoreAttribute.remapName(name);\\n const attrib = this._geometry.getAttribute(remaped_name);\\n if (attrib) {\\n const {array} = attrib;\\n if (this._coreGeometry.isAttribIndexed(remaped_name)) {\\n return this.indexedAttribValue(remaped_name);\\n } else {\\n const size = attrib.itemSize;\\n const start_index = this._index * size;\\n if (component_index == null) {\\n switch (size) {\\n case 1:\\n return array[start_index];\\n break;\\n case 2:\\n target = target || new Vector2();\\n target.fromArray(array, start_index);\\n return target;\\n break;\\n case 3:\\n target = target || new Vector3();\\n target.fromArray(array, start_index);\\n return target;\\n break;\\n case 4:\\n target = target || new Vector4();\\n target.fromArray(array, start_index);\\n return target;\\n break;\\n default:\\n throw `size not valid (${size})`;\\n }\\n } else {\\n switch (size) {\\n case 1:\\n return array[start_index];\\n break;\\n default:\\n return array[start_index + component_index];\\n }\\n }\\n }\\n } else {\\n const message = `attrib ${name} not found. availables are: ${Object.keys(this._geometry.attributes || {}).join(\\\\\\\",\\\\\\\")}`;\\n console.warn(message);\\n throw message;\\n }\\n }\\n }\\n indexedAttribValue(name) {\\n const value_index = this.attribValueIndex(name);\\n return this._coreGeometry.userDataAttrib(name)[value_index];\\n }\\n stringAttribValue(name) {\\n return this.indexedAttribValue(name);\\n }\\n attribValueIndex(name) {\\n if (this._coreGeometry.isAttribIndexed(name)) {\\n return this._geometry.getAttribute(name).array[this._index];\\n } else {\\n return -1;\\n }\\n }\\n isAttribIndexed(name) {\\n return this._coreGeometry.isAttribIndexed(name);\\n }\\n position() {\\n return this._position || (this._position = this.getPosition(new Vector3()));\\n }\\n getPosition(target) {\\n const {array} = this._geometry.getAttribute(ATTRIB_NAMES.POSITION);\\n return target.fromArray(array, this._index * 3);\\n }\\n setPosition(new_position) {\\n this.setAttribValueVector3(ATTRIB_NAMES.POSITION, new_position);\\n }\\n normal() {\\n return this._normal = this._normal || this.getNormal(new Vector3());\\n }\\n getNormal(target) {\\n const {array} = this._geometry.getAttribute(ATTRIB_NAMES.NORMAL);\\n return target.fromArray(array, this._index * 3);\\n }\\n setNormal(new_normal) {\\n return this.setAttribValueVector3(ATTRIB_NAMES.NORMAL, new_normal);\\n }\\n setAttribValue(name, value) {\\n if (value == null) {\\n return;\\n }\\n if (name == null) {\\n throw \\\\\\\"Point.set_attrib_value requires a name\\\\\\\";\\n }\\n const attrib = this._geometry.getAttribute(name);\\n const array = attrib.array;\\n const attrib_size = attrib.itemSize;\\n if (CoreType.isArray(value)) {\\n for (let i = 0; i < attrib_size; i++) {\\n array[this._index * attrib_size + i] = value[i];\\n }\\n return;\\n }\\n switch (attrib_size) {\\n case 1:\\n array[this._index] = value;\\n break;\\n case 2:\\n const v2 = value;\\n array[this._index * 2 + 0] = v2.x;\\n array[this._index * 2 + 1] = v2.y;\\n break;\\n case 3:\\n const is_color = value.r != null;\\n if (is_color) {\\n const col = value;\\n array[this._index * 3 + 0] = col.r;\\n array[this._index * 3 + 1] = col.g;\\n array[this._index * 3 + 2] = col.b;\\n } else {\\n const v3 = value;\\n array[this._index * 3 + 0] = v3.x;\\n array[this._index * 3 + 1] = v3.y;\\n array[this._index * 3 + 2] = v3.z;\\n }\\n break;\\n case 4:\\n const v4 = value;\\n array[this._index * 4 + 0] = v4.x;\\n array[this._index * 4 + 1] = v4.y;\\n array[this._index * 4 + 2] = v4.z;\\n array[this._index * 4 + 3] = v4.w;\\n break;\\n default:\\n console.warn(`Point.set_attrib_value does not yet allow attrib size ${attrib_size}`);\\n throw `attrib size ${attrib_size} not implemented`;\\n }\\n }\\n setAttribValueVector3(name, value) {\\n if (value == null) {\\n return;\\n }\\n if (name == null) {\\n throw \\\\\\\"Point.set_attrib_value requires a name\\\\\\\";\\n }\\n const attrib = this._geometry.getAttribute(name);\\n const array = attrib.array;\\n const i = this._index * 3;\\n array[i] = value.x;\\n array[i + 1] = value.y;\\n array[i + 2] = value.z;\\n }\\n setAttribIndex(name, new_value_index) {\\n const array = this._geometry.getAttribute(name).array;\\n return array[this._index] = new_value_index;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/math/Easing.ts\\nvar ease_i = function(power) {\\n return function(t) {\\n return Math.pow(t, power);\\n };\\n};\\nvar ease_o = function(power) {\\n return function(t) {\\n return 1 - Math.abs(Math.pow(t - 1, power));\\n };\\n};\\nvar ease_io = function(power) {\\n return function(t) {\\n return t < 0.5 ? ease_i(power)(t * 2) / 2 : ease_o(power)(t * 2 - 1) / 2 + 0.5;\\n };\\n};\\nvar Easing = {\\n linear: ease_io(1),\\n ease_i: function(t, power) {\\n return ease_i(power)(t);\\n },\\n ease_o: function(t, power) {\\n return ease_o(power)(t);\\n },\\n ease_io: function(t, power) {\\n return ease_io(power)(t);\\n },\\n ease_i2: ease_i(2),\\n ease_o2: ease_o(2),\\n ease_io2: ease_io(2),\\n ease_i3: ease_io(3),\\n ease_o3: ease_io(3),\\n ease_io3: ease_io(3),\\n ease_i4: ease_io(4),\\n ease_o4: ease_io(4),\\n ease_io4: ease_io(4),\\n ease_i_sin: function(t) {\\n return 1 + Math.sin(Math.PI / 2 * t - Math.PI / 2);\\n },\\n ease_o_sin: function(t) {\\n return Math.sin(Math.PI / 2 * t);\\n },\\n ease_io_sin: function(t) {\\n return (1 + Math.sin(Math.PI * t - Math.PI / 2)) / 2;\\n },\\n ease_i_elastic: function(t) {\\n return (0.04 - 0.04 / t) * Math.sin(25 * t) + 1;\\n },\\n ease_o_elastic: function(t) {\\n return 0.04 * t / --t * Math.sin(25 * t);\\n },\\n ease_io_elastic: function(t) {\\n return (t -= 0.5) < 0 ? (0.02 + 0.01 / t) * Math.sin(50 * t) : (0.02 - 0.01 / t) * Math.sin(50 * t) + 1;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/math/_Module.ts\\nvar RAD_DEG_RATIO = Math.PI / 180;\\nvar RAND_A = 12.9898;\\nvar RAND_B = 78.233;\\nvar RAND_C = 43758.5453;\\nvar CoreMath = class {\\n static clamp(val, min, max) {\\n if (val < min) {\\n return min;\\n } else if (val > max) {\\n return max;\\n } else {\\n return val;\\n }\\n }\\n static fit01(val, dest_min, dest_max) {\\n return this.fit(val, 0, 1, dest_min, dest_max);\\n }\\n static fit(val, src_min, src_max, dest_min, dest_max) {\\n const src_range = src_max - src_min;\\n const dest_range = dest_max - dest_min;\\n const r = (val - src_min) / src_range;\\n return r * dest_range + dest_min;\\n }\\n static blend(num0, num1, blend) {\\n return (1 - blend) * num0 + blend * num1;\\n }\\n static degrees_to_radians(degrees) {\\n return degrees * RAD_DEG_RATIO;\\n }\\n static radians_to_degrees(radians) {\\n return radians / RAD_DEG_RATIO;\\n }\\n static deg2rad(deg) {\\n return this.degrees_to_radians(deg);\\n }\\n static rad2deg(rad) {\\n return this.radians_to_degrees(rad);\\n }\\n static rand(number) {\\n if (CoreType.isNumber(number)) {\\n return this.randFloat(number);\\n } else {\\n return this.randVec2(number);\\n }\\n }\\n static round(number, step_size) {\\n const steps_count = number / step_size;\\n const rounded_steps_count = number < 0 ? Math.ceil(steps_count) : Math.floor(steps_count);\\n return rounded_steps_count * step_size;\\n }\\n static highest_even(number) {\\n return 2 * Math.ceil(number * 0.5);\\n }\\n static randFloat(x, y = 136574) {\\n this._vec.x = x;\\n this._vec.y = y;\\n return this.randVec2(this._vec);\\n }\\n static randVec2(uv) {\\n const dt = uv.x * RAND_A + uv.y * RAND_B;\\n const sn = dt % Math.PI;\\n return this.fract(Math.sin(sn) * RAND_C);\\n }\\n static geodesic_distance(lnglat1, lnglat2) {\\n var R = 6371e3;\\n var d1 = this.deg2rad(lnglat1.lat);\\n var d2 = this.deg2rad(lnglat2.lat);\\n var ad1 = this.deg2rad(lnglat2.lat - lnglat1.lat);\\n var ad2 = this.deg2rad(lnglat2.lng - lnglat1.lng);\\n var a = Math.sin(ad1 / 2) * Math.sin(ad1 / 2) + Math.cos(d1) * Math.cos(d2) * Math.sin(ad2 / 2) * Math.sin(ad2 / 2);\\n var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));\\n var d = R * c;\\n return d;\\n }\\n static expand_triangle(triangle, margin) {\\n triangle.getMidpoint(this._triangle_mid);\\n this._triangle_mid_to_corner.copy(triangle.a).sub(this._triangle_mid);\\n this._triangle_mid_to_corner.normalize().multiplyScalar(margin);\\n triangle.a.add(this._triangle_mid_to_corner);\\n this._triangle_mid_to_corner.copy(triangle.b).sub(this._triangle_mid);\\n this._triangle_mid_to_corner.normalize().multiplyScalar(margin);\\n triangle.b.add(this._triangle_mid_to_corner);\\n this._triangle_mid_to_corner.copy(triangle.c).sub(this._triangle_mid);\\n this._triangle_mid_to_corner.normalize().multiplyScalar(margin);\\n triangle.c.add(this._triangle_mid_to_corner);\\n }\\n static nearestPower2(num) {\\n return Math.pow(2, Math.ceil(Math.log(num) / Math.log(2)));\\n }\\n};\\nCoreMath.Easing = Easing;\\nCoreMath.fract = (number) => number - Math.floor(number);\\nCoreMath._vec = {x: 0, y: 136574};\\nCoreMath._triangle_mid = new Vector3();\\nCoreMath._triangle_mid_to_corner = new Vector3();\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Face.ts\\nvar CoreFace = class {\\n constructor(_coreGeometry, _index) {\\n this._coreGeometry = _coreGeometry;\\n this._index = _index;\\n this._geometry = this._coreGeometry.geometry();\\n }\\n index() {\\n return this._index;\\n }\\n points() {\\n return this._points = this._points || this._get_points();\\n }\\n applyMatrix4(matrix) {\\n for (let point of this.points()) {\\n point.applyMatrix4(matrix);\\n }\\n }\\n _get_points() {\\n const indexArray = this._geometry.index?.array || [];\\n const start = this._index * 3;\\n return [\\n new CorePoint(this._coreGeometry, indexArray[start + 0]),\\n new CorePoint(this._coreGeometry, indexArray[start + 1]),\\n new CorePoint(this._coreGeometry, indexArray[start + 2])\\n ];\\n }\\n positions() {\\n return this._positions = this._positions || this._getPositions();\\n }\\n _getPositions() {\\n const points2 = this.points();\\n return [points2[0].position(), points2[1].position(), points2[2].position()];\\n }\\n triangle() {\\n return this._triangle = this._triangle || this._get_triangle();\\n }\\n _get_triangle() {\\n const positions = this.positions();\\n return new Triangle(positions[0], positions[1], positions[2]);\\n }\\n deltas() {\\n return this._deltas = this._deltas || this._getDeltas();\\n }\\n _getDeltas() {\\n const positions = this.positions();\\n return [positions[1].clone().sub(positions[0]), positions[2].clone().sub(positions[0])];\\n }\\n area() {\\n return this.triangle().getArea();\\n }\\n center(target) {\\n const positions = this.positions();\\n target.x = (positions[0].x + positions[1].x + positions[2].x) / 3;\\n target.y = (positions[0].y + positions[1].y + positions[2].y) / 3;\\n target.z = (positions[0].z + positions[1].z + positions[2].z) / 3;\\n return target;\\n }\\n randomPosition(seed) {\\n let weights = [CoreMath.randFloat(seed), CoreMath.randFloat(seed * 6541)];\\n if (weights[0] + weights[1] > 1) {\\n weights[0] = 1 - weights[0];\\n weights[1] = 1 - weights[1];\\n }\\n return this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(weights[0])).add(this.deltas()[1].clone().multiplyScalar(weights[1]));\\n }\\n attribValueAtPosition(attrib_name, position) {\\n const barycentricCoordinates = new Vector3();\\n this.triangle().getBarycoord(position, barycentricCoordinates);\\n const weights = barycentricCoordinates.toArray();\\n const attrib = this._geometry.attributes[attrib_name];\\n const attribSize = attrib.itemSize;\\n const pointValues = this.points().map((point) => point.attribValue(attrib_name));\\n let newAttribValue;\\n let sum;\\n let index = 0;\\n switch (attribSize) {\\n case 1: {\\n sum = 0;\\n for (let pointValue of pointValues) {\\n sum += pointValue * weights[index];\\n index++;\\n }\\n newAttribValue = sum;\\n break;\\n }\\n default: {\\n for (let pointValue of pointValues) {\\n const weightedValue = pointValue.multiplyScalar(weights[index]);\\n if (sum) {\\n sum.add(weightedValue);\\n } else {\\n sum = weightedValue;\\n }\\n index++;\\n }\\n newAttribValue = sum;\\n }\\n }\\n return newAttribValue;\\n }\\n static interpolatedValue(geometry, face, intersectPoint, attrib) {\\n const pointIndices = [face.a, face.b, face.c];\\n const positionAttrib = geometry.getAttribute(\\\\\\\"position\\\\\\\");\\n const positionAttribArray = positionAttrib.array;\\n const pointPositions = pointIndices.map((point_index) => new Vector3(positionAttribArray[point_index * 3 + 0], positionAttribArray[point_index * 3 + 1], positionAttribArray[point_index * 3 + 2]));\\n const attribSize = attrib.itemSize;\\n const attribArray = attrib.array;\\n let attribValues = [];\\n switch (attribSize) {\\n case 1:\\n attribValues = pointIndices.map((point_index) => attribArray[point_index]);\\n break;\\n case 2:\\n attribValues = pointIndices.map((point_index) => new Vector2(attribArray[point_index * 2 + 0], attribArray[point_index * 2 + 1]));\\n break;\\n case 3:\\n attribValues = pointIndices.map((point_index) => new Vector3(attribArray[point_index * 3 + 0], attribArray[point_index * 3 + 1], attribArray[point_index * 3 + 2]));\\n break;\\n }\\n const distToPoints = pointIndices.map((point_index, i) => intersectPoint.distanceTo(pointPositions[i]));\\n const distanceTotal = ArrayUtils.sum([\\n distToPoints[0] * distToPoints[1],\\n distToPoints[0] * distToPoints[2],\\n distToPoints[1] * distToPoints[2]\\n ]);\\n const weights = [\\n distToPoints[1] * distToPoints[2] / distanceTotal,\\n distToPoints[0] * distToPoints[2] / distanceTotal,\\n distToPoints[0] * distToPoints[1] / distanceTotal\\n ];\\n let newAttribValue;\\n switch (attribSize) {\\n case 1:\\n newAttribValue = ArrayUtils.sum(pointIndices.map((point_indx, i) => weights[i] * attribValues[i]));\\n break;\\n default:\\n var values = pointIndices.map((point_index, i) => attribValues[i].multiplyScalar(weights[i]));\\n newAttribValue = null;\\n for (let value of values) {\\n if (newAttribValue) {\\n newAttribValue.add(value);\\n } else {\\n newAttribValue = value;\\n }\\n }\\n }\\n return newAttribValue;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/builders/_Base.ts\\nvar CoreGeometryBuilderBase = class {\\n from_points(points2) {\\n points2 = this._filter_points(points2);\\n const geometry = new BufferGeometry();\\n const core_geometry = new CoreGeometry(geometry);\\n const first_point = points2[0];\\n if (first_point != null) {\\n const old_geometry = first_point.geometry();\\n const old_core_geometry = first_point.coreGeometry();\\n const new_index_by_old_index = {};\\n for (let i = 0; i < points2.length; i++) {\\n new_index_by_old_index[points2[i].index()] = i;\\n }\\n const indices = this._indices_from_points(new_index_by_old_index, old_geometry);\\n if (indices) {\\n geometry.setIndex(indices);\\n }\\n const {attributes} = old_geometry;\\n for (let attribute_name of Object.keys(attributes)) {\\n const attrib_values = old_core_geometry.userDataAttribs()[attribute_name];\\n const is_attrib_indexed = attrib_values != null;\\n if (is_attrib_indexed) {\\n const new_values = ArrayUtils.uniq(points2.map((point) => point.indexedAttribValue(attribute_name)));\\n const new_index_by_value = {};\\n new_values.forEach((new_value, i) => new_index_by_value[new_value] = i);\\n core_geometry.userDataAttribs()[attribute_name] = new_values;\\n const new_attrib_indices = [];\\n for (let point of points2) {\\n const new_index = new_index_by_value[point.indexedAttribValue(attribute_name)];\\n new_attrib_indices.push(new_index);\\n }\\n geometry.setAttribute(attribute_name, new Float32BufferAttribute(new_attrib_indices, 1));\\n } else {\\n const attrib_size = attributes[attribute_name].itemSize;\\n const values = new Array(points2.length * attrib_size);\\n switch (attrib_size) {\\n case 1:\\n for (let i = 0; i < points2.length; i++) {\\n values[i] = points2[i].attribValue(attribute_name);\\n }\\n break;\\n default:\\n let value;\\n for (let i = 0; i < points2.length; i++) {\\n value = points2[i].attribValue(attribute_name);\\n value.toArray(values, i * attrib_size);\\n }\\n break;\\n }\\n geometry.setAttribute(attribute_name, new Float32BufferAttribute(values, attrib_size));\\n }\\n }\\n }\\n return geometry;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/builders/Points.ts\\nvar CoreGeometryBuilderPoints = class extends CoreGeometryBuilderBase {\\n _filter_points(points2) {\\n return points2;\\n }\\n _indices_from_points(new_index_by_old_index, old_geometry) {\\n const index_attrib = old_geometry.index;\\n if (index_attrib != null) {\\n const old_indices = index_attrib.array;\\n const new_indices = [];\\n let old_index;\\n let new_index;\\n for (let i = 0; i < old_indices.length; i++) {\\n old_index = old_indices[i];\\n new_index = new_index_by_old_index[old_index];\\n if (new_index != null) {\\n new_indices.push(new_index);\\n }\\n }\\n return new_indices;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/modules/three/examples/jsm/utils/BufferGeometryUtils.js\\nfunction mergeBufferGeometries(geometries, useGroups = false) {\\n const isIndexed = geometries[0].index !== null;\\n const attributesUsed = new Set(Object.keys(geometries[0].attributes));\\n const morphAttributesUsed = new Set(Object.keys(geometries[0].morphAttributes));\\n const attributes = {};\\n const morphAttributes = {};\\n const morphTargetsRelative = geometries[0].morphTargetsRelative;\\n const mergedGeometry = new BufferGeometry();\\n let offset = 0;\\n for (let i = 0; i < geometries.length; ++i) {\\n const geometry = geometries[i];\\n let attributesCount = 0;\\n if (isIndexed !== (geometry.index !== null)) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + \\\\\\\". All geometries must have compatible attributes; make sure index attribute exists among all geometries, or in none of them.\\\\\\\");\\n return null;\\n }\\n for (const name in geometry.attributes) {\\n if (!attributesUsed.has(name)) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + '. All geometries must have compatible attributes; make sure \\\\\\\"' + name + '\\\\\\\" attribute exists among all geometries, or in none of them.');\\n return null;\\n }\\n if (attributes[name] === void 0)\\n attributes[name] = [];\\n attributes[name].push(geometry.attributes[name]);\\n attributesCount++;\\n }\\n if (attributesCount !== attributesUsed.size) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + \\\\\\\". Make sure all geometries have the same number of attributes.\\\\\\\");\\n return null;\\n }\\n if (morphTargetsRelative !== geometry.morphTargetsRelative) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + \\\\\\\". .morphTargetsRelative must be consistent throughout all geometries.\\\\\\\");\\n return null;\\n }\\n for (const name in geometry.morphAttributes) {\\n if (!morphAttributesUsed.has(name)) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + \\\\\\\". .morphAttributes must be consistent throughout all geometries.\\\\\\\");\\n return null;\\n }\\n if (morphAttributes[name] === void 0)\\n morphAttributes[name] = [];\\n morphAttributes[name].push(geometry.morphAttributes[name]);\\n }\\n mergedGeometry.userData.mergedUserData = mergedGeometry.userData.mergedUserData || [];\\n mergedGeometry.userData.mergedUserData.push(geometry.userData);\\n if (useGroups) {\\n let count;\\n if (isIndexed) {\\n count = geometry.index.count;\\n } else if (geometry.attributes.position !== void 0) {\\n count = geometry.attributes.position.count;\\n } else {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\" + i + \\\\\\\". The geometry must have either an index or a position attribute\\\\\\\");\\n return null;\\n }\\n mergedGeometry.addGroup(offset, count, i);\\n offset += count;\\n }\\n }\\n if (isIndexed) {\\n let indexOffset = 0;\\n const mergedIndex = [];\\n for (let i = 0; i < geometries.length; ++i) {\\n const index = geometries[i].index;\\n for (let j = 0; j < index.count; ++j) {\\n mergedIndex.push(index.getX(j) + indexOffset);\\n }\\n indexOffset += geometries[i].attributes.position.count;\\n }\\n mergedGeometry.setIndex(mergedIndex);\\n }\\n for (const name in attributes) {\\n const mergedAttribute = mergeBufferAttributes(attributes[name]);\\n if (!mergedAttribute) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\" + name + \\\\\\\" attribute.\\\\\\\");\\n return null;\\n }\\n mergedGeometry.setAttribute(name, mergedAttribute);\\n }\\n for (const name in morphAttributes) {\\n const numMorphTargets = morphAttributes[name][0].length;\\n if (numMorphTargets === 0)\\n break;\\n mergedGeometry.morphAttributes = mergedGeometry.morphAttributes || {};\\n mergedGeometry.morphAttributes[name] = [];\\n for (let i = 0; i < numMorphTargets; ++i) {\\n const morphAttributesToMerge = [];\\n for (let j = 0; j < morphAttributes[name].length; ++j) {\\n morphAttributesToMerge.push(morphAttributes[name][j][i]);\\n }\\n const mergedMorphAttribute = mergeBufferAttributes(morphAttributesToMerge);\\n if (!mergedMorphAttribute) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\" + name + \\\\\\\" morphAttribute.\\\\\\\");\\n return null;\\n }\\n mergedGeometry.morphAttributes[name].push(mergedMorphAttribute);\\n }\\n }\\n return mergedGeometry;\\n}\\nfunction mergeBufferAttributes(attributes) {\\n let TypedArray;\\n let itemSize;\\n let normalized;\\n let arrayLength = 0;\\n for (let i = 0; i < attributes.length; ++i) {\\n const attribute = attributes[i];\\n if (attribute.isInterleavedBufferAttribute) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. InterleavedBufferAttributes are not supported.\\\\\\\");\\n return null;\\n }\\n if (TypedArray === void 0)\\n TypedArray = attribute.array.constructor;\\n if (TypedArray !== attribute.array.constructor) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.array must be of consistent array types across matching attributes.\\\\\\\");\\n return null;\\n }\\n if (itemSize === void 0)\\n itemSize = attribute.itemSize;\\n if (itemSize !== attribute.itemSize) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.itemSize must be consistent across matching attributes.\\\\\\\");\\n return null;\\n }\\n if (normalized === void 0)\\n normalized = attribute.normalized;\\n if (normalized !== attribute.normalized) {\\n console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.normalized must be consistent across matching attributes.\\\\\\\");\\n return null;\\n }\\n arrayLength += attribute.array.length;\\n }\\n const array = new TypedArray(arrayLength);\\n let offset = 0;\\n for (let i = 0; i < attributes.length; ++i) {\\n array.set(attributes[i].array, offset);\\n offset += attributes[i].array.length;\\n }\\n return new BufferAttribute(array, itemSize, normalized);\\n}\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/util/IndexBuilder.ts\\nvar POSITION = \\\\\\\"position\\\\\\\";\\nvar CoreGeometryIndexBuilder = class {\\n static createIndexIfNone(geometry) {\\n if (!geometry.index) {\\n const position = geometry.getAttribute(POSITION);\\n if (position) {\\n const position_array = position.array;\\n geometry.setIndex(ArrayUtils.range(position_array.length / 3));\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/builders/Merge.ts\\nvar CoreGeometryBuilderMerge = class {\\n static merge(geometries) {\\n if (geometries.length === 0) {\\n return;\\n }\\n for (let geometry of geometries) {\\n CoreGeometryIndexBuilder.createIndexIfNone(geometry);\\n }\\n const core_geometries = geometries.map((geometry) => new CoreGeometry(geometry));\\n const indexed_attribute_names = core_geometries[0].indexedAttributeNames();\\n const new_values_by_attribute_name = {};\\n for (let indexed_attribute_name of indexed_attribute_names) {\\n const index_by_values = {};\\n const all_geometries_points = [];\\n for (let core_geometry of core_geometries) {\\n const geometry_points = core_geometry.points();\\n for (let point of geometry_points) {\\n all_geometries_points.push(point);\\n const value = point.indexedAttribValue(indexed_attribute_name);\\n index_by_values[value] != null ? index_by_values[value] : index_by_values[value] = Object.keys(index_by_values).length;\\n }\\n }\\n const values = Object.keys(index_by_values);\\n for (let point of all_geometries_points) {\\n const value = point.indexedAttribValue(indexed_attribute_name);\\n const new_index = index_by_values[value];\\n point.setAttribIndex(indexed_attribute_name, new_index);\\n }\\n new_values_by_attribute_name[indexed_attribute_name] = values;\\n }\\n const merged_geometry = mergeBufferGeometries(geometries);\\n const merged_core_geometry = new CoreGeometry(merged_geometry);\\n Object.keys(new_values_by_attribute_name).forEach((indexed_attribute_name) => {\\n const values = new_values_by_attribute_name[indexed_attribute_name];\\n merged_core_geometry.setIndexedAttributeValues(indexed_attribute_name, values);\\n });\\n if (merged_geometry) {\\n delete merged_geometry.userData.mergedUserData;\\n }\\n return merged_geometry;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/builders/Mesh.ts\\nvar CoreGeometryBuilderMesh = class extends CoreGeometryBuilderBase {\\n _filter_points(points2) {\\n const first_point = points2[0];\\n if (first_point) {\\n const geometry = first_point.geometry();\\n const indices = geometry.getIndex()?.array;\\n if (indices) {\\n const points_by_index = {};\\n for (let point of points2) {\\n points_by_index[point.index()] = point;\\n }\\n const filtered_points = [];\\n const index_length = indices.length;\\n let pt0;\\n let pt1;\\n let pt2;\\n for (let i = 0; i < index_length; i += 3) {\\n pt0 = points_by_index[indices[i + 0]];\\n pt1 = points_by_index[indices[i + 1]];\\n pt2 = points_by_index[indices[i + 2]];\\n if (pt0 && pt1 && pt2) {\\n filtered_points.push(pt0);\\n filtered_points.push(pt1);\\n filtered_points.push(pt2);\\n }\\n }\\n return filtered_points;\\n }\\n }\\n return [];\\n }\\n _indices_from_points(new_index_by_old_index, old_geometry) {\\n const index_attrib = old_geometry.index;\\n if (index_attrib != null) {\\n const old_indices = index_attrib.array;\\n const new_indices = [];\\n let old_index0;\\n let old_index1;\\n let old_index2;\\n let new_index0;\\n let new_index1;\\n let new_index2;\\n for (let i = 0; i < old_indices.length; i += 3) {\\n old_index0 = old_indices[i + 0];\\n old_index1 = old_indices[i + 1];\\n old_index2 = old_indices[i + 2];\\n new_index0 = new_index_by_old_index[old_index0];\\n new_index1 = new_index_by_old_index[old_index1];\\n new_index2 = new_index_by_old_index[old_index2];\\n if (new_index0 != null && new_index1 != null && new_index2 != null) {\\n new_indices.push(new_index0);\\n new_indices.push(new_index1);\\n new_indices.push(new_index2);\\n }\\n }\\n return new_indices;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/builders/LineSegments.ts\\nvar CoreGeometryBuilderLineSegments = class extends CoreGeometryBuilderBase {\\n _filter_points(points2) {\\n const first_point = points2[0];\\n if (first_point) {\\n const geometry = first_point.geometry();\\n const indices = geometry.getIndex()?.array;\\n if (indices) {\\n const points_by_index = {};\\n for (let point of points2) {\\n points_by_index[point.index()] = point;\\n }\\n const filtered_points = [];\\n const index_length = indices.length;\\n let pt0;\\n let pt1;\\n for (let i = 0; i < index_length; i += 2) {\\n pt0 = points_by_index[indices[i + 0]];\\n pt1 = points_by_index[indices[i + 1]];\\n if (pt0 && pt1) {\\n filtered_points.push(pt0);\\n filtered_points.push(pt1);\\n }\\n }\\n return filtered_points;\\n }\\n }\\n return [];\\n }\\n _indices_from_points(new_index_by_old_index, old_geometry) {\\n const index_attrib = old_geometry.index;\\n if (index_attrib != null) {\\n const old_indices = index_attrib.array;\\n const new_indices = [];\\n let old_index0;\\n let old_index1;\\n let new_index0;\\n let new_index1;\\n for (let i = 0; i < old_indices.length; i += 2) {\\n old_index0 = old_indices[i];\\n old_index1 = old_indices[i + 1];\\n new_index0 = new_index_by_old_index[old_index0];\\n new_index1 = new_index_by_old_index[old_index1];\\n if (new_index0 != null && new_index1 != null) {\\n new_indices.push(new_index0);\\n new_indices.push(new_index1);\\n }\\n }\\n return new_indices;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/ObjectUtils.ts\\nvar import_cloneDeep = __toModule(require_cloneDeep());\\nvar import_clone = __toModule(require_clone());\\nvar ObjectUtils = class {\\n static isEqual(object0, object1) {\\n if (CoreType.isObject(object0) && CoreType.isObject(object1)) {\\n const keys0 = Object.keys(object0);\\n const keys1 = Object.keys(object1);\\n if (!ArrayUtils.isEqual(keys0, keys1)) {\\n return false;\\n }\\n return JSON.stringify(object0) == JSON.stringify(object1);\\n }\\n return false;\\n }\\n static merge(object0, object1) {\\n return Object.assign(object0, object1);\\n }\\n static clone(value) {\\n return import_clone.default(value);\\n }\\n static cloneDeep(value) {\\n return import_cloneDeep.default(value);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Geometry.ts\\nvar IS_INSTANCE_KEY = \\\\\\\"isInstance\\\\\\\";\\nvar INDEX_ATTRIB_VALUES = \\\\\\\"indexed_attrib_values\\\\\\\";\\nvar CoreGeometry2 = class {\\n constructor(_geometry) {\\n this._geometry = _geometry;\\n }\\n geometry() {\\n return this._geometry;\\n }\\n uuid() {\\n return this._geometry.uuid;\\n }\\n boundingBox() {\\n return this._bounding_box = this._bounding_box || this._create_bounding_box();\\n }\\n _create_bounding_box() {\\n this._geometry.computeBoundingBox();\\n if (this._geometry.boundingBox) {\\n return this._geometry.boundingBox;\\n }\\n }\\n markAsInstance() {\\n this._geometry.userData[IS_INSTANCE_KEY] = true;\\n }\\n static markedAsInstance(geometry) {\\n return geometry.userData[IS_INSTANCE_KEY] === true;\\n }\\n markedAsInstance() {\\n return CoreGeometry2.markedAsInstance(this._geometry);\\n }\\n positionAttribName() {\\n let name = \\\\\\\"position\\\\\\\";\\n if (this.markedAsInstance()) {\\n name = \\\\\\\"instancePosition\\\\\\\";\\n }\\n return name;\\n }\\n computeVertexNormals() {\\n this._geometry.computeVertexNormals();\\n }\\n userDataAttribs() {\\n return this._geometry.userData[INDEX_ATTRIB_VALUES] = this._geometry.userData[INDEX_ATTRIB_VALUES] || {};\\n }\\n indexedAttributeNames() {\\n return Object.keys(this.userDataAttribs() || {});\\n }\\n userDataAttrib(name) {\\n name = CoreAttribute.remapName(name);\\n return this.userDataAttribs()[name];\\n }\\n isAttribIndexed(name) {\\n name = CoreAttribute.remapName(name);\\n return this.userDataAttrib(name) != null;\\n }\\n hasAttrib(name) {\\n if (name === \\\\\\\"ptnum\\\\\\\") {\\n return true;\\n }\\n name = CoreAttribute.remapName(name);\\n return this._geometry.attributes[name] != null;\\n }\\n attribType(name) {\\n if (this.isAttribIndexed(name)) {\\n return AttribType.STRING;\\n } else {\\n return AttribType.NUMERIC;\\n }\\n }\\n static attribNames(geometry) {\\n return Object.keys(geometry.attributes);\\n }\\n attribNames() {\\n return CoreGeometry2.attribNames(this._geometry);\\n }\\n static attribNamesMatchingMask(geometry, masks_string) {\\n const masks = CoreString.attribNames(masks_string);\\n const matching_attrib_names = [];\\n for (let attrib_name of this.attribNames(geometry)) {\\n for (let mask of masks) {\\n if (CoreString.matchMask(attrib_name, mask)) {\\n matching_attrib_names.push(attrib_name);\\n }\\n }\\n }\\n return ArrayUtils.uniq(matching_attrib_names);\\n }\\n attribSizes() {\\n const h = {};\\n for (let attrib_name of this.attribNames()) {\\n h[attrib_name] = this._geometry.attributes[attrib_name].itemSize;\\n }\\n return h;\\n }\\n attribSize(name) {\\n let attrib;\\n name = CoreAttribute.remapName(name);\\n if ((attrib = this._geometry.attributes[name]) != null) {\\n return attrib.itemSize;\\n } else {\\n if (name === \\\\\\\"ptnum\\\\\\\") {\\n return 1;\\n } else {\\n return 0;\\n }\\n }\\n }\\n setIndexedAttributeValues(name, values) {\\n this.userDataAttribs()[name] = values;\\n }\\n setIndexedAttribute(name, values, indices) {\\n this.setIndexedAttributeValues(name, values);\\n this._geometry.setAttribute(name, new Int32BufferAttribute(indices, 1));\\n }\\n addNumericAttrib(name, size = 1, default_value = 0) {\\n const values = [];\\n let attribute_added = false;\\n if (CoreType.isNumber(default_value)) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n for (let j = 0; j < size; j++) {\\n values.push(default_value);\\n }\\n }\\n attribute_added = true;\\n } else {\\n if (size > 1) {\\n if (CoreType.isArray(default_value)) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n for (let j = 0; j < size; j++) {\\n values.push(default_value[j]);\\n }\\n }\\n attribute_added = true;\\n } else {\\n const vec2 = default_value;\\n if (size == 2 && vec2.x != null && vec2.y != null) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n values.push(vec2.x);\\n values.push(vec2.y);\\n }\\n attribute_added = true;\\n }\\n const vec3 = default_value;\\n if (size == 3 && vec3.x != null && vec3.y != null && vec3.z != null) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n values.push(vec3.x);\\n values.push(vec3.y);\\n values.push(vec3.z);\\n }\\n attribute_added = true;\\n }\\n const col = default_value;\\n if (size == 3 && col.r != null && col.g != null && col.b != null) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n values.push(col.r);\\n values.push(col.g);\\n values.push(col.b);\\n }\\n attribute_added = true;\\n }\\n const vec4 = default_value;\\n if (size == 4 && vec4.x != null && vec4.y != null && vec4.z != null && vec4.w != null) {\\n for (let i = 0; i < this.pointsCount(); i++) {\\n values.push(vec4.x);\\n values.push(vec4.y);\\n values.push(vec4.z);\\n values.push(vec4.w);\\n }\\n attribute_added = true;\\n }\\n }\\n }\\n }\\n if (attribute_added) {\\n this._geometry.setAttribute(name.trim(), new Float32BufferAttribute(values, size));\\n } else {\\n console.warn(default_value);\\n throw `CoreGeometry.add_numeric_attrib error: no other default value allowed for now in add_numeric_attrib (default given: ${default_value})`;\\n }\\n }\\n initPositionAttribute(points_count, default_value) {\\n const values = [];\\n if (default_value == null) {\\n default_value = new Vector3();\\n }\\n for (let i = 0; i < points_count; i++) {\\n values.push(default_value.x);\\n values.push(default_value.y);\\n values.push(default_value.z);\\n }\\n return this._geometry.setAttribute(\\\\\\\"position\\\\\\\", new Float32BufferAttribute(values, 3));\\n }\\n addAttribute(name, attrib_data) {\\n switch (attrib_data.type()) {\\n case AttribType.STRING:\\n return console.log(\\\\\\\"TODO: to implement\\\\\\\");\\n case AttribType.NUMERIC:\\n return this.addNumericAttrib(name, attrib_data.size());\\n }\\n }\\n renameAttrib(old_name, new_name) {\\n if (this.isAttribIndexed(old_name)) {\\n this.userDataAttribs()[new_name] = ObjectUtils.clone(this.userDataAttribs()[old_name]);\\n delete this.userDataAttribs()[old_name];\\n }\\n const old_attrib = this._geometry.getAttribute(old_name);\\n this._geometry.setAttribute(new_name.trim(), new Float32BufferAttribute(old_attrib.array, old_attrib.itemSize));\\n return this._geometry.deleteAttribute(old_name);\\n }\\n deleteAttribute(name) {\\n if (this.isAttribIndexed(name)) {\\n delete this.userDataAttribs()[name];\\n }\\n return this._geometry.deleteAttribute(name);\\n }\\n clone() {\\n return CoreGeometry2.clone(this._geometry);\\n }\\n static clone(src_geometry) {\\n let src_userData;\\n const new_geometry = src_geometry.clone();\\n if ((src_userData = src_geometry.userData) != null) {\\n new_geometry.userData = ObjectUtils.cloneDeep(src_userData);\\n }\\n return new_geometry;\\n }\\n pointsCount() {\\n return CoreGeometry2.pointsCount(this._geometry);\\n }\\n static pointsCount(geometry) {\\n let position;\\n let count = 0;\\n const core_geometry = new this(geometry);\\n let position_attrib_name = \\\\\\\"position\\\\\\\";\\n if (core_geometry.markedAsInstance()) {\\n position_attrib_name = \\\\\\\"instancePosition\\\\\\\";\\n }\\n if ((position = geometry.getAttribute(position_attrib_name)) != null) {\\n let array;\\n if ((array = position.array) != null) {\\n count = array.length / 3;\\n }\\n }\\n return count;\\n }\\n points() {\\n return this.pointsFromGeometry();\\n }\\n pointsFromGeometry() {\\n const points2 = [];\\n const positionAttrib = this._geometry.getAttribute(this.positionAttribName());\\n if (positionAttrib != null) {\\n const count = positionAttrib.array.length / 3;\\n for (let i = 0; i < count; i++) {\\n const point = new CorePoint(this, i);\\n points2.push(point);\\n }\\n }\\n return points2;\\n }\\n static geometryFromPoints(points2, object_type) {\\n switch (object_type) {\\n case ObjectType.MESH:\\n return this._mesh_builder.from_points(points2);\\n case ObjectType.POINTS:\\n return this._points_builder.from_points(points2);\\n case ObjectType.LINE_SEGMENTS:\\n return this._lines_segment_builder.from_points(points2);\\n case ObjectType.OBJECT3D:\\n return null;\\n case ObjectType.GROUP:\\n return null;\\n case ObjectType.LOD:\\n return null;\\n }\\n TypeAssert.unreachable(object_type);\\n }\\n static mergeGeometries(geometries) {\\n return CoreGeometryBuilderMerge.merge(geometries);\\n }\\n static merge_geometries(geometries) {\\n return CoreGeometryBuilderMerge.merge(geometries);\\n }\\n segments() {\\n const index = this.geometry().index?.array || [];\\n return ArrayUtils.chunk(index, 2);\\n }\\n faces() {\\n return this.facesFromGeometry();\\n }\\n facesFromGeometry() {\\n const index_array = this.geometry().index?.array || [];\\n const faces_count = index_array.length / 3;\\n return ArrayUtils.range(faces_count).map((i) => new CoreFace(this, i));\\n }\\n};\\nvar CoreGeometry = CoreGeometry2;\\nCoreGeometry._mesh_builder = new CoreGeometryBuilderMesh();\\nCoreGeometry._points_builder = new CoreGeometryBuilderPoints();\\nCoreGeometry._lines_segment_builder = new CoreGeometryBuilderLineSegments();\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Material.ts\\nvar CustomMaterialName;\\n(function(CustomMaterialName2) {\\n CustomMaterialName2[\\\\\\\"customDistanceMaterial\\\\\\\"] = \\\\\\\"customDistanceMaterial\\\\\\\";\\n CustomMaterialName2[\\\\\\\"customDepthMaterial\\\\\\\"] = \\\\\\\"customDepthMaterial\\\\\\\";\\n CustomMaterialName2[\\\\\\\"customDepthDOFMaterial\\\\\\\"] = \\\\\\\"customDepthDOFMaterial\\\\\\\";\\n})(CustomMaterialName || (CustomMaterialName = {}));\\nvar RENDER_HOOK_USER_DATA_KEY = \\\\\\\"POLY_render_hook\\\\\\\";\\nvar EMPTY_RENDER_HOOK = (renderer, scene, camera, geometry, material, group) => {\\n};\\nvar CoreMaterial = class {\\n static node(scene, material) {\\n return scene.node(material.name);\\n }\\n static clone(src_material) {\\n const cloned_material = src_material.clone();\\n const src_uniforms = src_material.uniforms;\\n if (src_uniforms) {\\n cloned_material.uniforms = UniformsUtils.clone(src_uniforms);\\n }\\n return cloned_material;\\n }\\n static add_user_data_render_hook(material, render_hook) {\\n material.userData[RENDER_HOOK_USER_DATA_KEY] = render_hook;\\n }\\n static apply_render_hook(object, material) {\\n if (material.userData) {\\n const render_hook = material.userData[RENDER_HOOK_USER_DATA_KEY];\\n if (render_hook) {\\n object.onBeforeRender = (renderer, scene, camera, geometry, material2, group) => {\\n render_hook(renderer, scene, camera, geometry, material2, group, object);\\n };\\n return;\\n }\\n }\\n object.onBeforeRender = EMPTY_RENDER_HOOK;\\n }\\n static applyCustomMaterials(object, material) {\\n const material_with_custom = material;\\n if (material_with_custom.customMaterials) {\\n for (let name of Object.keys(material_with_custom.customMaterials)) {\\n const mat_name = name;\\n const custom_material = material_with_custom.customMaterials[mat_name];\\n if (custom_material) {\\n object[mat_name] = custom_material;\\n custom_material.needsUpdate = true;\\n }\\n }\\n }\\n }\\n static assign_custom_uniforms(mat, uniform_name, uniform_value) {\\n const material = mat;\\n if (material.customMaterials) {\\n for (let name of Object.keys(material.customMaterials)) {\\n const mat_name = name;\\n const custom_material = material.customMaterials[mat_name];\\n if (custom_material) {\\n custom_material.uniforms[uniform_name].value = uniform_value;\\n }\\n }\\n }\\n }\\n static init_custom_material_uniforms(mat, uniform_name, uniform_value) {\\n const material = mat;\\n if (material.customMaterials) {\\n for (let name of Object.keys(material.customMaterials)) {\\n const mat_name = name;\\n const custom_material = material.customMaterials[mat_name];\\n if (custom_material) {\\n custom_material.uniforms[uniform_name] = custom_material.uniforms[uniform_name] || uniform_value;\\n }\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Object.ts\\nvar PTNUM2 = \\\\\\\"ptnum\\\\\\\";\\nvar NAME_ATTR = \\\\\\\"name\\\\\\\";\\nvar ATTRIBUTES = \\\\\\\"attributes\\\\\\\";\\nvar CoreObject = class extends CoreEntity {\\n constructor(_object, index) {\\n super(index);\\n this._object = _object;\\n if (this._object.userData[ATTRIBUTES] == null) {\\n this._object.userData[ATTRIBUTES] = {};\\n }\\n }\\n object() {\\n return this._object;\\n }\\n geometry() {\\n return this._object.geometry;\\n }\\n coreGeometry() {\\n const geo = this.geometry();\\n if (geo) {\\n return new CoreGeometry(geo);\\n } else {\\n return null;\\n }\\n }\\n points() {\\n return this.coreGeometry()?.points() || [];\\n }\\n pointsFromGroup(group) {\\n if (group) {\\n const indices = CoreString.indices(group);\\n if (indices) {\\n const points2 = this.points();\\n return indices.map((i) => points2[i]);\\n } else {\\n return [];\\n }\\n } else {\\n return this.points();\\n }\\n }\\n static isInGroup(groupString, object) {\\n const group = groupString.trim();\\n if (group.length == 0) {\\n return true;\\n }\\n const elements = group.split(\\\\\\\"=\\\\\\\");\\n const attribNameWithPrefix = elements[0];\\n if (attribNameWithPrefix[0] == \\\\\\\"@\\\\\\\") {\\n const attribName = attribNameWithPrefix.substr(1);\\n const expectedAttribValue = elements[1];\\n const currentAttribValue = this.attribValue(object, attribName);\\n return expectedAttribValue == currentAttribValue;\\n }\\n return false;\\n }\\n computeVertexNormals() {\\n this.coreGeometry()?.computeVertexNormals();\\n }\\n static _convert_array_to_vector(value) {\\n switch (value.length) {\\n case 1:\\n return value[0];\\n case 2:\\n return new Vector2(value[0], value[1]);\\n case 3:\\n return new Vector3(value[0], value[1], value[2]);\\n case 4:\\n return new Vector4(value[0], value[1], value[2], value[3]);\\n }\\n }\\n static addAttribute(object, attrib_name, value) {\\n if (CoreType.isArray(value)) {\\n const converted_value = this._convert_array_to_vector(value);\\n if (!converted_value) {\\n const message = `attribute_value invalid`;\\n console.error(message, value);\\n throw new Error(message);\\n }\\n }\\n const data = value;\\n const user_data = object.userData;\\n user_data[ATTRIBUTES] = user_data[ATTRIBUTES] || {};\\n user_data[ATTRIBUTES][attrib_name] = data;\\n }\\n addAttribute(name, value) {\\n CoreObject.addAttribute(this._object, name, value);\\n }\\n addNumericAttrib(name, value) {\\n this.addAttribute(name, value);\\n }\\n setAttribValue(name, value) {\\n this.addAttribute(name, value);\\n }\\n addNumericVertexAttrib(name, size, default_value) {\\n if (default_value == null) {\\n default_value = CoreAttribute.default_value(size);\\n }\\n this.coreGeometry()?.addNumericAttrib(name, size, default_value);\\n }\\n attributeNames() {\\n return Object.keys(this._object.userData[ATTRIBUTES]);\\n }\\n attribNames() {\\n return this.attributeNames();\\n }\\n hasAttrib(name) {\\n return this.attributeNames().includes(name);\\n }\\n renameAttrib(old_name, new_name) {\\n const current_value = this.attribValue(old_name);\\n if (current_value != null) {\\n this.addAttribute(new_name, current_value);\\n this.deleteAttribute(old_name);\\n } else {\\n console.warn(`attribute ${old_name} not found`);\\n }\\n }\\n deleteAttribute(name) {\\n delete this._object.userData[ATTRIBUTES][name];\\n }\\n static attribValue(object, name, index = 0, target) {\\n if (name === PTNUM2) {\\n return index;\\n }\\n if (object.userData && object.userData[ATTRIBUTES]) {\\n const val = object.userData[ATTRIBUTES][name];\\n if (val == null) {\\n if (name == NAME_ATTR) {\\n return object.name;\\n }\\n } else {\\n if (CoreType.isArray(val) && target) {\\n target.fromArray(val);\\n return target;\\n }\\n }\\n return val;\\n }\\n if (name == NAME_ATTR) {\\n return object.name;\\n }\\n }\\n static stringAttribValue(object, name, index = 0) {\\n const str = this.attribValue(object, name, index);\\n if (str != null) {\\n if (CoreType.isString(str)) {\\n return str;\\n } else {\\n return `${str}`;\\n }\\n }\\n }\\n attribValue(name, target) {\\n return CoreObject.attribValue(this._object, name, this._index, target);\\n }\\n stringAttribValue(name) {\\n return CoreObject.stringAttribValue(this._object, name, this._index);\\n }\\n name() {\\n return this.attribValue(NAME_ATTR);\\n }\\n humanType() {\\n return CoreConstant.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name];\\n }\\n attribTypes() {\\n const h = {};\\n for (let attrib_name of this.attribNames()) {\\n const type = this.attribType(attrib_name);\\n if (type != null) {\\n h[attrib_name] = type;\\n }\\n }\\n return h;\\n }\\n attribType(name) {\\n const val = this.attribValue(name);\\n if (CoreType.isString(val)) {\\n return AttribType.STRING;\\n } else {\\n return AttribType.NUMERIC;\\n }\\n }\\n attribSizes() {\\n const h = {};\\n for (let attrib_name of this.attribNames()) {\\n const size = this.attribSize(attrib_name);\\n if (size != null) {\\n h[attrib_name] = size;\\n }\\n }\\n return h;\\n }\\n attribSize(name) {\\n const val = this.attribValue(name);\\n if (val == null) {\\n return null;\\n }\\n return CoreAttribute.attribSizeFromValue(val);\\n }\\n clone() {\\n return CoreObject.clone(this._object);\\n }\\n static clone(src_object) {\\n const new_object = src_object.clone();\\n var sourceLookup = new Map();\\n var cloneLookup = new Map();\\n CoreObject.parallelTraverse(src_object, new_object, function(sourceNode, clonedNode) {\\n sourceLookup.set(clonedNode, sourceNode);\\n cloneLookup.set(sourceNode, clonedNode);\\n });\\n new_object.traverse(function(node) {\\n const src_node = sourceLookup.get(node);\\n const mesh_node = node;\\n if (mesh_node.geometry) {\\n const src_node_geometry = src_node.geometry;\\n mesh_node.geometry = CoreGeometry.clone(src_node_geometry);\\n const mesh_node_geometry = mesh_node.geometry;\\n if (mesh_node_geometry.userData) {\\n mesh_node_geometry.userData = ObjectUtils.cloneDeep(src_node_geometry.userData);\\n }\\n }\\n if (mesh_node.material) {\\n mesh_node.material = src_node.material;\\n CoreMaterial.applyCustomMaterials(node, mesh_node.material);\\n const material_with_color = mesh_node.material;\\n if (material_with_color.color == null) {\\n material_with_color.color = new Color(1, 1, 1);\\n }\\n }\\n if (src_object.userData) {\\n node.userData = ObjectUtils.cloneDeep(src_node.userData);\\n }\\n const src_node_with_animations = src_node;\\n if (src_node_with_animations.animations) {\\n node.animations = src_node_with_animations.animations.map((animation) => animation.clone());\\n }\\n const skinned_node = node;\\n if (skinned_node.isSkinnedMesh) {\\n var clonedMesh = skinned_node;\\n var sourceMesh = src_node;\\n var sourceBones = sourceMesh.skeleton.bones;\\n clonedMesh.skeleton = sourceMesh.skeleton.clone();\\n clonedMesh.bindMatrix.copy(sourceMesh.bindMatrix);\\n const new_bones = sourceBones.map(function(bone) {\\n return cloneLookup.get(bone);\\n });\\n clonedMesh.skeleton.bones = new_bones;\\n clonedMesh.bind(clonedMesh.skeleton, clonedMesh.bindMatrix);\\n }\\n });\\n return new_object;\\n }\\n static parallelTraverse(a, b, callback) {\\n callback(a, b);\\n for (var i = 0; i < a.children.length; i++) {\\n this.parallelTraverse(a.children[i], b.children[i], callback);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Geometry.ts\\nvar GeometryContainer = class extends TypedContainer {\\n coreContentCloned() {\\n if (this._content) {\\n return this._content.clone();\\n }\\n }\\n set_content(content) {\\n super.set_content(content);\\n }\\n firstObject() {\\n if (this._content) {\\n return this._content.objects()[0];\\n }\\n }\\n firstCoreObject() {\\n const object = this.firstObject();\\n if (object) {\\n return new CoreObject(object, 0);\\n }\\n }\\n firstGeometry() {\\n const object = this.firstObject();\\n if (object) {\\n return object.geometry;\\n } else {\\n return null;\\n }\\n }\\n objectsCount() {\\n if (this._content) {\\n return this._content.objects().length;\\n } else {\\n return 0;\\n }\\n }\\n objectsVisibleCount() {\\n let count = 0;\\n if (this._content) {\\n const objects = this._content.objects();\\n for (let object of objects) {\\n if (object.visible) {\\n count++;\\n }\\n }\\n }\\n return count;\\n }\\n objectsCountByType() {\\n const count_by_type = {};\\n const core_group = this._content;\\n if (this._content && core_group) {\\n for (let core_object of core_group.coreObjects()) {\\n const human_type = core_object.humanType();\\n if (count_by_type[human_type] == null) {\\n count_by_type[human_type] = 0;\\n }\\n count_by_type[human_type] += 1;\\n }\\n }\\n return count_by_type;\\n }\\n objectsNamesByType() {\\n const names_by_type = {};\\n const core_group = this._content;\\n if (this._content && core_group) {\\n for (let core_object of core_group.coreObjects()) {\\n const human_type = core_object.humanType();\\n names_by_type[human_type] = names_by_type[human_type] || [];\\n names_by_type[human_type].push(core_object.name());\\n }\\n }\\n return names_by_type;\\n }\\n pointAttributeNames() {\\n let names = [];\\n const geometry = this.firstGeometry();\\n if (geometry) {\\n names = Object.keys(geometry.attributes);\\n }\\n return names;\\n }\\n pointAttributeSizesByName() {\\n let sizes_by_name = {};\\n const geometry = this.firstGeometry();\\n if (geometry) {\\n Object.keys(geometry.attributes).forEach((attrib_name) => {\\n const attrib = geometry.attributes[attrib_name];\\n sizes_by_name[attrib_name] = attrib.itemSize;\\n });\\n }\\n return sizes_by_name;\\n }\\n objectAttributeSizesByName() {\\n let sizes_by_name = {};\\n const core_object = this.firstCoreObject();\\n if (core_object) {\\n const attribNames = core_object.attribNames();\\n for (let name of attribNames) {\\n const size = core_object.attribSize(name);\\n if (size != null) {\\n sizes_by_name[name] = size;\\n }\\n }\\n }\\n return sizes_by_name;\\n }\\n pointAttributeTypesByName() {\\n let types_by_name = {};\\n const geometry = this.firstGeometry();\\n if (geometry) {\\n const core_geo = new CoreGeometry(geometry);\\n Object.keys(geometry.attributes).forEach((attrib_name) => {\\n types_by_name[attrib_name] = core_geo.attribType(attrib_name);\\n });\\n }\\n return types_by_name;\\n }\\n objectAttributeTypesByName() {\\n let types_by_name = {};\\n const core_object = this.firstCoreObject();\\n if (core_object) {\\n for (let name of core_object.attribNames()) {\\n types_by_name[name] = core_object.attribType(name);\\n }\\n }\\n return types_by_name;\\n }\\n objectAttributeNames() {\\n let names = [];\\n const object = this.firstObject();\\n if (object) {\\n names = Object.keys(object.userData[\\\\\\\"attributes\\\\\\\"] || {});\\n }\\n return names;\\n }\\n pointsCount() {\\n if (this._content) {\\n return this._content.pointsCount();\\n } else {\\n return 0;\\n }\\n }\\n totalPointsCount() {\\n if (this._content) {\\n return this._content.totalPointsCount();\\n } else {\\n return 0;\\n }\\n }\\n objectsData() {\\n if (this._content) {\\n return this._content.objectsData();\\n } else {\\n return [];\\n }\\n }\\n boundingBox(forceUpdate = false) {\\n return this._content.boundingBox(forceUpdate);\\n }\\n center() {\\n return this._content.center();\\n }\\n size() {\\n return this._content.size();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Gl.ts\\nvar GlContainer = class extends TypedContainer {\\n object() {\\n return this._content;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Js.ts\\nvar JsContainer = class extends TypedContainer {\\n object() {\\n return this._content;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Manager.ts\\nvar ManagerContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Material.ts\\nvar MaterialContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n set_material(material) {\\n if (this._content != null) {\\n this._content.dispose();\\n }\\n this.set_content(material);\\n }\\n has_material() {\\n return this.has_content();\\n }\\n material() {\\n return this.content();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Object.ts\\nvar ObjectContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n set_object(object) {\\n return this.set_content(object);\\n }\\n has_object() {\\n return this.has_content();\\n }\\n object() {\\n return this.content();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Texture.ts\\nvar TextureContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n texture() {\\n return this._content;\\n }\\n coreContent() {\\n return this._content;\\n }\\n coreContentCloned() {\\n const texture = this._content?.clone();\\n if (texture) {\\n texture.needsUpdate = true;\\n }\\n return texture;\\n }\\n object() {\\n return this.texture();\\n }\\n infos() {\\n if (this._content != null) {\\n return [this._content];\\n }\\n }\\n resolution() {\\n if (this._content) {\\n const image = this._content.image;\\n if (image) {\\n if (image instanceof HTMLImageElement || image instanceof Image || image instanceof ImageData || image instanceof HTMLCanvasElement) {\\n return [image.width, image.height];\\n }\\n if (image.data && image.width != null && image.height != null) {\\n return [image.width, image.height];\\n }\\n const video = image;\\n return [video.videoWidth, video.videoHeight];\\n }\\n }\\n return [-1, -1];\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/PostProcess.ts\\nvar PostProcessContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n render_pass() {\\n return this._content;\\n }\\n object(options = {}) {\\n return this.render_pass();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/Rop.ts\\nvar RopContainer = class extends TypedContainer {\\n set_content(content) {\\n super.set_content(content);\\n }\\n renderer() {\\n return this._content;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/containers/utils/ContainerMap.ts\\nvar ContainerClassMap = {\\n [NodeContext.ANIM]: AnimationContainer,\\n [NodeContext.COP]: TextureContainer,\\n [NodeContext.EVENT]: EventContainer,\\n [NodeContext.GL]: GlContainer,\\n [NodeContext.JS]: JsContainer,\\n [NodeContext.MANAGER]: ManagerContainer,\\n [NodeContext.MAT]: MaterialContainer,\\n [NodeContext.OBJ]: ObjectContainer,\\n [NodeContext.POST]: PostProcessContainer,\\n [NodeContext.ROP]: RopContainer,\\n [NodeContext.SOP]: GeometryContainer\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/ContainerController.ts\\nvar TypedContainerController = class {\\n constructor(node) {\\n this.node = node;\\n this._callbacks = [];\\n this._callbacks_tmp = [];\\n const container_class = ContainerClassMap[node.context()];\\n this._container = new container_class(this.node);\\n }\\n container() {\\n return this._container;\\n }\\n async compute() {\\n if (this.node.flags?.bypass?.active()) {\\n const container = await this.requestInputContainer(0) || this._container;\\n this.node.cookController.endCook();\\n return container;\\n }\\n if (this.node.isDirty()) {\\n return new Promise((resolve, reject) => {\\n this._callbacks.push(resolve);\\n this.node.cookController.cookMain();\\n });\\n }\\n return this._container;\\n }\\n async requestInputContainer(input_index) {\\n const inputNode = this.node.io.inputs.input(input_index);\\n if (inputNode) {\\n return await inputNode.compute();\\n } else {\\n this.node.states.error.set(`input ${input_index} required`);\\n this.notifyRequesters();\\n return null;\\n }\\n }\\n notifyRequesters(container) {\\n this._callbacks_tmp = this._callbacks.slice();\\n this._callbacks.splice(0, this._callbacks.length);\\n if (!container) {\\n container = this.node.containerController.container();\\n }\\n let callback;\\n while (callback = this._callbacks_tmp.pop()) {\\n callback(container);\\n }\\n this.node.scene().cookController.removeNode(this.node);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/cook/PerformanceController.ts\\nvar performance = Poly.performance.performanceManager();\\nvar NodeCookPerformanceformanceController = class {\\n constructor(cookController) {\\n this.cookController = cookController;\\n this._inputs_start = 0;\\n this._params_start = 0;\\n this._cook_start = 0;\\n this._cooksCount = 0;\\n this._data = {\\n inputsTime: 0,\\n paramsTime: 0,\\n cookTime: 0\\n };\\n }\\n cooksCount() {\\n return this._cooksCount;\\n }\\n data2() {\\n return this._data;\\n }\\n active() {\\n return this.cookController.performanceRecordStarted();\\n }\\n recordInputsStart() {\\n if (this.active()) {\\n this._inputs_start = performance.now();\\n }\\n }\\n recordInputsEnd() {\\n if (this.active()) {\\n this._data.inputsTime = performance.now() - this._inputs_start;\\n }\\n }\\n recordParamsStart() {\\n if (this.active()) {\\n this._params_start = performance.now();\\n }\\n }\\n recordParamsEnd() {\\n if (this.active()) {\\n this._data.paramsTime = performance.now() - this._params_start;\\n }\\n }\\n recordCookStart() {\\n if (this.active()) {\\n this._cook_start = performance.now();\\n }\\n }\\n recordCookEnd() {\\n if (this.active()) {\\n this._data.cookTime = performance.now() - this._cook_start;\\n this._cooksCount += 1;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/CookController.ts\\nvar NodeCookController = class {\\n constructor(node) {\\n this.node = node;\\n this._cooking = false;\\n this._performanceController = new NodeCookPerformanceformanceController(this);\\n this._inputs_evaluation_required = true;\\n this._core_performance = this.node.scene().performance;\\n }\\n performanceRecordStarted() {\\n return this._core_performance.started();\\n }\\n disallowInputsEvaluation() {\\n this._inputs_evaluation_required = false;\\n }\\n isCooking() {\\n return this._cooking === true;\\n }\\n _start_cook_if_no_errors(input_contents) {\\n if (this.node.states.error.active()) {\\n this.endCook();\\n } else {\\n try {\\n this._performanceController.recordCookStart();\\n this.node.cook(input_contents);\\n } catch (e) {\\n this.node.states.error.set(`node internal error: '${e}'.`);\\n Poly.warn(e);\\n this.endCook();\\n }\\n }\\n }\\n async cookMain() {\\n if (this.isCooking()) {\\n return;\\n }\\n this._initCookingState();\\n this.node.states.error.clear();\\n this.node.scene().cookController.addNode(this.node);\\n let input_contents;\\n if (this._inputs_evaluation_required) {\\n input_contents = await this._evaluateInputs();\\n } else {\\n input_contents = [];\\n }\\n if (this.node.params.paramsEvalRequired()) {\\n await this._evaluateParams();\\n }\\n this._start_cook_if_no_errors(input_contents);\\n }\\n async cookMainWithoutInputs() {\\n this.node.scene().cookController.addNode(this.node);\\n if (this.isCooking()) {\\n Poly.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\", this.node.path());\\n return;\\n }\\n this._initCookingState();\\n this.node.states.error.clear();\\n if (this.node.params.paramsEvalRequired()) {\\n await this._evaluateParams();\\n }\\n this._start_cook_if_no_errors([]);\\n }\\n endCook(message) {\\n this._finalizeCookPerformance();\\n const dirty_timestamp = this.node.dirtyController.dirtyTimestamp();\\n if (dirty_timestamp == null || dirty_timestamp === this._cooking_dirty_timestamp) {\\n this.node.removeDirtyState();\\n this._terminateCookProcess();\\n } else {\\n Poly.log(\\\\\\\"COOK AGAIN\\\\\\\", dirty_timestamp, this._cooking_dirty_timestamp, this.node.path());\\n this._cooking = false;\\n this.cookMain();\\n }\\n }\\n _initCookingState() {\\n this._cooking = true;\\n this._cooking_dirty_timestamp = this.node.dirtyController.dirtyTimestamp();\\n }\\n _terminateCookProcess() {\\n if (this.isCooking()) {\\n this._cooking = false;\\n this.node.containerController.notifyRequesters();\\n this._run_on_cook_complete_hooks();\\n }\\n }\\n async _evaluateInputs() {\\n this._performanceController.recordInputsStart();\\n let input_containers = [];\\n const io_inputs = this.node.io.inputs;\\n if (this._inputs_evaluation_required) {\\n if (io_inputs.is_any_input_dirty()) {\\n input_containers = await io_inputs.eval_required_inputs();\\n } else {\\n input_containers = await io_inputs.containers_without_evaluation();\\n }\\n }\\n const inputs = io_inputs.inputs();\\n const input_contents = [];\\n let input_container;\\n for (let i = 0; i < inputs.length; i++) {\\n input_container = input_containers[i];\\n if (input_container) {\\n if (io_inputs.cloneRequired(i)) {\\n input_contents[i] = input_container.coreContentCloned();\\n } else {\\n input_contents[i] = input_container.coreContent();\\n }\\n }\\n }\\n this._performanceController.recordInputsEnd();\\n return input_contents;\\n }\\n async _evaluateParams() {\\n this._performanceController.recordParamsStart();\\n await this.node.params.evalAll();\\n this._performanceController.recordParamsEnd();\\n }\\n cooksCount() {\\n return this._performanceController.cooksCount();\\n }\\n cookTime() {\\n return this._performanceController.data2().cookTime;\\n }\\n _finalizeCookPerformance() {\\n if (!this._core_performance.started()) {\\n return;\\n }\\n this._performanceController.recordCookEnd();\\n this._core_performance.record_node_cook_data(this.node, this._performanceController.data2());\\n }\\n registerOnCookEnd(callbackName, callback) {\\n this._on_cook_complete_hook_names = this._on_cook_complete_hook_names || [];\\n this._on_cook_complete_hooks = this._on_cook_complete_hooks || [];\\n this._on_cook_complete_hook_names.push(callbackName);\\n this._on_cook_complete_hooks.push(callback);\\n }\\n deregisterOnCookEnd(callbackName) {\\n if (!this._on_cook_complete_hook_names || !this._on_cook_complete_hooks) {\\n return;\\n }\\n const index = this._on_cook_complete_hook_names?.indexOf(callbackName);\\n this._on_cook_complete_hook_names.splice(index, 1);\\n this._on_cook_complete_hooks.splice(index, 1);\\n }\\n _run_on_cook_complete_hooks() {\\n if (this._on_cook_complete_hooks) {\\n for (let hook of this._on_cook_complete_hooks) {\\n hook();\\n }\\n }\\n }\\n onCookEndCallbackNames() {\\n return this._on_cook_complete_hook_names;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/Serializer.ts\\nvar NodeSerializer = class {\\n constructor(node) {\\n this.node = node;\\n }\\n toJSON(include_param_components = false) {\\n const data = {\\n name: this.node.name(),\\n type: this.node.type(),\\n graph_node_id: this.node.graphNodeId(),\\n is_dirty: this.node.isDirty(),\\n ui_data_json: this.node.uiData.toJSON(),\\n error_message: this.node.states.error.message(),\\n children: this.childrenIds(),\\n maxInputsCount: this.maxInputsCount(),\\n inputs: this.inputIds(),\\n input_connection_output_indices: this.inputConnectionOutputIndices(),\\n named_input_connection_points: this.namedInputConnectionPoints(),\\n named_output_connection_points: this.namedOutputConnectionPoints(),\\n param_ids: this.to_json_params(include_param_components),\\n override_cloned_state_allowed: this.node.io.inputs.overrideClonedStateAllowed(),\\n inputs_clone_required_states: this.node.io.inputs.cloneRequiredStates(),\\n flags: {\\n display: this.node.flags?.display?.active(),\\n bypass: this.node.flags?.bypass?.active(),\\n optimize: this.node.flags?.optimize?.active()\\n },\\n selection: void 0\\n };\\n if (this.node.childrenAllowed() && this.node.childrenController) {\\n data[\\\\\\\"selection\\\\\\\"] = this.node.childrenController.selection.toJSON();\\n }\\n return data;\\n }\\n childrenIds() {\\n return this.node.children().map((node) => node.graphNodeId());\\n }\\n maxInputsCount() {\\n return this.node.io.inputs.maxInputsCount();\\n }\\n inputIds() {\\n return this.node.io.inputs.inputs().map((node) => node != null ? node.graphNodeId() : void 0);\\n }\\n inputConnectionOutputIndices() {\\n return this.node.io.connections.inputConnections()?.map((connection) => connection != null ? connection.output_index : void 0);\\n }\\n namedInputConnectionPoints() {\\n return this.node.io.inputs.namedInputConnectionPoints().map((i) => i.toJSON());\\n }\\n namedOutputConnectionPoints() {\\n return this.node.io.outputs.namedOutputConnectionPoints().map((o) => o.toJSON());\\n }\\n to_json_params_from_names(param_names, include_components = false) {\\n return param_names.map((param_name) => {\\n return this.node.params.get(param_name).graphNodeId();\\n });\\n }\\n to_json_params(include_components = false) {\\n return this.to_json_params_from_names(this.node.params.names, include_components);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/ParamType.ts\\nvar ParamType;\\n(function(ParamType2) {\\n ParamType2[\\\\\\\"BOOLEAN\\\\\\\"] = \\\\\\\"boolean\\\\\\\";\\n ParamType2[\\\\\\\"BUTTON\\\\\\\"] = \\\\\\\"button\\\\\\\";\\n ParamType2[\\\\\\\"COLOR\\\\\\\"] = \\\\\\\"color\\\\\\\";\\n ParamType2[\\\\\\\"FLOAT\\\\\\\"] = \\\\\\\"float\\\\\\\";\\n ParamType2[\\\\\\\"FOLDER\\\\\\\"] = \\\\\\\"folder\\\\\\\";\\n ParamType2[\\\\\\\"INTEGER\\\\\\\"] = \\\\\\\"integer\\\\\\\";\\n ParamType2[\\\\\\\"OPERATOR_PATH\\\\\\\"] = \\\\\\\"operator_path\\\\\\\";\\n ParamType2[\\\\\\\"PARAM_PATH\\\\\\\"] = \\\\\\\"param_path\\\\\\\";\\n ParamType2[\\\\\\\"NODE_PATH\\\\\\\"] = \\\\\\\"node_path\\\\\\\";\\n ParamType2[\\\\\\\"RAMP\\\\\\\"] = \\\\\\\"ramp\\\\\\\";\\n ParamType2[\\\\\\\"STRING\\\\\\\"] = \\\\\\\"string\\\\\\\";\\n ParamType2[\\\\\\\"VECTOR2\\\\\\\"] = \\\\\\\"vector2\\\\\\\";\\n ParamType2[\\\\\\\"VECTOR3\\\\\\\"] = \\\\\\\"vector3\\\\\\\";\\n ParamType2[\\\\\\\"VECTOR4\\\\\\\"] = \\\\\\\"vector4\\\\\\\";\\n})(ParamType || (ParamType = {}));\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/ParamEvent.ts\\nvar ParamEvent;\\n(function(ParamEvent2) {\\n ParamEvent2[\\\\\\\"VISIBLE_UPDATED\\\\\\\"] = \\\\\\\"param_visible_updated\\\\\\\";\\n ParamEvent2[\\\\\\\"RAW_INPUT_UPDATED\\\\\\\"] = \\\\\\\"raw_input_updated\\\\\\\";\\n ParamEvent2[\\\\\\\"VALUE_UPDATED\\\\\\\"] = \\\\\\\"param_value_updated\\\\\\\";\\n ParamEvent2[\\\\\\\"EXPRESSION_UPDATED\\\\\\\"] = \\\\\\\"param_expression_update\\\\\\\";\\n ParamEvent2[\\\\\\\"ERROR_UPDATED\\\\\\\"] = \\\\\\\"param_error_updated\\\\\\\";\\n ParamEvent2[\\\\\\\"DELETED\\\\\\\"] = \\\\\\\"param_deleted\\\\\\\";\\n})(ParamEvent || (ParamEvent = {}));\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/OptionsController.ts\\nvar CALLBACK_OPTION = \\\\\\\"callback\\\\\\\";\\nvar CALLBACK_STRING_OPTION = \\\\\\\"callbackString\\\\\\\";\\nvar COMPUTE_ON_DIRTY = \\\\\\\"computeOnDirty\\\\\\\";\\nvar COOK_OPTION = \\\\\\\"cook\\\\\\\";\\nvar FILE_BROWSE_OPTION = \\\\\\\"fileBrowse\\\\\\\";\\nvar FILE_TYPE_OPTION = \\\\\\\"type\\\\\\\";\\nvar EXPRESSION = \\\\\\\"expression\\\\\\\";\\nvar FOR_ENTITIES = \\\\\\\"forEntities\\\\\\\";\\nvar LABEL = \\\\\\\"label\\\\\\\";\\nvar LEVEL = \\\\\\\"level\\\\\\\";\\nvar MENU = \\\\\\\"menu\\\\\\\";\\nvar MENU_STRING = \\\\\\\"menuString\\\\\\\";\\nvar ENTRIES = \\\\\\\"entries\\\\\\\";\\nvar MULTILINE_OPTION = \\\\\\\"multiline\\\\\\\";\\nvar LANGUAGE_OPTION = \\\\\\\"language\\\\\\\";\\nvar NODE_SELECTION = \\\\\\\"nodeSelection\\\\\\\";\\nvar NODE_SELECTION_CONTEXT = \\\\\\\"context\\\\\\\";\\nvar NODE_SELECTION_TYPES = \\\\\\\"types\\\\\\\";\\nvar PARAM_SELECTION = \\\\\\\"paramSelection\\\\\\\";\\nvar DEPENDENT_ON_FOUND_NODE = \\\\\\\"dependentOnFoundNode\\\\\\\";\\nvar RANGE_OPTION = \\\\\\\"range\\\\\\\";\\nvar RANGE_LOCKED_OPTION = \\\\\\\"rangeLocked\\\\\\\";\\nvar STEP_OPTION = \\\\\\\"step\\\\\\\";\\nvar SPARE_OPTION = \\\\\\\"spare\\\\\\\";\\nvar TEXTURE_OPTION = \\\\\\\"texture\\\\\\\";\\nvar ENV_OPTION = \\\\\\\"env\\\\\\\";\\nvar HIDDEN_OPTION = \\\\\\\"hidden\\\\\\\";\\nvar FIELD_OPTION = \\\\\\\"field\\\\\\\";\\nvar VISIBLE_IF_OPTION = \\\\\\\"visibleIf\\\\\\\";\\nvar COLOR_CONVERSION = \\\\\\\"conversion\\\\\\\";\\nvar SEPARATOR_BEFORE_OPTION = \\\\\\\"separatorBefore\\\\\\\";\\nvar SEPARATOR_AFTER_OPTION = \\\\\\\"separatorAfter\\\\\\\";\\nvar StringParamLanguage;\\n(function(StringParamLanguage2) {\\n StringParamLanguage2[\\\\\\\"TYPESCRIPT\\\\\\\"] = \\\\\\\"typescript\\\\\\\";\\n})(StringParamLanguage || (StringParamLanguage = {}));\\nvar FileType;\\n(function(FileType2) {\\n FileType2[\\\\\\\"AUDIO\\\\\\\"] = \\\\\\\"audio\\\\\\\";\\n FileType2[\\\\\\\"TEXTURE_IMAGE\\\\\\\"] = \\\\\\\"texture_image\\\\\\\";\\n FileType2[\\\\\\\"TEXTURE_VIDEO\\\\\\\"] = \\\\\\\"texture_video\\\\\\\";\\n FileType2[\\\\\\\"GEOMETRY\\\\\\\"] = \\\\\\\"geometry\\\\\\\";\\n FileType2[\\\\\\\"FONT\\\\\\\"] = \\\\\\\"font\\\\\\\";\\n FileType2[\\\\\\\"SVG\\\\\\\"] = \\\\\\\"svg\\\\\\\";\\n FileType2[\\\\\\\"JSON\\\\\\\"] = \\\\\\\"json\\\\\\\";\\n})(FileType || (FileType = {}));\\nvar OptionsController = class {\\n constructor(_param) {\\n this._param = _param;\\n this._programatic_visible_state = true;\\n this._callbackAllowed = false;\\n this._updateVisibilityAndRemoveDirtyBound = this.updateVisibilityAndRemoveDirty.bind(this);\\n this._ui_data_dependency_set = false;\\n }\\n dispose() {\\n try {\\n this._options[CALLBACK_OPTION] = void 0;\\n this._options[CALLBACK_STRING_OPTION] = void 0;\\n } catch (err) {\\n }\\n this._visibility_graph_node?.dispose();\\n }\\n set(options) {\\n this._default_options = options;\\n this._options = ObjectUtils.cloneDeep(this._default_options);\\n this.post_set_options();\\n }\\n copy(options_controller) {\\n this._default_options = ObjectUtils.cloneDeep(options_controller.default());\\n this._options = ObjectUtils.cloneDeep(options_controller.current());\\n this.post_set_options();\\n }\\n setOption(name, value) {\\n this._options[name] = value;\\n if (this._param.components) {\\n for (let component of this._param.components) {\\n component.options.setOption(name, value);\\n }\\n }\\n }\\n post_set_options() {\\n this._handleComputeOnDirty();\\n }\\n param() {\\n return this._param;\\n }\\n node() {\\n return this._param.node;\\n }\\n default() {\\n return this._default_options;\\n }\\n current() {\\n return this._options;\\n }\\n hasOptionsOverridden() {\\n return !ObjectUtils.isEqual(this._options, this._default_options);\\n }\\n overriddenOptions() {\\n const overriden = {};\\n const option_names = Object.keys(this._options);\\n for (let option_name of option_names) {\\n if (!ObjectUtils.isEqual(this._options[option_name], this._default_options[option_name])) {\\n const cloned_option = ObjectUtils.cloneDeep(this._options[option_name]);\\n Object.assign(overriden, {[option_name]: cloned_option});\\n }\\n }\\n return overriden;\\n }\\n overriddenOptionNames() {\\n return Object.keys(this.overriddenOptions());\\n }\\n computeOnDirty() {\\n return this._options[COMPUTE_ON_DIRTY] || false;\\n }\\n _handleComputeOnDirty() {\\n if (this.computeOnDirty()) {\\n if (!this._computeOnDirty_callback_added) {\\n this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\", this._computeParam.bind(this));\\n this._computeOnDirty_callback_added = true;\\n }\\n }\\n }\\n async _computeParam() {\\n await this.param().compute();\\n }\\n hasCallback() {\\n return this._options[CALLBACK_OPTION] != null || this._options[CALLBACK_STRING_OPTION] != null;\\n }\\n allowCallback() {\\n this._callbackAllowed = true;\\n }\\n executeCallback() {\\n if (!this._callbackAllowed) {\\n return;\\n }\\n if (!this.node()) {\\n return;\\n }\\n const callback = this.getCallback();\\n if (!callback) {\\n return;\\n }\\n if (!this.node().scene().loadingController.loaded()) {\\n return;\\n }\\n const parent_param = this.param().parent_param;\\n if (parent_param) {\\n parent_param.options.executeCallback();\\n } else {\\n callback(this.node(), this.param());\\n }\\n }\\n getCallback() {\\n if (this.hasCallback()) {\\n return this._options[CALLBACK_OPTION] = this._options[CALLBACK_OPTION] || this.createCallbackFromString();\\n }\\n }\\n createCallbackFromString() {\\n const callbackString = this._options[CALLBACK_STRING_OPTION];\\n if (callbackString) {\\n const callback_function = new Function(\\\\\\\"node\\\\\\\", \\\\\\\"scene\\\\\\\", \\\\\\\"window\\\\\\\", \\\\\\\"location\\\\\\\", callbackString);\\n return () => {\\n callback_function(this.node(), this.node().scene(), null, null);\\n };\\n }\\n }\\n colorConversion() {\\n return this._options[COLOR_CONVERSION];\\n }\\n makesNodeDirtyWhenDirty() {\\n let cook_options;\\n if (this.param().parent_param != null) {\\n return false;\\n }\\n let value = true;\\n if ((cook_options = this._options[COOK_OPTION]) != null) {\\n value = cook_options;\\n }\\n return value;\\n }\\n fileBrowseOption() {\\n return this._options[FILE_BROWSE_OPTION];\\n }\\n fileBrowseAllowed() {\\n return this.fileBrowseOption() != null;\\n }\\n fileBrowseType() {\\n const option = this.fileBrowseOption();\\n if (option) {\\n return option[FILE_TYPE_OPTION];\\n } else {\\n return null;\\n }\\n }\\n separatorBefore() {\\n return this._options[SEPARATOR_BEFORE_OPTION];\\n }\\n separatorAfter() {\\n return this._options[SEPARATOR_AFTER_OPTION];\\n }\\n isExpressionForEntities() {\\n const expr_option = this._options[EXPRESSION];\\n if (expr_option) {\\n return expr_option[FOR_ENTITIES] || false;\\n }\\n return false;\\n }\\n level() {\\n return this._options[LEVEL] || 0;\\n }\\n hasMenu() {\\n return this.menuOptions() != null || this.menuStringOptions() != null;\\n }\\n menuOptions() {\\n return this._options[MENU];\\n }\\n menuStringOptions() {\\n return this._options[MENU_STRING];\\n }\\n menuEntries() {\\n const options = this.menuOptions() || this.menuStringOptions();\\n if (options) {\\n return options[ENTRIES];\\n } else {\\n return [];\\n }\\n }\\n isMultiline() {\\n return this._options[MULTILINE_OPTION] === true;\\n }\\n language() {\\n return this._options[LANGUAGE_OPTION];\\n }\\n isCode() {\\n return this.language() != null;\\n }\\n nodeSelectionOptions() {\\n return this._options[NODE_SELECTION];\\n }\\n nodeSelectionContext() {\\n const options = this.nodeSelectionOptions();\\n if (options) {\\n return options[NODE_SELECTION_CONTEXT];\\n }\\n }\\n nodeSelectionTypes() {\\n const options = this.nodeSelectionOptions();\\n if (options) {\\n return options[NODE_SELECTION_TYPES];\\n }\\n }\\n dependentOnFoundNode() {\\n if (DEPENDENT_ON_FOUND_NODE in this._options) {\\n return this._options[DEPENDENT_ON_FOUND_NODE];\\n } else {\\n return true;\\n }\\n }\\n isSelectingParam() {\\n return this.paramSelectionOptions() != null;\\n }\\n paramSelectionOptions() {\\n return this._options[PARAM_SELECTION];\\n }\\n paramSelectionType() {\\n const options = this.paramSelectionOptions();\\n if (options) {\\n const type_or_boolean = options;\\n if (!CoreType.isBoolean(type_or_boolean)) {\\n return type_or_boolean;\\n }\\n }\\n }\\n range() {\\n return this._options[RANGE_OPTION] || [0, 1];\\n }\\n step() {\\n return this._options[STEP_OPTION];\\n }\\n rangeLocked() {\\n return this._options[RANGE_LOCKED_OPTION] || [false, false];\\n }\\n ensureInRange(value) {\\n const range = this.range();\\n if (value >= range[0] && value <= range[1]) {\\n return value;\\n } else {\\n if (value < range[0]) {\\n return this.rangeLocked()[0] === true ? range[0] : value;\\n } else {\\n return this.rangeLocked()[1] === true ? range[1] : value;\\n }\\n }\\n }\\n isSpare() {\\n return this._options[SPARE_OPTION] || false;\\n }\\n textureOptions() {\\n return this._options[TEXTURE_OPTION];\\n }\\n textureAsEnv() {\\n const texture_options = this.textureOptions();\\n if (texture_options != null) {\\n return texture_options[ENV_OPTION] === true;\\n }\\n return false;\\n }\\n isHidden() {\\n return this._options[HIDDEN_OPTION] === true || this._programatic_visible_state === false;\\n }\\n isVisible() {\\n return !this.isHidden();\\n }\\n setVisibleState(state) {\\n this._options[HIDDEN_OPTION] = !state;\\n this.param().emit(ParamEvent.VISIBLE_UPDATED);\\n }\\n label() {\\n return this._options[LABEL];\\n }\\n isLabelHidden() {\\n const type = this.param().type();\\n return type === ParamType.BUTTON || type === ParamType.BOOLEAN && this.isFieldHidden();\\n }\\n isFieldHidden() {\\n return this._options[FIELD_OPTION] === false;\\n }\\n uiDataDependsOnOtherParams() {\\n return VISIBLE_IF_OPTION in this._options;\\n }\\n visibilityPredecessors() {\\n const visibility_options = this._options[VISIBLE_IF_OPTION];\\n if (!visibility_options) {\\n return [];\\n }\\n let predecessor_names = [];\\n if (CoreType.isArray(visibility_options)) {\\n predecessor_names = ArrayUtils.uniq(visibility_options.map((options) => Object.keys(options)).flat());\\n } else {\\n predecessor_names = Object.keys(visibility_options);\\n }\\n const node = this.param().node;\\n return ArrayUtils.compact(predecessor_names.map((name) => {\\n const param = node.params.get(name);\\n if (param) {\\n return param;\\n } else {\\n console.error(`param ${name} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`);\\n }\\n }));\\n }\\n setUiDataDependency() {\\n if (this._ui_data_dependency_set) {\\n return;\\n }\\n this._ui_data_dependency_set = true;\\n const predecessors = this.visibilityPredecessors();\\n if (predecessors.length > 0) {\\n this._visibility_graph_node = new CoreGraphNode(this.param().scene(), \\\\\\\"param_visibility\\\\\\\");\\n for (let predecessor of predecessors) {\\n this._visibility_graph_node.addGraphInput(predecessor);\\n }\\n this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\", this._updateVisibilityAndRemoveDirtyBound);\\n }\\n }\\n updateVisibilityAndRemoveDirty() {\\n this.updateVisibility();\\n this.param().removeDirtyState();\\n }\\n async updateVisibility() {\\n const options = this._options[VISIBLE_IF_OPTION];\\n if (options) {\\n const params = this.visibilityPredecessors();\\n const promises = params.map((p) => {\\n if (p.isDirty()) {\\n return p.compute();\\n }\\n });\\n this._programatic_visible_state = false;\\n await Promise.all(promises);\\n if (CoreType.isArray(options)) {\\n for (let options_set of options) {\\n const satisfied_values = params.filter((param) => param.value == options_set[param.name()]);\\n if (satisfied_values.length == params.length) {\\n this._programatic_visible_state = true;\\n }\\n }\\n } else {\\n const satisfied_values = params.filter((param) => param.value == options[param.name()]);\\n this._programatic_visible_state = satisfied_values.length == params.length;\\n }\\n this.param().emit(ParamEvent.VISIBLE_UPDATED);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/EmitController.ts\\nvar EmitController = class {\\n constructor(param) {\\n this.param = param;\\n this._blocked_emit = false;\\n this._blocked_parent_emit = false;\\n this._count_by_event_name = {};\\n }\\n emitAllowed() {\\n if (this._blocked_emit === true) {\\n return false;\\n }\\n if (this.param.scene().loadingController.isLoading()) {\\n return false;\\n }\\n return this.param.scene().dispatchController.emitAllowed();\\n }\\n blockEmit() {\\n this._blocked_emit = true;\\n if (this.param.isMultiple() && this.param.components) {\\n for (let component of this.param.components) {\\n component.emitController.blockEmit();\\n }\\n }\\n return true;\\n }\\n unblockEmit() {\\n this._blocked_emit = false;\\n if (this.param.isMultiple() && this.param.components) {\\n for (let component of this.param.components) {\\n component.emitController.unblockEmit();\\n }\\n }\\n return true;\\n }\\n blockParentEmit() {\\n this._blocked_parent_emit = true;\\n return true;\\n }\\n unblockParentEmit() {\\n this._blocked_parent_emit = false;\\n return true;\\n }\\n incrementCount(event_name) {\\n this._count_by_event_name[event_name] = this._count_by_event_name[event_name] || 0;\\n this._count_by_event_name[event_name] += 1;\\n }\\n eventsCount(event_name) {\\n return this._count_by_event_name[event_name] || 0;\\n }\\n emit(event) {\\n if (this.emitAllowed()) {\\n this.param.emit(event);\\n if (this.param.parent_param != null && this._blocked_parent_emit !== true) {\\n this.param.parent_param.emit(event);\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/Serializer.ts\\nvar ParamSerializer = class {\\n constructor(param) {\\n this.param = param;\\n }\\n toJSON() {\\n const data = {\\n name: this.param.name(),\\n type: this.param.type(),\\n raw_input: this.rawInput(),\\n value: this.value(),\\n value_pre_conversion: this.value_pre_conversion(),\\n expression: this.expression(),\\n graph_node_id: this.param.graphNodeId(),\\n error_message: this.error_message(),\\n is_visible: this.is_visible(),\\n components: void 0\\n };\\n if (this.param.isMultiple() && this.param.components) {\\n data[\\\\\\\"components\\\\\\\"] = this.param.components.map((component) => component.graphNodeId());\\n }\\n return data;\\n }\\n rawInput() {\\n return this.param.rawInputSerialized();\\n }\\n value() {\\n return this.param.valueSerialized();\\n }\\n value_pre_conversion() {\\n return this.param.valuePreConversionSerialized();\\n }\\n expression() {\\n return this.param.hasExpression() ? this.param.expressionController?.expression() : void 0;\\n }\\n error_message() {\\n return this.param.states.error.message();\\n }\\n is_visible() {\\n return this.param.options.isVisible();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/states/TimeDependent.ts\\nvar TimeDependentState2 = class {\\n constructor(param) {\\n this.param = param;\\n }\\n active() {\\n const frame_graph_node_id = this.param.scene().timeController.graphNode.graphNodeId();\\n return this.param.graphPredecessorIds().includes(frame_graph_node_id);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/states/Error.ts\\nvar ParamErrorState = class {\\n constructor(param) {\\n this.param = param;\\n }\\n set(message) {\\n if (this._message != message) {\\n this._message = message;\\n if (this._message) {\\n Poly.warn(this.param.path(), this._message);\\n }\\n this.param.emitController.emit(ParamEvent.ERROR_UPDATED);\\n }\\n }\\n message() {\\n return this._message;\\n }\\n clear() {\\n this.set(void 0);\\n }\\n active() {\\n return this._message != null;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/StatesController.ts\\nvar StatesController2 = class {\\n constructor(param) {\\n this.param = param;\\n this.timeDependent = new TimeDependentState2(this.param);\\n this.error = new ParamErrorState(this.param);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/MethodDependency.ts\\nvar MethodDependency = class extends CoreGraphNode {\\n constructor(param, path_argument, decomposed_path) {\\n super(param.scene(), \\\\\\\"MethodDependency\\\\\\\");\\n this.param = param;\\n this.path_argument = path_argument;\\n this.decomposed_path = decomposed_path;\\n this._update_from_name_change_bound = this._update_from_name_change.bind(this);\\n param.expressionController?.registerMethodDependency(this);\\n this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\", this._update_from_name_change_bound);\\n }\\n _update_from_name_change(trigger) {\\n if (trigger && this.decomposed_path) {\\n const node = trigger;\\n this.decomposed_path.update_from_name_change(node);\\n const new_path = this.decomposed_path.to_path();\\n const literal = this.jsep_node;\\n if (literal) {\\n literal.value = `${literal.value}`.replace(`${this.path_argument}`, new_path);\\n literal.raw = literal.raw.replace(`${this.path_argument}`, new_path);\\n }\\n if (this.param.expressionController) {\\n this.param.expressionController.updateFromMethodDependencyNameChange();\\n }\\n }\\n }\\n reset() {\\n this.graphDisconnectPredecessors();\\n }\\n listen_for_name_changes() {\\n if (this.jsep_node && this.decomposed_path) {\\n for (let node_in_path of this.decomposed_path.named_nodes()) {\\n if (node_in_path) {\\n const node = node_in_path;\\n if (node.nameController) {\\n this.addGraphInput(node.nameController.graph_node);\\n }\\n }\\n }\\n }\\n }\\n set_jsep_node(jsep_node) {\\n this.jsep_node = jsep_node;\\n }\\n set_resolved_graph_node(node) {\\n this.resolved_graph_node = node;\\n }\\n set_unresolved_path(path) {\\n this.unresolved_path = path;\\n }\\n static create(param, index_or_path, node, decomposed_path) {\\n const is_index = CoreType.isNumber(index_or_path);\\n const instance = new MethodDependency(param, index_or_path, decomposed_path);\\n if (node) {\\n instance.set_resolved_graph_node(node);\\n } else {\\n if (!is_index) {\\n const path = index_or_path;\\n instance.set_unresolved_path(path);\\n }\\n }\\n return instance;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/_Base.ts\\nvar TYPED_PARAM_DEFAULT_COMPONENT_NAMES = [];\\nvar TypedParam = class extends CoreGraphNode {\\n constructor(scene, node) {\\n super(scene, \\\\\\\"BaseParam\\\\\\\");\\n this._options = new OptionsController(this);\\n this._emit_controller = new EmitController(this);\\n this._is_computing = false;\\n this._node = node;\\n this.initialize_param();\\n }\\n get options() {\\n return this._options = this._options || new OptionsController(this);\\n }\\n get emitController() {\\n return this._emit_controller = this._emit_controller || new EmitController(this);\\n }\\n get expressionController() {\\n return this._expression_controller;\\n }\\n get serializer() {\\n return this._serializer = this._serializer || new ParamSerializer(this);\\n }\\n get states() {\\n return this._states = this._states || new StatesController2(this);\\n }\\n dispose() {\\n const predecessors = this.graphPredecessors();\\n for (let predecessor of predecessors) {\\n if (predecessor instanceof MethodDependency) {\\n predecessor.dispose();\\n }\\n }\\n this.scene().missingExpressionReferencesController.deregisterParam(this);\\n this._expression_controller?.dispose();\\n super.dispose();\\n this._options?.dispose();\\n }\\n initialize_param() {\\n }\\n static type() {\\n return ParamType.FLOAT;\\n }\\n type() {\\n return this.constructor.type();\\n }\\n isNumeric() {\\n return false;\\n }\\n setName(name) {\\n super.setName(name);\\n }\\n get value() {\\n return this._value;\\n }\\n copy_value(param) {\\n if (param.type() == this.type()) {\\n this._copy_value(param);\\n } else {\\n console.warn(`cannot copy value from ${param.type()} to ${this.type()}`);\\n }\\n }\\n _copy_value(param) {\\n throw \\\\\\\"abstract method param._copy_value\\\\\\\";\\n }\\n valuePreConversionSerialized() {\\n return void 0;\\n }\\n convert(raw_val) {\\n return null;\\n }\\n static are_raw_input_equal(val1, val2) {\\n return false;\\n }\\n is_raw_input_equal(other_raw_input) {\\n return this.constructor.are_raw_input_equal(this._raw_input, other_raw_input);\\n }\\n static are_values_equal(val1, val2) {\\n return false;\\n }\\n is_value_equal(other_val) {\\n return this.constructor.are_values_equal(this.value, other_val);\\n }\\n _clone_raw_input(raw_input) {\\n return raw_input;\\n }\\n set(raw_input) {\\n this._raw_input = this._clone_raw_input(this._prefilter_invalid_raw_input(raw_input));\\n this.emitController.emit(ParamEvent.RAW_INPUT_UPDATED);\\n this.processRawInput();\\n }\\n _prefilter_invalid_raw_input(raw_input) {\\n return raw_input;\\n }\\n defaultValue() {\\n return this._default_value;\\n }\\n isDefault() {\\n return this._raw_input == this._default_value;\\n }\\n rawInput() {\\n return this._raw_input;\\n }\\n processRawInput() {\\n }\\n async compute() {\\n if (this.scene().loadingController.isLoading()) {\\n console.warn(`param attempt to compute ${this.path()}`);\\n }\\n if (this.isDirty()) {\\n if (!this._is_computing) {\\n this._is_computing = true;\\n await this.processComputation();\\n this._is_computing = false;\\n if (this._compute_resolves) {\\n let callback;\\n while (callback = this._compute_resolves.pop()) {\\n callback();\\n }\\n }\\n } else {\\n return new Promise((resolve, reject) => {\\n this._compute_resolves = this._compute_resolves || [];\\n this._compute_resolves.push(resolve);\\n });\\n }\\n }\\n }\\n async processComputation() {\\n }\\n setInitValue(init_value) {\\n this._default_value = this._clone_raw_input(this._prefilter_invalid_raw_input(init_value));\\n }\\n _setupNodeDependencies(node) {\\n if (!node) {\\n if (this._node) {\\n this._node.params.params_node?.removeGraphInput(this);\\n }\\n } else {\\n this.options.allowCallback();\\n if (!this.parent_param) {\\n if (this.options.makesNodeDirtyWhenDirty()) {\\n node.params.params_node?.addGraphInput(this, false);\\n } else {\\n this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\", async () => {\\n await this.compute();\\n this.options.executeCallback();\\n });\\n }\\n }\\n }\\n if (this.components) {\\n for (let c of this.components) {\\n c._setupNodeDependencies(node);\\n }\\n }\\n }\\n get node() {\\n return this._node;\\n }\\n parent() {\\n return this.node;\\n }\\n set_parent_param(param) {\\n param.addGraphInput(this, false);\\n this._parent_param = param;\\n }\\n get parent_param() {\\n return this._parent_param;\\n }\\n has_parent_param() {\\n return this._parent_param != null;\\n }\\n path() {\\n return this.node?.path() + \\\\\\\"/\\\\\\\" + this.name();\\n }\\n pathRelativeTo(node) {\\n const nodeRelativePath = CoreWalker.relativePath(node, this.node);\\n if (nodeRelativePath.length > 0) {\\n return `${nodeRelativePath}${CoreWalker.SEPARATOR}${this.name()}`;\\n } else {\\n return this.name();\\n }\\n }\\n emit(event_name) {\\n if (this.emitController.emitAllowed()) {\\n this.emitController.incrementCount(event_name);\\n this.scene().dispatchController.dispatch(this, event_name);\\n }\\n }\\n get components() {\\n return this._components;\\n }\\n componentNames() {\\n return TYPED_PARAM_DEFAULT_COMPONENT_NAMES;\\n }\\n isMultiple() {\\n return this.componentNames().length > 0;\\n }\\n initComponents() {\\n }\\n hasExpression() {\\n return this.expressionController != null && this.expressionController.active();\\n }\\n toJSON() {\\n return this.serializer.toJSON();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/traversers/ParsedTree.ts\\nvar import_jsep = __toModule(require_jsep());\\nimport_jsep.default.addUnaryOp(\\\\\\\"@\\\\\\\");\\nvar precedence = 10;\\nimport_jsep.default.addBinaryOp(\\\\\\\"**\\\\\\\", precedence);\\nvar JSEP_IDENTIFIER = \\\\\\\"Identifier\\\\\\\";\\nvar JSEP_LITERAL = \\\\\\\"Literal\\\\\\\";\\nvar JSEP_CALL_EXPRESSION = \\\\\\\"CallExpression\\\\\\\";\\nvar STRING_EXPRESSION_SEPARATOR = \\\\\\\"`\\\\\\\";\\nvar ParsedTree = class {\\n constructor() {\\n }\\n parse_expression(string) {\\n try {\\n this.reset();\\n this.node = import_jsep.default(string);\\n } catch (e) {\\n const message = `could not parse the expression '${string}' (error: ${e})`;\\n this.error_message = message;\\n }\\n }\\n parse_expression_for_string_param(string) {\\n try {\\n this.reset();\\n const elements = ParsedTree.string_value_elements(string);\\n const nodes = [];\\n for (let i = 0; i < elements.length; i++) {\\n const element = elements[i];\\n let node;\\n if (i % 2 == 1) {\\n node = import_jsep.default(element);\\n } else {\\n const sanitizedElement = element.replace(/\\\\'/g, \\\\\\\"\\\\\\\\'\\\\\\\");\\n node = {\\n type: JSEP_LITERAL,\\n value: `'${sanitizedElement}'`,\\n raw: `'${sanitizedElement}'`\\n };\\n }\\n nodes.push(node);\\n }\\n this.node = {\\n type: JSEP_CALL_EXPRESSION,\\n arguments: nodes,\\n callee: {\\n type: JSEP_IDENTIFIER,\\n name: \\\\\\\"strConcat\\\\\\\"\\n }\\n };\\n } catch (e) {\\n const message = `could not parse the expression '${string}' (error: ${e})`;\\n this.error_message = message;\\n }\\n }\\n static string_value_elements(v) {\\n if (v != null) {\\n if (CoreType.isString(v)) {\\n return v.split(STRING_EXPRESSION_SEPARATOR);\\n } else {\\n return [];\\n }\\n } else {\\n return [];\\n }\\n }\\n reset() {\\n this.node = void 0;\\n this.error_message = void 0;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/LiteralConstructsController.ts\\nvar LiteralConstructsController = class {\\n static if(args) {\\n const condition = args[0];\\n const val_true = args[1];\\n const val_false = args[2];\\n return `(${condition}) ? (${val_true}) : (${val_false})`;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/traversers/_Base.ts\\nvar VARIABLE_PREFIX = \\\\\\\"$\\\\\\\";\\nvar BaseTraverser = class {\\n constructor(param) {\\n this.param = param;\\n this._set_error_from_error_bound = this._set_error_from_error.bind(this);\\n }\\n clear_error() {\\n this._error_message = void 0;\\n }\\n set_error(message) {\\n this._error_message = this._error_message || message;\\n }\\n _set_error_from_error(error) {\\n if (CoreType.isString(error)) {\\n this._error_message = error;\\n } else {\\n this._error_message = error.message;\\n }\\n }\\n is_errored() {\\n return this._error_message != null;\\n }\\n error_message() {\\n return this._error_message;\\n }\\n reset() {\\n this._error_message = void 0;\\n }\\n traverse_node(node) {\\n const method_name = `traverse_${node.type}`;\\n const method = this[method_name];\\n if (method) {\\n return this[method_name](node);\\n } else {\\n this.set_error(`expression unknown node type: ${node.type}`);\\n }\\n }\\n traverse_BinaryExpression(node) {\\n return `${this.traverse_node(node.left)} ${node.operator} ${this.traverse_node(node.right)}`;\\n }\\n traverse_LogicalExpression(node) {\\n return `${this.traverse_node(node.left)} ${node.operator} ${this.traverse_node(node.right)}`;\\n }\\n traverse_MemberExpression(node) {\\n return `${this.traverse_node(node.object)}.${this.traverse_node(node.property)}`;\\n }\\n traverse_ConditionalExpression(node) {\\n return `(${this.traverse_node(node.test)}) ? (${this.traverse_node(node.consequent)}) : (${this.traverse_node(node.alternate)})`;\\n }\\n traverse_Compound(node) {\\n const args = node.body;\\n let traversed_args = [];\\n for (let i = 0; i < args.length; i++) {\\n const arg_node = args[i];\\n if (arg_node.type == \\\\\\\"Identifier\\\\\\\") {\\n if (arg_node.name[0] == VARIABLE_PREFIX) {\\n traversed_args.push(\\\\\\\"`${\\\\\\\" + this.traverse_node(arg_node) + \\\\\\\"}`\\\\\\\");\\n } else {\\n traversed_args.push(`'${arg_node.name}'`);\\n }\\n } else {\\n traversed_args.push(\\\\\\\"`${\\\\\\\" + this.traverse_node(arg_node) + \\\\\\\"}`\\\\\\\");\\n }\\n }\\n return traversed_args.join(\\\\\\\" + \\\\\\\");\\n }\\n traverse_Literal(node) {\\n return `${node.raw}`;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/AttributeRequirementsController.ts\\nvar VAR_CORE_GEOMETRY = `coreGeometry`;\\nvar AttributeRequirementsController = class {\\n constructor() {\\n }\\n reset() {\\n if (this._attributeNames) {\\n this._attributeNames.clear();\\n }\\n }\\n assignAttributesLines() {\\n if (this._attributeNames) {\\n const lines = [];\\n for (let attribName of this._attributeNames) {\\n lines.push(AttributeRequirementsController.assignAttributeLine(attribName));\\n }\\n return lines.join(\\\\\\\";\\\\n\\\\\\\");\\n } else {\\n return \\\\\\\"\\\\\\\";\\n }\\n }\\n assignArraysLines() {\\n if (this._attributeNames) {\\n const lines = [];\\n if (this._attributeNames.size > 0) {\\n const coreGeoLine = `const ${VAR_CORE_GEOMETRY} = entities[0].coreGeometry();`;\\n lines.push(coreGeoLine);\\n }\\n for (let attribName of this._attributeNames) {\\n lines.push(AttributeRequirementsController.assignItemSizeLine(attribName));\\n lines.push(AttributeRequirementsController.assignArrayLine(attribName));\\n }\\n return lines.join(\\\\\\\";\\\\n\\\\\\\");\\n } else {\\n return \\\\\\\"\\\\\\\";\\n }\\n }\\n attributePresenceCheckLine() {\\n if (this._attributeNames) {\\n const varNames = [];\\n for (let attribName of this._attributeNames) {\\n const varName = AttributeRequirementsController._varAttribute(attribName);\\n varNames.push(varName);\\n }\\n if (varNames.length > 0) {\\n return varNames.join(\\\\\\\" && \\\\\\\");\\n }\\n }\\n return \\\\\\\"true\\\\\\\";\\n }\\n add(attribName) {\\n this._attributeNames = this._attributeNames || new Set();\\n this._attributeNames.add(attribName);\\n }\\n static assignAttributeLine(attribName) {\\n const varAttribute = this._varAttribute(attribName);\\n return `const ${varAttribute} = entities[0].geometry().attributes['${attribName}']`;\\n }\\n static assignItemSizeLine(attribName) {\\n const varAttribute = this._varAttribute(attribName);\\n const varAttributeSize = this._varAttribSize(attribName);\\n return `const ${varAttributeSize} = ${varAttribute}.itemSize`;\\n }\\n static assignArrayLine(attribName) {\\n const varAttribute = this._varAttribute(attribName);\\n const varArray = this._varArray(attribName);\\n const isIndexedCondition = `${VAR_CORE_GEOMETRY}.isAttribIndexed('${attribName}')`;\\n const indexedArray = `entities.map(e=>e.indexedAttribValue('${attribName}'))`;\\n const nonIndexedArray = `${varAttribute}.array`;\\n return `const ${varArray} = ${isIndexedCondition} ? ${indexedArray} : ${nonIndexedArray}`;\\n }\\n static _varAttribute(attribName) {\\n return `attrib_${attribName}`;\\n }\\n static _varAttribSize(attribName) {\\n return `attrib_size_${attribName}`;\\n }\\n static _varArray(attribName) {\\n return `array_${attribName}`;\\n }\\n varAttributeSize(attribName) {\\n return AttributeRequirementsController._varAttribSize(attribName);\\n }\\n varArray(attribName) {\\n return AttributeRequirementsController._varArray(attribName);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/traversers/FunctionGenerator.ts\\nvar NATIVE_MATH_METHODS = [\\n \\\\\\\"abs\\\\\\\",\\n \\\\\\\"acos\\\\\\\",\\n \\\\\\\"acosh\\\\\\\",\\n \\\\\\\"asin\\\\\\\",\\n \\\\\\\"asinh\\\\\\\",\\n \\\\\\\"atan\\\\\\\",\\n \\\\\\\"atan2\\\\\\\",\\n \\\\\\\"atanh\\\\\\\",\\n \\\\\\\"ceil\\\\\\\",\\n \\\\\\\"cos\\\\\\\",\\n \\\\\\\"cosh\\\\\\\",\\n \\\\\\\"exp\\\\\\\",\\n \\\\\\\"expm1\\\\\\\",\\n \\\\\\\"floor\\\\\\\",\\n \\\\\\\"log\\\\\\\",\\n \\\\\\\"log1p\\\\\\\",\\n \\\\\\\"log2\\\\\\\",\\n \\\\\\\"log10\\\\\\\",\\n \\\\\\\"max\\\\\\\",\\n \\\\\\\"min\\\\\\\",\\n \\\\\\\"pow\\\\\\\",\\n \\\\\\\"round\\\\\\\",\\n \\\\\\\"sign\\\\\\\",\\n \\\\\\\"sin\\\\\\\",\\n \\\\\\\"sinh\\\\\\\",\\n \\\\\\\"sqrt\\\\\\\",\\n \\\\\\\"tan\\\\\\\",\\n \\\\\\\"tanh\\\\\\\"\\n];\\nvar NATIVE_ES6_MATH_METHODS = [\\\\\\\"cbrt\\\\\\\", \\\\\\\"hypot\\\\\\\", \\\\\\\"log10\\\\\\\", \\\\\\\"trunc\\\\\\\"];\\nvar NATIVE_MATH_METHODS_RENAMED = {\\n math_random: \\\\\\\"random\\\\\\\"\\n};\\nvar CORE_MATH_METHODS = [\\\\\\\"fit\\\\\\\", \\\\\\\"fit01\\\\\\\", \\\\\\\"fract\\\\\\\", \\\\\\\"deg2rad\\\\\\\", \\\\\\\"rad2deg\\\\\\\", \\\\\\\"rand\\\\\\\", \\\\\\\"clamp\\\\\\\"];\\nvar EASING_METHODS = Object.keys(Easing);\\nvar CORE_STRING_METHODS = [\\\\\\\"precision\\\\\\\"];\\nvar NATIVE_MATH_CONSTANTS = [\\\\\\\"E\\\\\\\", \\\\\\\"LN2\\\\\\\", \\\\\\\"LN10\\\\\\\", \\\\\\\"LOG10E\\\\\\\", \\\\\\\"LOG2E\\\\\\\", \\\\\\\"PI\\\\\\\", \\\\\\\"SQRT1_2\\\\\\\", \\\\\\\"SQRT2\\\\\\\"];\\nvar DIRECT_EXPRESSION_FUNCTIONS = {};\\nNATIVE_MATH_METHODS.forEach((name) => {\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Math.${name}`;\\n});\\nNATIVE_ES6_MATH_METHODS.forEach((name) => {\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Math.${name}`;\\n});\\nObject.keys(NATIVE_MATH_METHODS_RENAMED).forEach((name) => {\\n const remaped = NATIVE_MATH_METHODS_RENAMED[name];\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Math.${remaped}`;\\n});\\nCORE_MATH_METHODS.forEach((name) => {\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Core.Math.${name}`;\\n});\\nEASING_METHODS.forEach((name) => {\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Core.Math.Easing.${name}`;\\n});\\nCORE_STRING_METHODS.forEach((name) => {\\n DIRECT_EXPRESSION_FUNCTIONS[name] = `Core.String.${name}`;\\n});\\nvar LITERAL_CONSTRUCT = {\\n if: LiteralConstructsController.if\\n};\\nvar GLOBAL_CONSTANTS = {};\\nNATIVE_MATH_CONSTANTS.forEach((name) => {\\n GLOBAL_CONSTANTS[name] = `Math.${name}`;\\n});\\nvar QUOTE = \\\\\\\"'\\\\\\\";\\nvar ARGUMENTS_SEPARATOR = \\\\\\\", \\\\\\\";\\nvar ATTRIBUTE_PREFIX = \\\\\\\"@\\\\\\\";\\nvar PROPERTY_OFFSETS = {\\n x: 0,\\n y: 1,\\n z: 2,\\n w: 3,\\n r: 0,\\n g: 1,\\n b: 2\\n};\\nvar FunctionGenerator = class extends BaseTraverser {\\n constructor(param) {\\n super(param);\\n this.param = param;\\n this._attribute_requirements_controller = new AttributeRequirementsController();\\n this.methods = [];\\n this.method_index = -1;\\n this.method_dependencies = [];\\n this.immutable_dependencies = [];\\n }\\n parse_tree(parsed_tree) {\\n this.reset();\\n if (parsed_tree.error_message == null) {\\n try {\\n this._attribute_requirements_controller = new AttributeRequirementsController();\\n if (parsed_tree.node) {\\n const function_main_string = this.traverse_node(parsed_tree.node);\\n if (function_main_string && !this.is_errored()) {\\n this.function_main_string = function_main_string;\\n }\\n } else {\\n console.warn(\\\\\\\"no parsed_tree.node\\\\\\\");\\n }\\n } catch (e) {\\n console.warn(`error in expression for param ${this.param.path()}`);\\n console.warn(e);\\n }\\n if (this.function_main_string) {\\n try {\\n const body = this._functionBody();\\n this.function = new Function(\\\\\\\"Core\\\\\\\", \\\\\\\"param\\\\\\\", \\\\\\\"methods\\\\\\\", \\\\\\\"_set_error_from_error\\\\\\\", `\\n\\t\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\t\\t${body}\\n\\t\\t\\t\\t\\t} catch(e) {\\n\\t\\t\\t\\t\\t\\t_set_error_from_error(e)\\n\\t\\t\\t\\t\\t\\treturn null;\\n\\t\\t\\t\\t\\t}`);\\n } catch (e) {\\n console.warn(e);\\n this.set_error(\\\\\\\"cannot generate function\\\\\\\");\\n }\\n } else {\\n this.set_error(\\\\\\\"cannot generate function body\\\\\\\");\\n }\\n } else {\\n this.set_error(\\\\\\\"cannot parse expression\\\\\\\");\\n }\\n }\\n reset() {\\n super.reset();\\n this.function_main_string = void 0;\\n this.methods = [];\\n this.method_index = -1;\\n this.function = void 0;\\n this.method_dependencies = [];\\n this.immutable_dependencies = [];\\n }\\n _functionBody() {\\n if (this.param.options.isExpressionForEntities()) {\\n return `\\n\\t\\t\\tconst entities = param.expressionController.entities();\\n\\t\\t\\tif(entities){\\n\\t\\t\\t\\treturn new Promise( async (resolve, reject)=>{\\n\\t\\t\\t\\t\\tlet entity;\\n\\t\\t\\t\\t\\tconst entity_callback = param.expressionController.entity_callback();\\n\\t\\t\\t\\t\\t// assign_attributes_lines\\n\\t\\t\\t\\t\\t${this._attribute_requirements_controller.assignAttributesLines()}\\n\\t\\t\\t\\t\\t// check if attributes are present\\n\\t\\t\\t\\t\\tif( ${this._attribute_requirements_controller.attributePresenceCheckLine()} ){\\n\\t\\t\\t\\t\\t\\t// assign_arrays_lines\\n\\t\\t\\t\\t\\t\\t${this._attribute_requirements_controller.assignArraysLines()}\\n\\t\\t\\t\\t\\t\\tfor(let index=0; index < entities.length; index++){\\n\\t\\t\\t\\t\\t\\t\\tentity = entities[index];\\n\\t\\t\\t\\t\\t\\t\\tresult = ${this.function_main_string};\\n\\t\\t\\t\\t\\t\\t\\tentity_callback(entity, result);\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\tresolve()\\n\\t\\t\\t\\t\\t} else {\\n\\t\\t\\t\\t\\t\\tconst error = new Error('attribute not found')\\n\\t\\t\\t\\t\\t\\t_set_error_from_error(error)\\n\\t\\t\\t\\t\\t\\treject(error)\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t})\\n\\t\\t\\t}\\n\\t\\t\\treturn []`;\\n } else {\\n return `\\n\\t\\t\\treturn new Promise( async (resolve, reject)=>{\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\tconst value = ${this.function_main_string}\\n\\t\\t\\t\\t\\tresolve(value)\\n\\t\\t\\t\\t} catch(e) {\\n\\t\\t\\t\\t\\t_set_error_from_error(e)\\n\\t\\t\\t\\t\\treject()\\n\\t\\t\\t\\t}\\n\\t\\t\\t})\\n\\t\\t\\t`;\\n }\\n }\\n evalAllowed() {\\n return this.function != null;\\n }\\n evalFunction() {\\n if (this.function) {\\n this.clear_error();\\n const Core = {\\n Math: CoreMath,\\n String: CoreString\\n };\\n const result = this.function(Core, this.param, this.methods, this._set_error_from_error_bound);\\n return result;\\n }\\n }\\n traverse_CallExpression(node) {\\n const method_arguments = node.arguments.map((arg) => {\\n return this.traverse_node(arg);\\n });\\n const callee = node.callee;\\n const method_name = callee.name;\\n if (method_name) {\\n const literal_contruct = LITERAL_CONSTRUCT[method_name];\\n if (literal_contruct) {\\n return literal_contruct(method_arguments);\\n }\\n const arguments_joined = `${method_arguments.join(ARGUMENTS_SEPARATOR)}`;\\n const direct_function_name = DIRECT_EXPRESSION_FUNCTIONS[method_name];\\n if (direct_function_name) {\\n return `${direct_function_name}(${arguments_joined})`;\\n }\\n const expressionRegister = Poly.expressionsRegister;\\n const indirect_method = expressionRegister.getMethod(method_name);\\n if (indirect_method) {\\n const path_node = node.arguments[0];\\n const function_string = `return ${method_arguments[0]}`;\\n let path_argument_function;\\n let path_argument = [];\\n try {\\n path_argument_function = new Function(function_string);\\n path_argument = path_argument_function();\\n } catch {\\n }\\n this._create_method_and_dependencies(method_name, path_argument, path_node);\\n return `(await methods[${this.method_index}].processArguments([${arguments_joined}]))`;\\n } else {\\n const available_methods = expressionRegister.availableMethods().join(\\\\\\\", \\\\\\\");\\n const message = `method not found (${method_name}), available methods are: ${available_methods}`;\\n Poly.warn(message);\\n }\\n }\\n this.set_error(`unknown method: ${method_name}`);\\n }\\n traverse_BinaryExpression(node) {\\n return `(${this.traverse_node(node.left)} ${node.operator} ${this.traverse_node(node.right)})`;\\n }\\n traverse_LogicalExpression(node) {\\n return `(${this.traverse_node(node.left)} ${node.operator} ${this.traverse_node(node.right)})`;\\n }\\n traverse_MemberExpression(node) {\\n return `${this.traverse_node(node.object)}.${this.traverse_node(node.property)}`;\\n }\\n traverse_UnaryExpression(node) {\\n if (node.operator === ATTRIBUTE_PREFIX) {\\n let argument = node.argument;\\n let attribute_name;\\n let property;\\n switch (argument.type) {\\n case \\\\\\\"Identifier\\\\\\\": {\\n const argument_identifier = argument;\\n attribute_name = argument_identifier.name;\\n break;\\n }\\n case \\\\\\\"MemberExpression\\\\\\\": {\\n const argument_member_expression = argument;\\n const attrib_node = argument_member_expression.object;\\n const property_node = argument_member_expression.property;\\n attribute_name = attrib_node.name;\\n property = property_node.name;\\n break;\\n }\\n }\\n if (attribute_name) {\\n attribute_name = CoreAttribute.remapName(attribute_name);\\n if (attribute_name == \\\\\\\"ptnum\\\\\\\") {\\n return \\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";\\n } else {\\n const var_attribute_size = this._attribute_requirements_controller.varAttributeSize(attribute_name);\\n const var_array = this._attribute_requirements_controller.varArray(attribute_name);\\n this._attribute_requirements_controller.add(attribute_name);\\n if (property) {\\n const property_offset = PROPERTY_OFFSETS[property];\\n return `${var_array}[entity.index()*${var_attribute_size}+${property_offset}]`;\\n } else {\\n return `${var_array}[entity.index()*${var_attribute_size}]`;\\n }\\n }\\n } else {\\n console.warn(\\\\\\\"attribute not found\\\\\\\");\\n return \\\\\\\"\\\\\\\";\\n }\\n } else {\\n return `${node.operator}${this.traverse_node(node.argument)}`;\\n }\\n }\\n traverse_Literal(node) {\\n return `${node.raw}`;\\n }\\n traverse_Identifier(node) {\\n const identifier_first_char = node.name[0];\\n if (identifier_first_char == VARIABLE_PREFIX) {\\n const identifier_name_without_dollar_sign = node.name.substr(1);\\n const direct_constant_name = GLOBAL_CONSTANTS[identifier_name_without_dollar_sign];\\n if (direct_constant_name) {\\n return direct_constant_name;\\n }\\n const method_name = `traverse_Identifier_${identifier_name_without_dollar_sign}`;\\n const method = this[method_name];\\n if (method) {\\n return this[method_name]();\\n } else {\\n this.set_error(`identifier unknown: ${node.name}`);\\n }\\n } else {\\n return node.name;\\n }\\n }\\n traverse_Identifier_F() {\\n this.immutable_dependencies.push(this.param.scene().timeController.graphNode);\\n return `param.scene().timeController.frame()`;\\n }\\n traverse_Identifier_T() {\\n this.immutable_dependencies.push(this.param.scene().timeController.graphNode);\\n return `param.scene().timeController.time()`;\\n }\\n traverse_Identifier_OS() {\\n return `${QUOTE}${this.param.node.name()}${QUOTE}`;\\n }\\n traverse_Identifier_CH() {\\n return `${QUOTE}${this.param.name()}${QUOTE}`;\\n }\\n traverse_Identifier_CEX() {\\n return this._method_centroid(\\\\\\\"x\\\\\\\");\\n }\\n traverse_Identifier_CEY() {\\n return this._method_centroid(\\\\\\\"y\\\\\\\");\\n }\\n traverse_Identifier_CEZ() {\\n return this._method_centroid(\\\\\\\"z\\\\\\\");\\n }\\n _method_centroid(component) {\\n const method_arguments = [0, `${QUOTE}${component}${QUOTE}`];\\n const arguments_joined = method_arguments.join(ARGUMENTS_SEPARATOR);\\n this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\", 0);\\n return `(await methods[${this.method_index}].processArguments([${arguments_joined}]))`;\\n }\\n _create_method_and_dependencies(method_name, path_argument, path_node) {\\n const expressionRegister = Poly.expressionsRegister;\\n const method_constructor = expressionRegister.getMethod(method_name);\\n if (!method_constructor) {\\n const available_methods = expressionRegister.availableMethods();\\n const message = `method not found (${method_name}), available methods are: ${available_methods.join(\\\\\\\", \\\\\\\")}`;\\n this.set_error(message);\\n Poly.warn(message);\\n return;\\n }\\n const method = new method_constructor(this.param);\\n this.method_index += 1;\\n this.methods[this.method_index] = method;\\n if (method.require_dependency()) {\\n const method_dependency = method.findDependency(path_argument);\\n if (method_dependency) {\\n if (path_node) {\\n method_dependency.set_jsep_node(path_node);\\n }\\n this.method_dependencies.push(method_dependency);\\n } else {\\n if (path_node && CoreType.isString(path_argument)) {\\n this.param.scene().missingExpressionReferencesController.register(this.param, path_argument);\\n }\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/traversers/ExpressionStringGenerator.ts\\nvar ARGUMENTS_SEPARATOR2 = \\\\\\\", \\\\\\\";\\nvar ExpressionStringGenerator = class extends BaseTraverser {\\n constructor(param) {\\n super(param);\\n this.param = param;\\n }\\n parse_tree(parsed_tree) {\\n if (parsed_tree.error_message == null && parsed_tree.node) {\\n try {\\n return this.traverse_node(parsed_tree.node);\\n } catch (e) {\\n this.set_error(\\\\\\\"could not traverse tree\\\\\\\");\\n }\\n } else {\\n this.set_error(\\\\\\\"cannot parse tree\\\\\\\");\\n }\\n }\\n traverse_CallExpression(node) {\\n const method_arguments = node.arguments.map((arg) => {\\n return this.traverse_node(arg);\\n });\\n const arguments_joined = `${method_arguments.join(ARGUMENTS_SEPARATOR2)}`;\\n const method_name = node.callee.name;\\n return `${method_name}(${arguments_joined})`;\\n }\\n traverse_UnaryExpression(node) {\\n return `${node.operator}${this.traverse_node(node.argument)}`;\\n }\\n traverse_Identifier(node) {\\n return `${node.name}`;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/DependenciesController.ts\\nvar DependenciesController = class {\\n constructor(param) {\\n this.param = param;\\n this.cyclic_graph_detected = false;\\n this.method_dependencies = [];\\n }\\n set_error(message) {\\n this.error_message = this.error_message || message;\\n }\\n reset() {\\n this.param.graphDisconnectPredecessors();\\n this.method_dependencies.forEach((method_dependency) => {\\n method_dependency.reset();\\n });\\n this.method_dependencies = [];\\n }\\n update(function_generator) {\\n this.cyclic_graph_detected = false;\\n this.connect_immutable_dependencies(function_generator);\\n this.method_dependencies = function_generator.method_dependencies;\\n this.handle_method_dependencies();\\n this.listen_for_name_changes();\\n }\\n connect_immutable_dependencies(function_generator) {\\n function_generator.immutable_dependencies.forEach((dependency) => {\\n if (this.cyclic_graph_detected == false) {\\n if (this.param.addGraphInput(dependency) == false) {\\n this.cyclic_graph_detected = true;\\n this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\");\\n this.reset();\\n return;\\n }\\n }\\n });\\n }\\n handle_method_dependencies() {\\n this.method_dependencies.forEach((method_dependency) => {\\n if (this.cyclic_graph_detected == false) {\\n this.handle_method_dependency(method_dependency);\\n }\\n });\\n }\\n handle_method_dependency(method_dependency) {\\n const node_simple = method_dependency.resolved_graph_node;\\n if (node_simple) {\\n if (!this.param.addGraphInput(node_simple)) {\\n this.cyclic_graph_detected = true;\\n this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\");\\n this.reset();\\n return;\\n }\\n }\\n }\\n listen_for_name_changes() {\\n this.method_dependencies.forEach((method_dependency) => {\\n method_dependency.listen_for_name_changes();\\n });\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/expressions/ExpressionManager.ts\\nvar ExpressionManager = class {\\n constructor(param) {\\n this.param = param;\\n this.parse_completed = false;\\n this.parse_started = false;\\n this.parsed_tree = new ParsedTree();\\n this._functionGenerator = new FunctionGenerator(this.param);\\n this.dependencies_controller = new DependenciesController(this.param);\\n }\\n parseExpression(expression) {\\n if (this.parse_started) {\\n throw new Error(`parse in progress for param ${this.param.path()}`);\\n }\\n this.parse_started = true;\\n this.parse_completed = false;\\n this.parsed_tree = this.parsed_tree || new ParsedTree();\\n this.reset();\\n if (this.param.type() == ParamType.STRING) {\\n this.parsed_tree.parse_expression_for_string_param(expression);\\n } else {\\n this.parsed_tree.parse_expression(expression);\\n }\\n this._functionGenerator.parse_tree(this.parsed_tree);\\n if (this._functionGenerator.error_message() == null) {\\n this.dependencies_controller.update(this._functionGenerator);\\n if (this.dependencies_controller.error_message) {\\n this.param.states.error.set(this.dependencies_controller.error_message);\\n } else {\\n this.parse_completed = true;\\n this.parse_started = false;\\n }\\n }\\n }\\n async computeFunction() {\\n if (this._computeAllowed()) {\\n try {\\n const new_value = await this._functionGenerator.evalFunction();\\n return new_value;\\n } catch (e) {\\n return;\\n }\\n } else {\\n return new Promise((resolve, reject) => {\\n resolve(null);\\n });\\n }\\n }\\n reset() {\\n this.parse_completed = false;\\n this.parse_started = false;\\n this.dependencies_controller.reset();\\n this._functionGenerator.reset();\\n }\\n is_errored() {\\n return this._functionGenerator.is_errored();\\n }\\n error_message() {\\n return this._functionGenerator.error_message();\\n }\\n _computeAllowed() {\\n return this._functionGenerator.evalAllowed();\\n }\\n updateFromMethodDependencyNameChange() {\\n this.expression_string_generator = this.expression_string_generator || new ExpressionStringGenerator(this.param);\\n const new_expression_string = this.expression_string_generator.parse_tree(this.parsed_tree);\\n if (new_expression_string) {\\n this.param.set(new_expression_string);\\n } else {\\n console.warn(\\\\\\\"failed to regenerate expression\\\\\\\");\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/utils/ExpressionController.ts\\nvar ExpressionController = class {\\n constructor(param) {\\n this.param = param;\\n }\\n dispose() {\\n this.param.scene().expressionsController.deregisterParam(this.param);\\n this._resetMethodDependencies();\\n }\\n _resetMethodDependencies() {\\n this._method_dependencies_by_graph_node_id?.forEach((method_dependency) => {\\n method_dependency.dispose();\\n });\\n this._method_dependencies_by_graph_node_id?.clear();\\n }\\n registerMethodDependency(method_dependency) {\\n this._method_dependencies_by_graph_node_id = this._method_dependencies_by_graph_node_id || new Map();\\n this._method_dependencies_by_graph_node_id.set(method_dependency.graphNodeId(), method_dependency);\\n }\\n active() {\\n return this._expression != null;\\n }\\n expression() {\\n return this._expression;\\n }\\n is_errored() {\\n if (this._manager) {\\n return this._manager.is_errored();\\n }\\n return false;\\n }\\n error_message() {\\n if (this._manager) {\\n return this._manager.error_message();\\n }\\n return null;\\n }\\n requires_entities() {\\n return this.param.options.isExpressionForEntities();\\n }\\n set_expression(expression, set_dirty = true) {\\n this.param.scene().missingExpressionReferencesController.deregisterParam(this.param);\\n this.param.scene().expressionsController.deregisterParam(this.param);\\n if (this._expression != expression) {\\n this._resetMethodDependencies();\\n this._expression = expression;\\n if (this._expression) {\\n this._manager = this._manager || new ExpressionManager(this.param);\\n this._manager.parseExpression(this._expression);\\n } else {\\n this._manager?.reset();\\n }\\n if (set_dirty) {\\n this.param.setDirty();\\n }\\n }\\n }\\n updateFromMethodDependencyNameChange() {\\n if (this._manager && this.active()) {\\n this._manager.updateFromMethodDependencyNameChange();\\n }\\n }\\n async computeExpression() {\\n if (this._manager && this.active()) {\\n const result = await this._manager.computeFunction();\\n return result;\\n }\\n }\\n async computeExpressionForEntities(entities, callback) {\\n this.set_entities(entities, callback);\\n await this.computeExpression();\\n if (this._manager?.error_message()) {\\n this.param.node.states.error.set(`expression evalution error: ${this._manager?.error_message()}`);\\n }\\n this.reset_entities();\\n }\\n computeExpressionForPoints(entities, callback) {\\n return this.computeExpressionForEntities(entities, callback);\\n }\\n computeExpressionForObjects(entities, callback) {\\n return this.computeExpressionForEntities(entities, callback);\\n }\\n entities() {\\n return this._entities;\\n }\\n entity_callback() {\\n return this._entity_callback;\\n }\\n set_entities(entities, callback) {\\n this._entities = entities;\\n this._entity_callback = callback;\\n }\\n reset_entities() {\\n this._entities = void 0;\\n this._entity_callback = void 0;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/_Numeric.ts\\nvar TypedNumericParam = class extends TypedParam {\\n isNumeric() {\\n return true;\\n }\\n isDefault() {\\n return this._raw_input == this._default_value;\\n }\\n _prefilter_invalid_raw_input(raw_input) {\\n if (CoreType.isArray(raw_input)) {\\n return raw_input[0];\\n } else {\\n return raw_input;\\n }\\n }\\n processRawInput() {\\n this.states.error.clear();\\n const converted = this.convert(this._raw_input);\\n if (converted != null) {\\n if (this._expression_controller) {\\n this._expression_controller.set_expression(void 0, false);\\n this.emitController.emit(ParamEvent.EXPRESSION_UPDATED);\\n }\\n if (converted != this._value) {\\n this._update_value(converted);\\n this.setSuccessorsDirty(this);\\n }\\n } else {\\n if (CoreType.isString(this._raw_input)) {\\n this._expression_controller = this._expression_controller || new ExpressionController(this);\\n if (this._raw_input != this._expression_controller.expression()) {\\n this._expression_controller.set_expression(this._raw_input);\\n this.emitController.emit(ParamEvent.EXPRESSION_UPDATED);\\n }\\n } else {\\n this.states.error.set(`param input is invalid (${this.path()})`);\\n }\\n }\\n }\\n async processComputation() {\\n if (this.expressionController?.active() && !this.expressionController.requires_entities()) {\\n const expression_result = await this.expressionController.computeExpression();\\n if (this.expressionController.is_errored()) {\\n this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);\\n } else {\\n const converted = this.convert(expression_result);\\n if (converted != null) {\\n if (this.states.error.active()) {\\n this.states.error.clear();\\n }\\n this._update_value(converted);\\n } else {\\n this.states.error.set(`expression returns an invalid type (${expression_result}) (${this.expressionController.expression()})`);\\n }\\n }\\n }\\n }\\n _update_value(new_value) {\\n this._value = new_value;\\n if (this.parent_param) {\\n this.parent_param.set_value_from_components();\\n }\\n this.options.executeCallback();\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n this.removeDirtyState();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Boolean.ts\\nvar BooleanParam = class extends TypedNumericParam {\\n static type() {\\n return ParamType.BOOLEAN;\\n }\\n defaultValueSerialized() {\\n if (CoreType.isString(this._default_value)) {\\n return this._default_value;\\n } else {\\n return this.convert(this._default_value) || false;\\n }\\n }\\n rawInputSerialized() {\\n return this._raw_input;\\n }\\n valueSerialized() {\\n return this.value;\\n }\\n _copy_value(param) {\\n this.set(param.value);\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n convert(raw_val) {\\n if (CoreType.isBoolean(raw_val)) {\\n return raw_val;\\n } else {\\n if (CoreType.isNumber(raw_val)) {\\n return raw_val >= 1;\\n } else {\\n if (CoreType.isString(raw_val)) {\\n if (CoreString.isBoolean(raw_val)) {\\n return CoreString.toBoolean(raw_val);\\n } else {\\n if (CoreString.isNumber(raw_val)) {\\n const parsed = parseFloat(raw_val);\\n return parsed >= 1;\\n }\\n }\\n }\\n }\\n }\\n return null;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Button.ts\\nvar ButtonParam = class extends TypedParam {\\n static type() {\\n return ParamType.BUTTON;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return this._raw_input;\\n }\\n valueSerialized() {\\n return this.value;\\n }\\n _copy_value(param) {\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return true;\\n }\\n static are_values_equal(val1, val2) {\\n return true;\\n }\\n async pressButton() {\\n if (this.node.isDirty() || this.node.cookController.isCooking()) {\\n await this.node.compute();\\n }\\n this.options.executeCallback();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Float.ts\\nvar FloatParam = class extends TypedNumericParam {\\n static type() {\\n return ParamType.FLOAT;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return this._raw_input;\\n }\\n valueSerialized() {\\n return this.value;\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n _prefilter_invalid_raw_input(raw_input) {\\n if (CoreType.isArray(raw_input)) {\\n return raw_input[0];\\n }\\n if (CoreType.isString(raw_input) && CoreString.isNumber(raw_input)) {\\n return parseFloat(raw_input);\\n }\\n return raw_input;\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n static convert(raw_val) {\\n if (CoreType.isNumber(raw_val)) {\\n return raw_val;\\n } else {\\n if (CoreType.isBoolean(raw_val)) {\\n return raw_val ? 1 : 0;\\n } else {\\n if (CoreString.isNumber(raw_val)) {\\n const parsed = parseFloat(raw_val);\\n if (CoreType.isNumber(parsed)) {\\n return parsed;\\n }\\n }\\n }\\n return null;\\n }\\n }\\n convert(raw_val) {\\n const result = FloatParam.convert(raw_val);\\n if (result) {\\n return this.options.ensureInRange(result);\\n } else {\\n return result;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/_Multiple.ts\\nvar TypedMultipleParam = class extends TypedParam {\\n constructor() {\\n super(...arguments);\\n this._components_contructor = FloatParam;\\n }\\n get components() {\\n return this._components;\\n }\\n isNumeric() {\\n return true;\\n }\\n isDefault() {\\n for (let c of this.components) {\\n if (!c.isDefault()) {\\n return false;\\n }\\n }\\n return true;\\n }\\n rawInput() {\\n return this._components.map((c) => c.rawInput());\\n }\\n rawInputSerialized() {\\n return this._components.map((c) => c.rawInputSerialized());\\n }\\n _copy_value(param) {\\n for (let i = 0; i < this.components.length; i++) {\\n const component = this.components[i];\\n const src_component = param.components[i];\\n component.copy_value(src_component);\\n }\\n }\\n initComponents() {\\n if (this._components != null) {\\n return;\\n }\\n let index = 0;\\n this._components = new Array(this.componentNames().length);\\n for (let component_name of this.componentNames()) {\\n const component = new this._components_contructor(this.scene(), this._node);\\n let default_val;\\n if (CoreType.isArray(this._default_value)) {\\n default_val = this._default_value[index];\\n } else {\\n default_val = this._default_value[component_name];\\n }\\n component.options.copy(this.options);\\n component.setInitValue(default_val);\\n component.setName(`${this.name()}${component_name}`);\\n component.set_parent_param(this);\\n this._components[index] = component;\\n index++;\\n }\\n }\\n async processComputation() {\\n await this.compute_components();\\n this.set_value_from_components();\\n }\\n set_value_from_components() {\\n }\\n hasExpression() {\\n for (let c of this.components) {\\n if (c.expressionController?.active()) {\\n return true;\\n }\\n }\\n return false;\\n }\\n async compute_components() {\\n const components = this.components;\\n const promises = [];\\n for (let c of components) {\\n if (c.isDirty()) {\\n promises.push(c.compute());\\n }\\n }\\n await Promise.all(promises);\\n this.removeDirtyState();\\n }\\n _prefilter_invalid_raw_input(raw_input) {\\n if (!CoreType.isArray(raw_input)) {\\n const number_or_string = raw_input;\\n const raw_input_wrapped_in_array = this.componentNames().map(() => number_or_string);\\n return raw_input_wrapped_in_array;\\n } else {\\n return raw_input;\\n }\\n }\\n processRawInput() {\\n const cooker = this.scene().cooker;\\n cooker.block();\\n const components = this.components;\\n for (let c of components) {\\n c.emitController.blockParentEmit();\\n }\\n const value = this._raw_input;\\n let prev_value = 0;\\n if (CoreType.isArray(value)) {\\n for (let i = 0; i < components.length; i++) {\\n let component_value = value[i];\\n if (component_value == null) {\\n component_value = prev_value;\\n }\\n components[i].set(component_value);\\n prev_value = component_value;\\n }\\n } else {\\n for (let i = 0; i < components.length; i++) {\\n const component_name = this.componentNames()[i];\\n let component_value = value[component_name];\\n if (component_value == null) {\\n component_value = prev_value;\\n }\\n components[i].set(component_value);\\n prev_value = component_value;\\n }\\n }\\n cooker.unblock();\\n for (let i = 0; i < components.length; i++) {\\n components[i].emitController.unblockParentEmit();\\n }\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/Color.ts\\nvar ColorConversion;\\n(function(ColorConversion2) {\\n ColorConversion2[\\\\\\\"NONE\\\\\\\"] = \\\\\\\"no conversion\\\\\\\";\\n ColorConversion2[\\\\\\\"GAMMA_TO_LINEAR\\\\\\\"] = \\\\\\\"gamma -> linear\\\\\\\";\\n ColorConversion2[\\\\\\\"LINEAR_TO_GAMMA\\\\\\\"] = \\\\\\\"linear -> gamma\\\\\\\";\\n ColorConversion2[\\\\\\\"SRGB_TO_LINEAR\\\\\\\"] = \\\\\\\"sRGB -> linear\\\\\\\";\\n ColorConversion2[\\\\\\\"LINEAR_TO_SRGB\\\\\\\"] = \\\\\\\"linear -> sRGB\\\\\\\";\\n})(ColorConversion || (ColorConversion = {}));\\nvar COLOR_CONVERSIONS = [\\n ColorConversion.NONE,\\n ColorConversion.GAMMA_TO_LINEAR,\\n ColorConversion.LINEAR_TO_GAMMA,\\n ColorConversion.SRGB_TO_LINEAR,\\n ColorConversion.LINEAR_TO_SRGB\\n];\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Color.ts\\nvar COMPONENT_NAMES_COLOR = [\\\\\\\"r\\\\\\\", \\\\\\\"g\\\\\\\", \\\\\\\"b\\\\\\\"];\\nvar ColorParam = class extends TypedMultipleParam {\\n constructor() {\\n super(...arguments);\\n this._value = new Color();\\n this._value_pre_conversion = new Color();\\n this._value_serialized_dirty = false;\\n this._value_serialized = [0, 0, 0];\\n this._value_pre_conversion_serialized = [0, 0, 0];\\n this._copied_value = [0, 0, 0];\\n }\\n static type() {\\n return ParamType.COLOR;\\n }\\n componentNames() {\\n return COMPONENT_NAMES_COLOR;\\n }\\n defaultValueSerialized() {\\n if (CoreType.isArray(this._default_value)) {\\n return this._default_value;\\n } else {\\n return this._default_value.toArray();\\n }\\n }\\n valueSerialized() {\\n this._update_value_serialized_if_required();\\n return this._value_serialized;\\n }\\n valuePreConversionSerialized() {\\n this._update_value_serialized_if_required();\\n return this._value_pre_conversion_serialized;\\n }\\n _copy_value(param) {\\n param.value.toArray(this._copied_value);\\n this.set(this._copied_value);\\n }\\n _clone_raw_input(raw_input) {\\n if (raw_input instanceof Color) {\\n return raw_input.clone();\\n } else {\\n const new_array = [raw_input[0], raw_input[1], raw_input[2]];\\n if (new_array[0] == null) {\\n new_array[0] = new_array[0] || 0;\\n }\\n if (new_array[1] == null) {\\n new_array[1] = new_array[1] || new_array[0];\\n }\\n if (new_array[2] == null) {\\n new_array[2] = new_array[2] || new_array[1];\\n }\\n return new_array;\\n }\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n if (raw_input1 instanceof Color) {\\n if (raw_input2 instanceof Color) {\\n return raw_input1.equals(raw_input2);\\n } else {\\n return raw_input1.r == raw_input2[0] && raw_input1.g == raw_input2[1] && raw_input1.b == raw_input2[2];\\n }\\n } else {\\n if (raw_input2 instanceof Color) {\\n return raw_input1[0] == raw_input2.r && raw_input1[1] == raw_input2.g && raw_input1[2] == raw_input2.b;\\n } else {\\n return raw_input1[0] == raw_input2[0] && raw_input1[1] == raw_input2[1] && raw_input1[2] == raw_input2[2];\\n }\\n }\\n }\\n static are_values_equal(val1, val2) {\\n return val1.equals(val2);\\n }\\n initComponents() {\\n super.initComponents();\\n this.r = this.components[0];\\n this.g = this.components[1];\\n this.b = this.components[2];\\n this._value_serialized_dirty = true;\\n }\\n _update_value_serialized_if_required() {\\n if (!this._value_serialized_dirty) {\\n return;\\n }\\n this._value_serialized[0] = this._value.r;\\n this._value_serialized[1] = this._value.g;\\n this._value_serialized[2] = this._value.b;\\n this._value_pre_conversion_serialized[0] = this._value_pre_conversion.r;\\n this._value_pre_conversion_serialized[1] = this._value_pre_conversion.g;\\n this._value_pre_conversion_serialized[2] = this._value_pre_conversion.b;\\n }\\n valuePreConversion() {\\n return this._value_pre_conversion;\\n }\\n set_value_from_components() {\\n this._value_pre_conversion.r = this.r.value;\\n this._value_pre_conversion.g = this.g.value;\\n this._value_pre_conversion.b = this.b.value;\\n this._value.copy(this._value_pre_conversion);\\n const conversion = this.options.colorConversion();\\n if (conversion != null && conversion != ColorConversion.NONE) {\\n switch (conversion) {\\n case ColorConversion.GAMMA_TO_LINEAR: {\\n this._value.convertGammaToLinear();\\n return;\\n }\\n case ColorConversion.LINEAR_TO_GAMMA: {\\n this._value.convertLinearToGamma();\\n return;\\n }\\n case ColorConversion.SRGB_TO_LINEAR: {\\n this._value.convertSRGBToLinear();\\n return;\\n }\\n case ColorConversion.LINEAR_TO_SRGB: {\\n this._value.convertLinearToSRGB();\\n return;\\n }\\n }\\n TypeAssert.unreachable(conversion);\\n }\\n this._value_serialized_dirty = true;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Folder.ts\\nvar FolderParam = class extends TypedParam {\\n static type() {\\n return ParamType.FOLDER;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return this._raw_input;\\n }\\n valueSerialized() {\\n return this.value;\\n }\\n _copy_value(param) {\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return true;\\n }\\n static are_values_equal(val1, val2) {\\n return true;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Integer.ts\\nvar IntegerParam = class extends TypedNumericParam {\\n static type() {\\n return ParamType.INTEGER;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return this._raw_input;\\n }\\n valueSerialized() {\\n return this.value;\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n _prefilter_invalid_raw_input(raw_input) {\\n if (CoreType.isArray(raw_input)) {\\n return raw_input[0];\\n }\\n if (CoreType.isString(raw_input) && CoreString.isNumber(raw_input)) {\\n return parseInt(raw_input);\\n }\\n return raw_input;\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n static convert(raw_val) {\\n if (CoreType.isNumber(raw_val)) {\\n return Math.round(raw_val);\\n } else {\\n if (CoreType.isBoolean(raw_val)) {\\n return raw_val ? 1 : 0;\\n } else {\\n if (CoreString.isNumber(raw_val)) {\\n const parsed = parseInt(raw_val);\\n if (CoreType.isNumber(parsed)) {\\n return parsed;\\n }\\n }\\n }\\n return null;\\n }\\n }\\n convert(raw_val) {\\n const result = IntegerParam.convert(raw_val);\\n if (result) {\\n return this.options.ensureInRange(result);\\n } else {\\n return result;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/DecomposedPath.ts\\nvar DecomposedPath = class {\\n constructor() {\\n this._index = -1;\\n this._path_elements = [];\\n this._named_nodes = [];\\n this._graph_node_ids = [];\\n this._node_element_by_graph_node_id = new Map();\\n }\\n reset() {\\n this._index = -1;\\n this._path_elements = [];\\n this._named_nodes = [];\\n this._graph_node_ids = [];\\n this._node_element_by_graph_node_id.clear();\\n }\\n add_node(name, node) {\\n this._index += 1;\\n if (name == node.name()) {\\n this._named_nodes[this._index] = node;\\n }\\n this._graph_node_ids[this._index] = node.graphNodeId();\\n this._node_element_by_graph_node_id.set(node.graphNodeId(), name);\\n }\\n add_path_element(path_element) {\\n this._index += 1;\\n this._path_elements[this._index] = path_element;\\n }\\n named_graph_nodes() {\\n return this._named_nodes;\\n }\\n named_nodes() {\\n const nodes = [];\\n for (let graph_node of this._named_nodes) {\\n if (graph_node) {\\n const node = graph_node;\\n if (node.nameController) {\\n nodes.push(node);\\n }\\n }\\n }\\n return nodes;\\n }\\n update_from_name_change(node) {\\n const named_graph_node_ids = this._named_nodes.map((n) => n?.graphNodeId());\\n if (named_graph_node_ids.includes(node.graphNodeId())) {\\n this._node_element_by_graph_node_id.set(node.graphNodeId(), node.name());\\n }\\n }\\n to_path() {\\n const elements = new Array(this._index);\\n for (let i = 0; i <= this._index; i++) {\\n const node = this._named_nodes[i];\\n if (node) {\\n const node_name = this._node_element_by_graph_node_id.get(node.graphNodeId());\\n if (node_name) {\\n elements[i] = node_name;\\n }\\n } else {\\n const path_element = this._path_elements[i];\\n if (path_element) {\\n elements[i] = path_element;\\n }\\n }\\n }\\n let joined_path = elements.join(CoreWalker.SEPARATOR);\\n const first_char = joined_path[0];\\n if (first_char) {\\n if (!CoreWalker.NON_LETTER_PREFIXES.includes(first_char)) {\\n joined_path = `${CoreWalker.SEPARATOR}${joined_path}`;\\n }\\n }\\n return joined_path;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/_BasePath.ts\\nvar TypedPathParam = class extends TypedParam {\\n constructor() {\\n super(...arguments);\\n this.decomposed_path = new DecomposedPath();\\n }\\n _handleReferences(node, path) {\\n this.scene().referencesController.setNamedNodesFromParam(this);\\n if (node) {\\n this.scene().referencesController.setReferenceFromParam(this, node);\\n } else {\\n this.scene().missingExpressionReferencesController.register(this, path);\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/OperatorPath.ts\\nvar OperatorPathMode;\\n(function(OperatorPathMode2) {\\n OperatorPathMode2[\\\\\\\"NODE\\\\\\\"] = \\\\\\\"NODE\\\\\\\";\\n OperatorPathMode2[\\\\\\\"PARAM\\\\\\\"] = \\\\\\\"PARAM\\\\\\\";\\n})(OperatorPathMode || (OperatorPathMode = {}));\\nvar OperatorPathParam = class extends TypedPathParam {\\n constructor() {\\n super(...arguments);\\n this._found_node = null;\\n this._found_node_with_expected_type = null;\\n this._found_param = null;\\n this._found_param_with_expected_type = null;\\n }\\n static type() {\\n return ParamType.OPERATOR_PATH;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return `${this._raw_input}`;\\n }\\n valueSerialized() {\\n return `${this.value}`;\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n isDefault() {\\n return this._value == this._default_value;\\n }\\n setNode(node) {\\n this.set(node.path());\\n }\\n processRawInput() {\\n if (this._value != this._raw_input) {\\n this._value = this._raw_input;\\n this.setDirty();\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n }\\n }\\n async processComputation() {\\n this.find_target();\\n }\\n find_target() {\\n if (!this.node) {\\n return;\\n }\\n const path = this._value;\\n let node = null;\\n let param = null;\\n const path_non_empty = path != null && path !== \\\\\\\"\\\\\\\";\\n const mode = this.options.paramSelectionOptions() ? OperatorPathMode.PARAM : OperatorPathMode.NODE;\\n this.scene().referencesController.resetReferenceFromParam(this);\\n this.decomposed_path.reset();\\n if (path_non_empty) {\\n if (mode == OperatorPathMode.PARAM) {\\n param = CoreWalker.findParam(this.node, path, this.decomposed_path);\\n } else {\\n node = CoreWalker.findNode(this.node, path, this.decomposed_path);\\n }\\n }\\n const current_found_entity = mode == OperatorPathMode.PARAM ? this._found_param : this._found_node;\\n const newly_found_entity = mode == OperatorPathMode.PARAM ? param : node;\\n this._handleReferences(node, path);\\n if (current_found_entity?.graphNodeId() !== newly_found_entity?.graphNodeId()) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n if (this._found_node) {\\n if (dependent_on_found_node) {\\n this.removeGraphInput(this._found_node);\\n } else {\\n }\\n }\\n if (mode == OperatorPathMode.PARAM) {\\n this._found_param = param;\\n this._found_node = null;\\n } else {\\n this._found_node = node;\\n this._found_param = null;\\n }\\n if (node) {\\n this._assign_found_node(node);\\n }\\n if (param) {\\n this._assign_found_param(param);\\n }\\n this.options.executeCallback();\\n }\\n this.removeDirtyState();\\n }\\n _assign_found_node(node) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n if (this._is_node_expected_context(node)) {\\n if (this._is_node_expected_type(node)) {\\n this._found_node_with_expected_type = node;\\n if (dependent_on_found_node) {\\n this.addGraphInput(node);\\n }\\n } else {\\n this.states.error.set(`node type is ${node.type()} but the params expects one of ${(this._expected_node_types() || []).join(\\\\\\\", \\\\\\\")}`);\\n }\\n } else {\\n this.states.error.set(`node context is ${node.context()} but the params expects a ${this._expected_context()}`);\\n }\\n }\\n _assign_found_param(param) {\\n if (this._is_param_expected_type(param)) {\\n this._found_param_with_expected_type = param;\\n } else {\\n this.states.error.set(`param type is ${param.type()} but the params expects a ${this._expected_param_type()}`);\\n }\\n }\\n found_node() {\\n return this._found_node;\\n }\\n found_param() {\\n return this._found_param;\\n }\\n found_node_with_context(context) {\\n return this._found_node_with_expected_type;\\n }\\n found_node_with_context_and_type(context, type_or_types) {\\n const node = this.found_node_with_context(context);\\n if (node) {\\n if (CoreType.isArray(type_or_types)) {\\n for (let type of type_or_types) {\\n if (node.type() == type) {\\n return node;\\n }\\n }\\n this.states.error.set(`expected node type to be ${type_or_types.join(\\\\\\\", \\\\\\\")}, but was instead ${node.type()}`);\\n } else {\\n const type = type_or_types;\\n if (node.type() == type) {\\n return node;\\n } else {\\n this.states.error.set(`expected node type to be ${type}, but was instead ${node.type()}`);\\n }\\n }\\n }\\n }\\n found_param_with_type(type) {\\n if (this._found_param_with_expected_type) {\\n return this._found_param_with_expected_type;\\n }\\n }\\n found_node_with_expected_type() {\\n return this._found_node_with_expected_type;\\n }\\n _expected_context() {\\n return this.options.nodeSelectionContext();\\n }\\n _is_node_expected_context(node) {\\n const expected_context = this._expected_context();\\n if (expected_context == null) {\\n return true;\\n }\\n const node_context = node.parent()?.childrenController?.context;\\n return expected_context == node_context;\\n }\\n _expected_node_types() {\\n return this.options.nodeSelectionTypes();\\n }\\n _expected_param_type() {\\n return this.options.paramSelectionType();\\n }\\n _is_node_expected_type(node) {\\n const expected_types = this._expected_node_types();\\n if (expected_types == null) {\\n return true;\\n }\\n return expected_types?.includes(node.type());\\n }\\n _is_param_expected_type(param) {\\n const expected_types = this._expected_node_types();\\n if (expected_types == null) {\\n return true;\\n }\\n return expected_types.includes(param.type());\\n }\\n notifyPathRebuildRequired(node) {\\n this.decomposed_path.update_from_name_change(node);\\n const new_path = this.decomposed_path.to_path();\\n this.set(new_path);\\n }\\n notifyTargetParamOwnerParamsUpdated(node) {\\n this.setDirty();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/ParamPath.ts\\nvar ParamPathParam = class extends TypedPathParam {\\n static type() {\\n return ParamType.PARAM_PATH;\\n }\\n initialize_param() {\\n this._value = new TypedParamPathParamValue();\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return `${this._raw_input}`;\\n }\\n valueSerialized() {\\n return `${this.value}`;\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n isDefault() {\\n return this._raw_input == this._default_value;\\n }\\n setParam(param) {\\n this.set(param.path());\\n }\\n processRawInput() {\\n if (this._value.path() != this._raw_input) {\\n this._value.set_path(this._raw_input);\\n this.find_target();\\n this.setDirty();\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n }\\n }\\n async processComputation() {\\n this.find_target();\\n }\\n find_target() {\\n if (!this.node) {\\n return;\\n }\\n const path = this._raw_input;\\n let param = null;\\n const path_non_empty = path != null && path !== \\\\\\\"\\\\\\\";\\n this.scene().referencesController.resetReferenceFromParam(this);\\n this.decomposed_path.reset();\\n if (path_non_empty) {\\n param = CoreWalker.findParam(this.node, path, this.decomposed_path);\\n }\\n const current_found_entity = this._value.param();\\n const newly_found_entity = param;\\n this._handleReferences(param, path);\\n if (current_found_entity?.graphNodeId() !== newly_found_entity?.graphNodeId()) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n const previously_found_node = this._value.param();\\n if (previously_found_node) {\\n if (dependent_on_found_node) {\\n this.removeGraphInput(previously_found_node);\\n } else {\\n }\\n }\\n if (param) {\\n this._assign_found_node(param);\\n } else {\\n this._value.set_param(null);\\n }\\n this.options.executeCallback();\\n }\\n this.removeDirtyState();\\n }\\n _assign_found_node(param) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n this._value.set_param(param);\\n if (dependent_on_found_node) {\\n this.addGraphInput(param);\\n }\\n }\\n notifyPathRebuildRequired(param) {\\n this.decomposed_path.update_from_name_change(param);\\n const new_path = this.decomposed_path.to_path();\\n this.set(new_path);\\n }\\n notifyTargetParamOwnerParamsUpdated(node) {\\n this.setDirty();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/NodePath.ts\\nvar NodePathParam = class extends TypedPathParam {\\n static type() {\\n return ParamType.NODE_PATH;\\n }\\n initialize_param() {\\n this._value = new TypedNodePathParamValue();\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n rawInputSerialized() {\\n return `${this._raw_input}`;\\n }\\n valueSerialized() {\\n return `${this.value}`;\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n isDefault() {\\n return this._raw_input == this._default_value;\\n }\\n setNode(node) {\\n this.set(node.path());\\n }\\n processRawInput() {\\n if (this._value.path() != this._raw_input) {\\n this._value.set_path(this._raw_input);\\n this._findTarget();\\n this.setDirty();\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n }\\n }\\n async processComputation() {\\n this._findTarget();\\n }\\n _findTarget() {\\n if (!this.node) {\\n return;\\n }\\n const path = this._raw_input;\\n let node = null;\\n const path_non_empty = path != null && path !== \\\\\\\"\\\\\\\";\\n this.scene().referencesController.resetReferenceFromParam(this);\\n this.decomposed_path.reset();\\n if (path_non_empty) {\\n node = CoreWalker.findNode(this.node, path, this.decomposed_path);\\n }\\n const current_found_entity = this._value.node();\\n const newly_found_entity = node;\\n this._handleReferences(node, path);\\n if (current_found_entity?.graphNodeId() !== newly_found_entity?.graphNodeId()) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n const previously_found_node = this._value.node();\\n if (previously_found_node) {\\n if (dependent_on_found_node) {\\n this.removeGraphInput(previously_found_node);\\n } else {\\n }\\n }\\n if (node) {\\n this._assign_found_node(node);\\n } else {\\n this._value.set_node(null);\\n }\\n this.options.executeCallback();\\n }\\n if (path_non_empty && !node && this.scene().loadingController.loaded()) {\\n if (path_non_empty) {\\n this.states.error.set(`no node found at path '${path}'`);\\n }\\n }\\n this.removeDirtyState();\\n }\\n _assign_found_node(node) {\\n const dependent_on_found_node = this.options.dependentOnFoundNode();\\n if (this._isNodeExpectedContext(node)) {\\n if (this._is_node_expected_type(node)) {\\n this.states.error.clear();\\n this._value.set_node(node);\\n if (dependent_on_found_node) {\\n this.addGraphInput(node);\\n }\\n } else {\\n this.states.error.set(`node type is ${node.type()} but the params expects one of ${(this._expected_node_types() || []).join(\\\\\\\", \\\\\\\")}`);\\n }\\n } else {\\n this.states.error.set(`node context is ${node.context()} but the params expects a ${this._expectedContext()}`);\\n }\\n }\\n _expectedContext() {\\n return this.options.nodeSelectionContext();\\n }\\n _isNodeExpectedContext(node) {\\n const expected_context = this._expectedContext();\\n if (expected_context == null) {\\n return true;\\n }\\n const node_context = node.parent()?.childrenController?.context;\\n return expected_context == node_context;\\n }\\n _expected_node_types() {\\n return this.options.nodeSelectionTypes();\\n }\\n _is_node_expected_type(node) {\\n const expected_types = this._expected_node_types();\\n if (expected_types == null) {\\n return true;\\n }\\n return expected_types?.includes(node.type());\\n }\\n notifyPathRebuildRequired(node) {\\n this.decomposed_path.update_from_name_change(node);\\n const newPath = this.decomposed_path.to_path();\\n this.set(newPath);\\n }\\n notifyTargetParamOwnerParamsUpdated(node) {\\n this.setDirty();\\n }\\n};\\n\\n// ../../../node_modules/three/src/textures/DataTexture.js\\nvar DataTexture = class extends Texture {\\n constructor(data = null, width = 1, height = 1, format, type, mapping, wrapS, wrapT, magFilter = NearestFilter, minFilter = NearestFilter, anisotropy, encoding) {\\n super(null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding);\\n this.image = {data, width, height};\\n this.magFilter = magFilter;\\n this.minFilter = minFilter;\\n this.generateMipmaps = false;\\n this.flipY = false;\\n this.unpackAlignment = 1;\\n this.needsUpdate = true;\\n }\\n};\\nDataTexture.prototype.isDataTexture = true;\\n\\n// ../../../node_modules/three/src/math/Interpolant.js\\nvar Interpolant = class {\\n constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) {\\n this.parameterPositions = parameterPositions;\\n this._cachedIndex = 0;\\n this.resultBuffer = resultBuffer !== void 0 ? resultBuffer : new sampleValues.constructor(sampleSize);\\n this.sampleValues = sampleValues;\\n this.valueSize = sampleSize;\\n this.settings = null;\\n this.DefaultSettings_ = {};\\n }\\n evaluate(t) {\\n const pp = this.parameterPositions;\\n let i1 = this._cachedIndex, t1 = pp[i1], t0 = pp[i1 - 1];\\n validate_interval: {\\n seek: {\\n let right;\\n linear_scan: {\\n forward_scan:\\n if (!(t < t1)) {\\n for (let giveUpAt = i1 + 2; ; ) {\\n if (t1 === void 0) {\\n if (t < t0)\\n break forward_scan;\\n i1 = pp.length;\\n this._cachedIndex = i1;\\n return this.afterEnd_(i1 - 1, t, t0);\\n }\\n if (i1 === giveUpAt)\\n break;\\n t0 = t1;\\n t1 = pp[++i1];\\n if (t < t1) {\\n break seek;\\n }\\n }\\n right = pp.length;\\n break linear_scan;\\n }\\n if (!(t >= t0)) {\\n const t1global = pp[1];\\n if (t < t1global) {\\n i1 = 2;\\n t0 = t1global;\\n }\\n for (let giveUpAt = i1 - 2; ; ) {\\n if (t0 === void 0) {\\n this._cachedIndex = 0;\\n return this.beforeStart_(0, t, t1);\\n }\\n if (i1 === giveUpAt)\\n break;\\n t1 = t0;\\n t0 = pp[--i1 - 1];\\n if (t >= t0) {\\n break seek;\\n }\\n }\\n right = i1;\\n i1 = 0;\\n break linear_scan;\\n }\\n break validate_interval;\\n }\\n while (i1 < right) {\\n const mid = i1 + right >>> 1;\\n if (t < pp[mid]) {\\n right = mid;\\n } else {\\n i1 = mid + 1;\\n }\\n }\\n t1 = pp[i1];\\n t0 = pp[i1 - 1];\\n if (t0 === void 0) {\\n this._cachedIndex = 0;\\n return this.beforeStart_(0, t, t1);\\n }\\n if (t1 === void 0) {\\n i1 = pp.length;\\n this._cachedIndex = i1;\\n return this.afterEnd_(i1 - 1, t0, t);\\n }\\n }\\n this._cachedIndex = i1;\\n this.intervalChanged_(i1, t0, t1);\\n }\\n return this.interpolate_(i1, t0, t, t1);\\n }\\n getSettings_() {\\n return this.settings || this.DefaultSettings_;\\n }\\n copySampleValue_(index) {\\n const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, offset = index * stride;\\n for (let i = 0; i !== stride; ++i) {\\n result[i] = values[offset + i];\\n }\\n return result;\\n }\\n interpolate_() {\\n throw new Error(\\\\\\\"call to abstract method\\\\\\\");\\n }\\n intervalChanged_() {\\n }\\n};\\nInterpolant.prototype.beforeStart_ = Interpolant.prototype.copySampleValue_;\\nInterpolant.prototype.afterEnd_ = Interpolant.prototype.copySampleValue_;\\n\\n// ../../../node_modules/three/src/math/interpolants/CubicInterpolant.js\\nvar CubicInterpolant = class extends Interpolant {\\n constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) {\\n super(parameterPositions, sampleValues, sampleSize, resultBuffer);\\n this._weightPrev = -0;\\n this._offsetPrev = -0;\\n this._weightNext = -0;\\n this._offsetNext = -0;\\n this.DefaultSettings_ = {\\n endingStart: ZeroCurvatureEnding,\\n endingEnd: ZeroCurvatureEnding\\n };\\n }\\n intervalChanged_(i1, t0, t1) {\\n const pp = this.parameterPositions;\\n let iPrev = i1 - 2, iNext = i1 + 1, tPrev = pp[iPrev], tNext = pp[iNext];\\n if (tPrev === void 0) {\\n switch (this.getSettings_().endingStart) {\\n case ZeroSlopeEnding:\\n iPrev = i1;\\n tPrev = 2 * t0 - t1;\\n break;\\n case WrapAroundEnding:\\n iPrev = pp.length - 2;\\n tPrev = t0 + pp[iPrev] - pp[iPrev + 1];\\n break;\\n default:\\n iPrev = i1;\\n tPrev = t1;\\n }\\n }\\n if (tNext === void 0) {\\n switch (this.getSettings_().endingEnd) {\\n case ZeroSlopeEnding:\\n iNext = i1;\\n tNext = 2 * t1 - t0;\\n break;\\n case WrapAroundEnding:\\n iNext = 1;\\n tNext = t1 + pp[1] - pp[0];\\n break;\\n default:\\n iNext = i1 - 1;\\n tNext = t0;\\n }\\n }\\n const halfDt = (t1 - t0) * 0.5, stride = this.valueSize;\\n this._weightPrev = halfDt / (t0 - tPrev);\\n this._weightNext = halfDt / (tNext - t1);\\n this._offsetPrev = iPrev * stride;\\n this._offsetNext = iNext * stride;\\n }\\n interpolate_(i1, t0, t, t1) {\\n const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, o1 = i1 * stride, o0 = o1 - stride, oP = this._offsetPrev, oN = this._offsetNext, wP = this._weightPrev, wN = this._weightNext, p = (t - t0) / (t1 - t0), pp = p * p, ppp = pp * p;\\n const sP = -wP * ppp + 2 * wP * pp - wP * p;\\n const s0 = (1 + wP) * ppp + (-1.5 - 2 * wP) * pp + (-0.5 + wP) * p + 1;\\n const s1 = (-1 - wN) * ppp + (1.5 + wN) * pp + 0.5 * p;\\n const sN = wN * ppp - wN * pp;\\n for (let i = 0; i !== stride; ++i) {\\n result[i] = sP * values[oP + i] + s0 * values[o0 + i] + s1 * values[o1 + i] + sN * values[oN + i];\\n }\\n return result;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/ramp/RampValue.ts\\nvar RampPoint = class {\\n constructor(_position3 = 0, _value = 0) {\\n this._position = _position3;\\n this._value = _value;\\n }\\n toJSON() {\\n return {\\n position: this._position,\\n value: this._value\\n };\\n }\\n position() {\\n return this._position;\\n }\\n value() {\\n return this._value;\\n }\\n copy(point) {\\n this._position = point.position();\\n this._value = point.value();\\n }\\n clone() {\\n const point = new RampPoint();\\n point.copy(this);\\n return point;\\n }\\n isEqual(other_point) {\\n return this._position == other_point.position() && this._value == other_point.value();\\n }\\n isEqualJSON(json) {\\n return this._position == json.position && this._value == json.value;\\n }\\n fromJSON(json) {\\n this._position = json.position;\\n this._value = json.value;\\n }\\n static areEqualJSON(json1, json2) {\\n return json1.position == json2.position && json1.value == json2.value;\\n }\\n static fromJSON(json) {\\n return new RampPoint(json.position, json.value);\\n }\\n};\\nvar RampInterpolation;\\n(function(RampInterpolation2) {\\n RampInterpolation2[\\\\\\\"LINEAR\\\\\\\"] = \\\\\\\"linear\\\\\\\";\\n})(RampInterpolation || (RampInterpolation = {}));\\nvar RampValue = class {\\n constructor(_interpolation = RampInterpolation.LINEAR, _points2 = []) {\\n this._interpolation = _interpolation;\\n this._points = _points2;\\n this._uuid = generateUUID();\\n }\\n uuid() {\\n return this._uuid;\\n }\\n interpolation() {\\n return this._interpolation;\\n }\\n points() {\\n return this._points;\\n }\\n static fromJSON(json) {\\n const points2 = [];\\n for (let jsonPoint of json.points) {\\n points2.push(RampPoint.fromJSON(jsonPoint));\\n }\\n return new RampValue(json.interpolation, points2);\\n }\\n toJSON() {\\n return {\\n interpolation: this._interpolation,\\n points: this._points.map((p) => p.toJSON())\\n };\\n }\\n clone() {\\n const ramp = new RampValue();\\n ramp.copy(this);\\n return ramp;\\n }\\n copy(ramp) {\\n this._interpolation = ramp.interpolation();\\n let index = 0;\\n for (let point of ramp.points()) {\\n const current_point = this._points[index];\\n if (current_point) {\\n current_point.copy(point);\\n } else {\\n this._points.push(point.clone());\\n }\\n index += 1;\\n }\\n }\\n is_equal(other_ramp_value) {\\n if (this._interpolation != other_ramp_value.interpolation()) {\\n return false;\\n }\\n const other_points = other_ramp_value.points();\\n if (this._points.length != other_points.length) {\\n return false;\\n }\\n let index = 0;\\n for (let point of this._points) {\\n const other_point = other_points[index];\\n if (!point.isEqual(other_point)) {\\n return false;\\n }\\n index += 1;\\n }\\n return true;\\n }\\n is_equal_json(json) {\\n if (this._interpolation != json.interpolation) {\\n return false;\\n }\\n if (this._points.length != json.points.length) {\\n return false;\\n }\\n let index = 0;\\n for (let point of this._points) {\\n const other_point = json.points[index];\\n if (!point.isEqualJSON(other_point)) {\\n return false;\\n }\\n index += 1;\\n }\\n return true;\\n }\\n static are_json_equal(json1, json2) {\\n if (json1.interpolation != json2.interpolation) {\\n return false;\\n }\\n if (json1.points.length != json2.points.length) {\\n return false;\\n }\\n let index = 0;\\n for (let point1 of json1.points) {\\n const point2 = json2.points[index];\\n if (!RampPoint.areEqualJSON(point1, point2)) {\\n return false;\\n }\\n index += 1;\\n }\\n return true;\\n }\\n from_json(json) {\\n this._interpolation = json.interpolation;\\n let index = 0;\\n for (let json_point of json.points) {\\n const current_point = this._points[index];\\n if (current_point) {\\n current_point.fromJSON(json_point);\\n } else {\\n this._points.push(RampPoint.fromJSON(json_point));\\n }\\n index += 1;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Ramp.ts\\nvar TEXTURE_WIDTH = 1024;\\nvar TEXTURE_HEIGHT = 1;\\nvar TEXTURE_SIZE = TEXTURE_WIDTH * TEXTURE_HEIGHT;\\nvar TEXTURE_BYTES_MULT = 255;\\nvar RampParam2 = class extends TypedParam {\\n constructor() {\\n super(...arguments);\\n this._texture_data = new Uint8Array(3 * TEXTURE_SIZE);\\n this._ramp_texture = new DataTexture(this._texture_data, TEXTURE_WIDTH, TEXTURE_HEIGHT, RGBFormat);\\n }\\n static type() {\\n return ParamType.RAMP;\\n }\\n defaultValueSerialized() {\\n if (this._default_value instanceof RampValue) {\\n return this._default_value.toJSON();\\n } else {\\n return this._default_value;\\n }\\n }\\n _clone_raw_input(raw_input) {\\n if (raw_input instanceof RampValue) {\\n return raw_input.clone();\\n } else {\\n return RampValue.fromJSON(raw_input).toJSON();\\n }\\n }\\n rawInputSerialized() {\\n if (this._raw_input instanceof RampValue) {\\n return this._raw_input.toJSON();\\n } else {\\n return RampValue.fromJSON(this._raw_input).toJSON();\\n }\\n }\\n valueSerialized() {\\n return this.value.toJSON();\\n }\\n _copy_value(param) {\\n this.set(param.valueSerialized());\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n if (raw_input1 instanceof RampValue) {\\n if (raw_input2 instanceof RampValue) {\\n return raw_input1.is_equal(raw_input2);\\n } else {\\n return raw_input1.is_equal_json(raw_input2);\\n }\\n } else {\\n if (raw_input2 instanceof RampValue) {\\n return raw_input2.is_equal_json(raw_input1);\\n } else {\\n return RampValue.are_json_equal(raw_input1, raw_input2);\\n }\\n }\\n }\\n static are_values_equal(val1, val2) {\\n return val1.is_equal(val2);\\n }\\n isDefault() {\\n if (this._default_value instanceof RampValue) {\\n return this.value.is_equal(this._default_value);\\n } else {\\n return this.value.is_equal_json(this._default_value);\\n }\\n }\\n processRawInput() {\\n if (this._raw_input instanceof RampValue) {\\n if (!this._value) {\\n this._value = this._raw_input;\\n } else {\\n this._value.copy(this._raw_input);\\n }\\n } else {\\n if (!this._value) {\\n this._value = RampValue.fromJSON(this._raw_input);\\n } else {\\n this._value.from_json(this._raw_input);\\n }\\n }\\n this._resetRampInterpolant();\\n this._updateRampTexture();\\n this.options.executeCallback();\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n this.setSuccessorsDirty(this);\\n }\\n hasExpression() {\\n return false;\\n }\\n _resetRampInterpolant() {\\n this._ramp_interpolant = void 0;\\n }\\n rampTexture() {\\n return this._ramp_texture;\\n }\\n _updateRampTexture() {\\n this._updateRampTextureData();\\n this.rampTexture().needsUpdate = true;\\n }\\n _updateRampTextureData() {\\n let stride = 0;\\n let position = 0;\\n let value = 0;\\n for (var i = 0; i < TEXTURE_SIZE; i++) {\\n stride = i * 3;\\n position = i / TEXTURE_WIDTH;\\n value = this.valueAtPosition(position);\\n if (value <= 0) {\\n this._texture_data[stride + 0] = (clamp(value, -1, 0) + 1) * TEXTURE_BYTES_MULT;\\n this._texture_data[stride + 1] = 0;\\n this._texture_data[stride + 2] = 0;\\n } else {\\n if (value <= 1) {\\n this._texture_data[stride + 0] = TEXTURE_BYTES_MULT;\\n this._texture_data[stride + 1] = clamp(value, 0, 1) * TEXTURE_BYTES_MULT;\\n this._texture_data[stride + 2] = 0;\\n } else {\\n this._texture_data[stride + 0] = TEXTURE_BYTES_MULT;\\n this._texture_data[stride + 1] = TEXTURE_BYTES_MULT;\\n this._texture_data[stride + 2] = (clamp(value, 1, 2) - 1) * TEXTURE_BYTES_MULT;\\n }\\n }\\n }\\n }\\n static createInterpolant(positions, values) {\\n const valuesCount = 1;\\n const interpolatedValues = new Float32Array(valuesCount);\\n return new CubicInterpolant(positions, values, valuesCount, interpolatedValues);\\n }\\n interpolant() {\\n return this._ramp_interpolant = this._ramp_interpolant || this._createInterpolant();\\n }\\n _createInterpolant() {\\n const points2 = this.value.points();\\n const sortedPoints = ArrayUtils.sortBy(points2, (point) => point.position());\\n const positions = new Float32Array(sortedPoints.length);\\n const values = new Float32Array(sortedPoints.length);\\n let i = 0;\\n for (let sortedPoint of sortedPoints) {\\n positions[i] = sortedPoint.position();\\n values[i] = sortedPoint.value();\\n i++;\\n }\\n return RampParam2.createInterpolant(positions, values);\\n }\\n valueAtPosition(position) {\\n return this.interpolant().evaluate(position)[0];\\n }\\n};\\nvar RampParam = RampParam2;\\nRampParam.DEFAULT_VALUE = new RampValue(RampInterpolation.LINEAR, [new RampPoint(0, 0), new RampPoint(1, 1)]);\\nRampParam.DEFAULT_VALUE_JSON = RampParam2.DEFAULT_VALUE.toJSON();\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/String.ts\\nvar StringParam = class extends TypedParam {\\n static type() {\\n return ParamType.STRING;\\n }\\n defaultValueSerialized() {\\n return this._default_value;\\n }\\n _clone_raw_input(raw_input) {\\n return `${raw_input}`;\\n }\\n rawInputSerialized() {\\n return `${this._raw_input}`;\\n }\\n valueSerialized() {\\n return `${this.value}`;\\n }\\n _copy_value(param) {\\n this.set(param.value);\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n return raw_input1 == raw_input2;\\n }\\n static are_values_equal(val1, val2) {\\n return val1 == val2;\\n }\\n isDefault() {\\n return this._raw_input == this._default_value;\\n }\\n convert(raw_val) {\\n if (CoreType.isString(raw_val)) {\\n return raw_val;\\n }\\n return `${raw_val}`;\\n }\\n rawInput() {\\n return this._raw_input;\\n }\\n processRawInput() {\\n this.states.error.clear();\\n if (this._value_elements(this._raw_input).length >= 3) {\\n this._expression_controller = this._expression_controller || new ExpressionController(this);\\n if (this._raw_input != this._expression_controller.expression()) {\\n this._expression_controller.set_expression(this._raw_input);\\n this.setDirty();\\n this.emitController.emit(ParamEvent.EXPRESSION_UPDATED);\\n }\\n } else {\\n if (this._raw_input != this._value) {\\n this._value = this._raw_input;\\n this.removeDirtyState();\\n this.setSuccessorsDirty(this);\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n this.options.executeCallback();\\n if (this._expression_controller) {\\n this._expression_controller.set_expression(void 0, false);\\n this.emitController.emit(ParamEvent.EXPRESSION_UPDATED);\\n }\\n }\\n }\\n }\\n async processComputation() {\\n if (this.expressionController?.active() && !this.expressionController.requires_entities()) {\\n const expression_result = await this.expressionController.computeExpression();\\n if (this.expressionController.is_errored()) {\\n this.states.error.set(`expression error: ${this.expressionController.error_message()}`);\\n } else {\\n const converted = this.convert(expression_result);\\n if (converted != null) {\\n this._value = converted;\\n this.emitController.emit(ParamEvent.VALUE_UPDATED);\\n this.options.executeCallback();\\n } else {\\n this.states.error.set(`expression returns an invalid type (${expression_result})`);\\n }\\n this.removeDirtyState();\\n }\\n }\\n }\\n _value_elements(v) {\\n return ParsedTree.string_value_elements(v);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Vector2.ts\\nvar COMPONENT_NAMES_VECTOR2 = [\\\\\\\"x\\\\\\\", \\\\\\\"y\\\\\\\"];\\nvar Vector2Param = class extends TypedMultipleParam {\\n constructor() {\\n super(...arguments);\\n this._value = new Vector2();\\n this._copied_value = [0, 0];\\n }\\n static type() {\\n return ParamType.VECTOR2;\\n }\\n componentNames() {\\n return COMPONENT_NAMES_VECTOR2;\\n }\\n defaultValueSerialized() {\\n if (CoreType.isArray(this._default_value)) {\\n return this._default_value;\\n } else {\\n return this._default_value.toArray();\\n }\\n }\\n valueSerialized() {\\n return this.value.toArray();\\n }\\n _copy_value(param) {\\n param.value.toArray(this._copied_value);\\n this.set(this._copied_value);\\n }\\n _clone_raw_input(raw_input) {\\n if (raw_input instanceof Vector2) {\\n return raw_input.clone();\\n } else {\\n const new_array = [raw_input[0], raw_input[1]];\\n if (new_array[0] == null) {\\n new_array[0] = new_array[0] || 0;\\n }\\n if (new_array[1] == null) {\\n new_array[1] = new_array[1] || new_array[0];\\n }\\n return new_array;\\n }\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n if (raw_input1 instanceof Vector2) {\\n if (raw_input2 instanceof Vector2) {\\n return raw_input1.equals(raw_input2);\\n } else {\\n return raw_input1.x == raw_input2[0] && raw_input1.y == raw_input2[1];\\n }\\n } else {\\n if (raw_input2 instanceof Vector2) {\\n return raw_input1[0] == raw_input2.x && raw_input1[1] == raw_input2.y;\\n } else {\\n return raw_input1[0] == raw_input2[0] && raw_input1[1] == raw_input2[1];\\n }\\n }\\n }\\n static are_values_equal(val1, val2) {\\n return val1.equals(val2);\\n }\\n initComponents() {\\n super.initComponents();\\n this.x = this.components[0];\\n this.y = this.components[1];\\n }\\n set_value_from_components() {\\n this._value.x = this.x.value;\\n this._value.y = this.y.value;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Vector3.ts\\nvar COMPONENT_NAMES_VECTOR3 = [\\\\\\\"x\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"z\\\\\\\"];\\nvar Vector3Param = class extends TypedMultipleParam {\\n constructor() {\\n super(...arguments);\\n this._value = new Vector3();\\n this._copied_value = [0, 0, 0];\\n }\\n static type() {\\n return ParamType.VECTOR3;\\n }\\n componentNames() {\\n return COMPONENT_NAMES_VECTOR3;\\n }\\n defaultValueSerialized() {\\n if (CoreType.isArray(this._default_value)) {\\n return this._default_value;\\n } else {\\n return this._default_value.toArray();\\n }\\n }\\n valueSerialized() {\\n return this.value.toArray();\\n }\\n _copy_value(param) {\\n param.value.toArray(this._copied_value);\\n this.set(this._copied_value);\\n }\\n _clone_raw_input(raw_input) {\\n if (raw_input instanceof Vector3) {\\n return raw_input.clone();\\n } else {\\n const new_array = [raw_input[0], raw_input[1], raw_input[2]];\\n if (new_array[0] == null) {\\n new_array[0] = new_array[0] || 0;\\n }\\n if (new_array[1] == null) {\\n new_array[1] = new_array[1] || new_array[0];\\n }\\n if (new_array[2] == null) {\\n new_array[2] = new_array[2] || new_array[1];\\n }\\n return new_array;\\n }\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n if (raw_input1 instanceof Vector3) {\\n if (raw_input2 instanceof Vector3) {\\n return raw_input1.equals(raw_input2);\\n } else {\\n return raw_input1.x == raw_input2[0] && raw_input1.y == raw_input2[1] && raw_input1.z == raw_input2[2];\\n }\\n } else {\\n if (raw_input2 instanceof Vector3) {\\n return raw_input1[0] == raw_input2.x && raw_input1[1] == raw_input2.y && raw_input1[2] == raw_input2.z;\\n } else {\\n return raw_input1[0] == raw_input2[0] && raw_input1[1] == raw_input2[1] && raw_input1[2] == raw_input2[2];\\n }\\n }\\n }\\n static are_values_equal(val1, val2) {\\n return val1.equals(val2);\\n }\\n initComponents() {\\n super.initComponents();\\n this.x = this.components[0];\\n this.y = this.components[1];\\n this.z = this.components[2];\\n }\\n set_value_from_components() {\\n this._value.x = this.x.value;\\n this._value.y = this.y.value;\\n this._value.z = this.z.value;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/Vector4.ts\\nvar COMPONENT_NAMES_VECTOR4 = [\\\\\\\"x\\\\\\\", \\\\\\\"y\\\\\\\", \\\\\\\"z\\\\\\\", \\\\\\\"w\\\\\\\"];\\nvar Vector4Param = class extends TypedMultipleParam {\\n constructor() {\\n super(...arguments);\\n this._value = new Vector4();\\n this._copied_value = [0, 0, 0, 0];\\n }\\n static type() {\\n return ParamType.VECTOR4;\\n }\\n componentNames() {\\n return COMPONENT_NAMES_VECTOR4;\\n }\\n defaultValueSerialized() {\\n if (CoreType.isArray(this._default_value)) {\\n return this._default_value;\\n } else {\\n return this._default_value.toArray();\\n }\\n }\\n valueSerialized() {\\n return this.value.toArray();\\n }\\n _copy_value(param) {\\n param.value.toArray(this._copied_value);\\n this.set(this._copied_value);\\n }\\n _clone_raw_input(raw_input) {\\n if (raw_input instanceof Vector4) {\\n return raw_input.clone();\\n } else {\\n const new_array = [raw_input[0], raw_input[1], raw_input[2], raw_input[3]];\\n if (new_array[0] == null) {\\n new_array[0] = new_array[0] || 0;\\n }\\n if (new_array[1] == null) {\\n new_array[1] = new_array[1] || new_array[0];\\n }\\n if (new_array[2] == null) {\\n new_array[2] = new_array[2] || new_array[1];\\n }\\n if (new_array[3] == null) {\\n new_array[3] = new_array[3] || new_array[2];\\n }\\n return new_array;\\n }\\n }\\n static are_raw_input_equal(raw_input1, raw_input2) {\\n if (raw_input1 instanceof Vector4) {\\n if (raw_input2 instanceof Vector4) {\\n return raw_input1.equals(raw_input2);\\n } else {\\n return raw_input1.x == raw_input2[0] && raw_input1.y == raw_input2[1] && raw_input1.z == raw_input2[2] && raw_input1.w == raw_input2[3];\\n }\\n } else {\\n if (raw_input2 instanceof Vector4) {\\n return raw_input1[0] == raw_input2.x && raw_input1[1] == raw_input2.y && raw_input1[2] == raw_input2.z && raw_input1[3] == raw_input2.w;\\n } else {\\n return raw_input1[0] == raw_input2[0] && raw_input1[1] == raw_input2[1] && raw_input1[2] == raw_input2[2] && raw_input1[3] == raw_input2[3];\\n }\\n }\\n }\\n static are_values_equal(val1, val2) {\\n return val1.equals(val2);\\n }\\n initComponents() {\\n super.initComponents();\\n this.x = this.components[0];\\n this.y = this.components[1];\\n this.z = this.components[2];\\n this.w = this.components[3];\\n }\\n set_value_from_components() {\\n this._value.x = this.x.value;\\n this._value.y = this.y.value;\\n this._value.z = this.z.value;\\n this._value.w = this.w.value;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/params/types/ParamConstructorByType.ts\\nvar ParamConstructorByType = {\\n [ParamType.BOOLEAN]: BooleanParam,\\n [ParamType.BUTTON]: ButtonParam,\\n [ParamType.COLOR]: ColorParam,\\n [ParamType.FLOAT]: FloatParam,\\n [ParamType.FOLDER]: FolderParam,\\n [ParamType.INTEGER]: IntegerParam,\\n [ParamType.OPERATOR_PATH]: OperatorPathParam,\\n [ParamType.PARAM_PATH]: ParamPathParam,\\n [ParamType.NODE_PATH]: NodePathParam,\\n [ParamType.RAMP]: RampParam,\\n [ParamType.STRING]: StringParam,\\n [ParamType.VECTOR2]: Vector2Param,\\n [ParamType.VECTOR3]: Vector3Param,\\n [ParamType.VECTOR4]: Vector4Param\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/params/ParamsLabelController.ts\\nvar ParamsLabelController = class {\\n dispose() {\\n this._callback = void 0;\\n }\\n params() {\\n return this._params;\\n }\\n callback() {\\n return this._callback;\\n }\\n init(params, callback) {\\n this._params = params;\\n if (callback) {\\n this._callback = callback;\\n } else {\\n const param = this._params[0];\\n switch (param.type()) {\\n case ParamType.STRING:\\n return this._handle_string_param(param);\\n case ParamType.OPERATOR_PATH:\\n return this._handle_operator_path_param(param);\\n case ParamType.NODE_PATH:\\n return this._handle_node_path_param(param);\\n case ParamType.PARAM_PATH:\\n return this._handle_param_path_param(param);\\n case ParamType.FLOAT:\\n return this._handle_number_param(param);\\n case ParamType.INTEGER:\\n return this._handle_number_param(param);\\n }\\n }\\n }\\n _handle_string_param(param) {\\n this._callback = () => {\\n return param.value;\\n };\\n }\\n _handle_operator_path_param(param) {\\n this._callback = () => {\\n return param.value;\\n };\\n }\\n _handle_node_path_param(param) {\\n this._callback = () => {\\n return param.value.path();\\n };\\n }\\n _handle_param_path_param(param) {\\n this._callback = () => {\\n return param.value.path();\\n };\\n }\\n _handle_number_param(param) {\\n this._callback = () => {\\n return `${param.value}`;\\n };\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/params/ParamsController.ts\\nvar NODE_SIMPLE_NAME = \\\\\\\"params\\\\\\\";\\nvar ParamsController = class {\\n constructor(node) {\\n this.node = node;\\n this._param_create_mode = false;\\n this._params_created = false;\\n this._params_by_name = {};\\n this._params_list = [];\\n this._param_names = [];\\n this._non_spare_params = [];\\n this._spare_params = [];\\n this._non_spare_param_names = [];\\n this._spare_param_names = [];\\n this._params_added_since_last_params_eval = false;\\n }\\n get label() {\\n return this._label_controller = this._label_controller || new ParamsLabelController();\\n }\\n hasLabelController() {\\n return this._label_controller != null;\\n }\\n dispose() {\\n if (this._params_node) {\\n this._params_node.dispose();\\n }\\n for (let param of this.all) {\\n param.dispose();\\n }\\n this._post_create_params_hook_names = void 0;\\n this._post_create_params_hooks = void 0;\\n this._on_scene_load_hooks = void 0;\\n this._on_scene_load_hook_names = void 0;\\n this._label_controller?.dispose();\\n }\\n initDependencyNode() {\\n if (!this._params_node) {\\n this._params_node = new CoreGraphNode(this.node.scene(), NODE_SIMPLE_NAME);\\n this.node.addGraphInput(this._params_node, false);\\n }\\n }\\n init() {\\n this.initDependencyNode();\\n this._param_create_mode = true;\\n this._initFromParamsConfig();\\n this.node.createParams();\\n this._postCreateParams();\\n }\\n _postCreateParams() {\\n this._updateCaches();\\n this._initParamAccessors();\\n this._param_create_mode = false;\\n this._params_created = true;\\n this._runPostCreateParamsHooks();\\n }\\n postCreateSpareParams() {\\n this._updateCaches();\\n this._initParamAccessors();\\n this.node.scene().referencesController.notifyParamsUpdated(this.node);\\n this.node.emit(NodeEvent.PARAMS_UPDATED);\\n }\\n updateParams(options) {\\n let has_created_a_param = false;\\n let has_deleted_a_param = false;\\n if (options.namesToDelete) {\\n for (let param_name of options.namesToDelete) {\\n if (this.has(param_name)) {\\n this._deleteParam(param_name);\\n has_deleted_a_param = true;\\n }\\n }\\n }\\n if (options.toAdd) {\\n for (let param_data of options.toAdd) {\\n const param = this.addParam(param_data.type, param_data.name, param_data.init_value, param_data.options);\\n if (param) {\\n if (param_data.raw_input != null) {\\n param.set(param_data.raw_input);\\n }\\n has_created_a_param = true;\\n }\\n }\\n }\\n if (has_deleted_a_param || has_created_a_param) {\\n this.postCreateSpareParams();\\n }\\n }\\n _initFromParamsConfig() {\\n const paramsConfig = this.node.paramsConfig;\\n let init_values_used = false;\\n if (paramsConfig) {\\n for (let name of Object.keys(paramsConfig)) {\\n const config = paramsConfig[name];\\n let init_value;\\n if (this.node.params_init_value_overrides) {\\n init_value = this.node.params_init_value_overrides[name];\\n init_values_used = true;\\n }\\n this.addParam(config.type, name, config.init_value, config.options, init_value);\\n }\\n }\\n if (init_values_used) {\\n this.node.setDirty();\\n }\\n this.node.params_init_value_overrides = void 0;\\n }\\n _initParamAccessors() {\\n let current_names_in_accessor = Object.getOwnPropertyNames(this.node.pv);\\n this._removeUnneededAccessors(current_names_in_accessor);\\n current_names_in_accessor = Object.getOwnPropertyNames(this.node.pv);\\n for (let param of this.all) {\\n const is_spare = param.options.isSpare();\\n const param_not_yet_in_accessors = !current_names_in_accessor.includes(param.name());\\n if (param_not_yet_in_accessors || is_spare) {\\n Object.defineProperty(this.node.pv, param.name(), {\\n get: () => {\\n return param.value;\\n },\\n configurable: is_spare\\n });\\n Object.defineProperty(this.node.p, param.name(), {\\n get: () => {\\n return param;\\n },\\n configurable: is_spare\\n });\\n }\\n }\\n }\\n _removeUnneededAccessors(current_names_in_accessor) {\\n const current_param_names = this._param_names;\\n const names_to_remove = [];\\n for (let current_name_in_accessor of current_names_in_accessor) {\\n if (!current_param_names.includes(current_name_in_accessor)) {\\n names_to_remove.push(current_name_in_accessor);\\n }\\n }\\n for (let name_to_remove of names_to_remove) {\\n Object.defineProperty(this.node.pv, name_to_remove, {\\n get: () => {\\n return void 0;\\n },\\n configurable: true\\n });\\n Object.defineProperty(this.node.p, name_to_remove, {\\n get: () => {\\n return void 0;\\n },\\n configurable: true\\n });\\n }\\n }\\n get params_node() {\\n return this._params_node;\\n }\\n get all() {\\n return this._params_list;\\n }\\n get non_spare() {\\n return this._non_spare_params;\\n }\\n get spare() {\\n return this._spare_params;\\n }\\n get names() {\\n return this._param_names;\\n }\\n get non_spare_names() {\\n return this._non_spare_param_names;\\n }\\n get spare_names() {\\n return this._spare_param_names;\\n }\\n set_with_type(param_name, value, type) {\\n const param = this.param_with_type(param_name, type);\\n if (param) {\\n param.set(value);\\n } else {\\n Poly.warn(`param ${param_name} not found with type ${type}`);\\n }\\n }\\n set_float(param_name, value) {\\n this.set_with_type(param_name, value, ParamType.FLOAT);\\n }\\n set_vector3(param_name, value) {\\n this.set_with_type(param_name, value, ParamType.VECTOR3);\\n }\\n has_param(param_name) {\\n return this._params_by_name[param_name] != null;\\n }\\n has(param_name) {\\n return this.has_param(param_name);\\n }\\n get(param_name) {\\n return this.param(param_name);\\n }\\n param_with_type(param_name, type) {\\n const param = this.param(param_name);\\n if (param && param.type() == type) {\\n return param;\\n }\\n }\\n get_float(param_name) {\\n return this.param_with_type(param_name, ParamType.FLOAT);\\n }\\n get_operator_path(param_name) {\\n return this.param_with_type(param_name, ParamType.OPERATOR_PATH);\\n }\\n value(param_name) {\\n return this.param(param_name)?.value;\\n }\\n value_with_type(param_name, type) {\\n return this.param_with_type(param_name, type)?.value;\\n }\\n boolean(param_name) {\\n return this.value_with_type(param_name, ParamType.BOOLEAN);\\n }\\n float(param_name) {\\n return this.value_with_type(param_name, ParamType.FLOAT);\\n }\\n integer(param_name) {\\n return this.value_with_type(param_name, ParamType.INTEGER);\\n }\\n string(param_name) {\\n return this.value_with_type(param_name, ParamType.STRING);\\n }\\n vector2(param_name) {\\n return this.value_with_type(param_name, ParamType.VECTOR2);\\n }\\n vector3(param_name) {\\n return this.value_with_type(param_name, ParamType.VECTOR3);\\n }\\n color(param_name) {\\n return this.value_with_type(param_name, ParamType.COLOR);\\n }\\n param(param_name) {\\n const p = this._params_by_name[param_name];\\n if (p != null) {\\n return p;\\n } else {\\n Poly.warn(`tried to access param '${param_name}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`);\\n return null;\\n }\\n }\\n _deleteParam(param_name) {\\n const param = this._params_by_name[param_name];\\n if (param) {\\n if (this._params_node) {\\n this._params_node.removeGraphInput(this._params_by_name[param_name]);\\n }\\n param._setupNodeDependencies(null);\\n delete this._params_by_name[param_name];\\n if (param.isMultiple() && param.components) {\\n for (let component of param.components) {\\n const child_name = component.name();\\n delete this._params_by_name[child_name];\\n }\\n }\\n } else {\\n throw new Error(`param '${param_name}' does not exist on node ${this.node.path()}`);\\n }\\n }\\n addParam(type, param_name, default_value, options = {}, init_data) {\\n const is_spare = options[\\\\\\\"spare\\\\\\\"] || false;\\n if (this._param_create_mode === false && !is_spare) {\\n Poly.warn(`node ${this.node.path()} (${this.node.type()}) param '${param_name}' cannot be created outside of create_params`);\\n }\\n if (this.node.scene() == null) {\\n Poly.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);\\n }\\n const constructor = ParamConstructorByType[type];\\n if (constructor != null) {\\n const existing_param = this._params_by_name[param_name];\\n if (existing_param) {\\n if (is_spare) {\\n if (existing_param.type() != type) {\\n this._deleteParam(existing_param.name());\\n }\\n } else {\\n Poly.warn(`a param named ${param_name} already exists`, this.node);\\n }\\n }\\n const param = new constructor(this.node.scene(), this.node);\\n param.options.set(options);\\n param.setName(param_name);\\n param.setInitValue(default_value);\\n param.initComponents();\\n if (init_data == null) {\\n param.set(default_value);\\n } else {\\n if (param.options.isExpressionForEntities()) {\\n param.set(default_value);\\n }\\n if (init_data.raw_input != null) {\\n param.set(init_data.raw_input);\\n } else {\\n if (init_data.simple_data != null) {\\n param.set(init_data.simple_data);\\n } else {\\n if (init_data.complex_data != null) {\\n const raw_input = init_data.complex_data.raw_input;\\n if (raw_input) {\\n param.set(raw_input);\\n } else {\\n param.set(default_value);\\n }\\n const overriden_options = init_data.complex_data.overriden_options;\\n if (overriden_options != null) {\\n const keys = Object.keys(overriden_options);\\n for (let key of keys) {\\n param.options.setOption(key, overriden_options[key]);\\n }\\n }\\n }\\n }\\n }\\n }\\n param._setupNodeDependencies(this.node);\\n this._params_by_name[param.name()] = param;\\n if (param.isMultiple() && param.components) {\\n for (let component of param.components) {\\n this._params_by_name[component.name()] = component;\\n }\\n }\\n this._params_added_since_last_params_eval = true;\\n return param;\\n }\\n }\\n _updateCaches() {\\n this._params_list = Object.values(this._params_by_name);\\n this._param_names = Object.keys(this._params_by_name);\\n this._non_spare_params = Object.values(this._params_by_name).filter((p) => !p.options.isSpare());\\n this._spare_params = Object.values(this._params_by_name).filter((p) => p.options.isSpare());\\n this._non_spare_param_names = Object.values(this._params_by_name).filter((p) => !p.options.isSpare()).map((p) => p.name());\\n this._spare_param_names = Object.values(this._params_by_name).filter((p) => p.options.isSpare()).map((p) => p.name());\\n }\\n async _evalParam(param) {\\n if (param.isDirty()) {\\n await param.compute();\\n if (param.states.error.active()) {\\n this.node.states.error.set(`param '${param.name()}' error: ${param.states.error.message()}`);\\n }\\n } else {\\n }\\n }\\n async evalParams(params) {\\n const promises = [];\\n for (let param of params) {\\n if (param.isDirty()) {\\n promises.push(this._evalParam(param));\\n }\\n }\\n await Promise.all(promises);\\n if (this.node.states.error.active()) {\\n this.node._setContainer(null);\\n }\\n }\\n paramsEvalRequired() {\\n return this._params_node != null && (this._params_node.isDirty() || this._params_added_since_last_params_eval);\\n }\\n async evalAll() {\\n if (this.paramsEvalRequired()) {\\n await this.evalParams(this._params_list);\\n this._params_node?.removeDirtyState();\\n this._params_added_since_last_params_eval = false;\\n }\\n }\\n onParamsCreated(hook_name, hook) {\\n if (this._params_created) {\\n hook();\\n } else {\\n if (this._post_create_params_hook_names && this._post_create_params_hook_names.includes(hook_name)) {\\n Poly.error(`hook name ${hook_name} already exists`);\\n return;\\n }\\n this._post_create_params_hook_names = this._post_create_params_hook_names || [];\\n this._post_create_params_hook_names.push(hook_name);\\n this._post_create_params_hooks = this._post_create_params_hooks || [];\\n this._post_create_params_hooks.push(hook);\\n }\\n }\\n addOnSceneLoadHook(param_name, method) {\\n this._on_scene_load_hook_names = this._on_scene_load_hook_names || [];\\n this._on_scene_load_hooks = this._on_scene_load_hooks || [];\\n if (!this._on_scene_load_hook_names.includes(param_name)) {\\n this._on_scene_load_hook_names.push(param_name);\\n this._on_scene_load_hooks.push(method);\\n } else {\\n Poly.warn(`hook with name ${param_name} already exists`, this.node);\\n }\\n }\\n _runPostCreateParamsHooks() {\\n if (this._post_create_params_hooks) {\\n for (let hook of this._post_create_params_hooks) {\\n hook();\\n }\\n }\\n }\\n runOnSceneLoadHooks() {\\n if (this._on_scene_load_hooks) {\\n for (let hook of this._on_scene_load_hooks) {\\n hook();\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/params/ParamsValueAccessor.ts\\nvar ParamsValueAccessor = class {\\n constructor() {\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/NodeConnection.ts\\nvar TypedNodeConnection2 = class {\\n constructor(_node_src, _node_dest, _output_index = 0, _input_index = 0) {\\n this._node_src = _node_src;\\n this._node_dest = _node_dest;\\n this._output_index = _output_index;\\n this._input_index = _input_index;\\n if (this._output_index == null) {\\n throw \\\\\\\"bad output index\\\\\\\";\\n }\\n if (this._input_index == null) {\\n throw \\\\\\\"bad input index\\\\\\\";\\n }\\n this._id = TypedNodeConnection2._next_id++;\\n if (this._node_src.io.connections && this._node_dest.io.connections) {\\n this._node_src.io.connections.addOutputConnection(this);\\n this._node_dest.io.connections.addInputConnection(this);\\n }\\n }\\n get id() {\\n return this._id;\\n }\\n get node_src() {\\n return this._node_src;\\n }\\n get node_dest() {\\n return this._node_dest;\\n }\\n get output_index() {\\n return this._output_index;\\n }\\n get input_index() {\\n return this._input_index;\\n }\\n src_connection_point() {\\n const node_src = this._node_src;\\n const output_index = this._output_index;\\n return node_src.io.outputs.namedOutputConnectionPoints()[output_index];\\n }\\n dest_connection_point() {\\n const node_dest = this._node_dest;\\n const input_index = this._input_index;\\n return node_dest.io.inputs.namedInputConnectionPoints()[input_index];\\n }\\n disconnect(options = {}) {\\n if (this._node_src.io.connections && this._node_dest.io.connections) {\\n this._node_src.io.connections.removeOutputConnection(this);\\n this._node_dest.io.connections.removeInputConnection(this);\\n }\\n if (options.setInput === true) {\\n this._node_dest.io.inputs.setInput(this._input_index, null);\\n }\\n }\\n};\\nvar TypedNodeConnection = TypedNodeConnection2;\\nTypedNodeConnection._next_id = 0;\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/utils/ClonedStatesController.ts\\nvar ClonedStatesController2 = class {\\n constructor(inputs_controller) {\\n this.inputs_controller = inputs_controller;\\n this._clone_required_states = [];\\n this._overridden = false;\\n this.node = inputs_controller.node;\\n }\\n initInputsClonedState(states) {\\n if (CoreType.isArray(states)) {\\n this._cloned_states = states;\\n } else {\\n this._cloned_state = states;\\n }\\n this._update_clone_required_state();\\n }\\n overrideClonedStateAllowed() {\\n if (this._cloned_states) {\\n for (let state of this._cloned_states) {\\n if (state == InputCloneMode.FROM_NODE) {\\n return true;\\n }\\n }\\n }\\n if (this._cloned_state) {\\n return this._cloned_state == InputCloneMode.FROM_NODE;\\n }\\n return false;\\n }\\n cloneRequiredState(index) {\\n return this._clone_required_states[index];\\n }\\n cloneRequiredStates() {\\n return this._clone_required_states;\\n }\\n _get_clone_required_state(index) {\\n const states = this._cloned_states;\\n if (states) {\\n const state = states[index];\\n if (state != null) {\\n return this.clone_required_from_state(state);\\n }\\n }\\n if (this._cloned_state) {\\n return this.clone_required_from_state(this._cloned_state);\\n }\\n return true;\\n }\\n clone_required_from_state(state) {\\n switch (state) {\\n case InputCloneMode.ALWAYS:\\n return true;\\n case InputCloneMode.NEVER:\\n return false;\\n case InputCloneMode.FROM_NODE:\\n return !this._overridden;\\n }\\n return TypeAssert.unreachable(state);\\n }\\n overrideClonedState(state) {\\n this._overridden = state;\\n this._update_clone_required_state();\\n this.node.emit(NodeEvent.OVERRIDE_CLONABLE_STATE_UPDATE);\\n this.node.setDirty();\\n }\\n overriden() {\\n return this._overridden;\\n }\\n _update_clone_required_state() {\\n if (this._cloned_states) {\\n const states = [];\\n for (let i = 0; i < this._cloned_states.length; i++) {\\n states[i] = this._get_clone_required_state(i);\\n }\\n this._clone_required_states = states;\\n return;\\n }\\n if (this._cloned_state) {\\n const max_inputs = this.inputs_controller.maxInputsCount();\\n const states = [];\\n for (let i = 0; i < max_inputs; i++) {\\n states[i] = this._get_clone_required_state(i);\\n }\\n this._clone_required_states = states;\\n return;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/InputsController.ts\\nvar MAX_INPUTS_COUNT_UNSET = 0;\\nvar InputsController2 = class {\\n constructor(node) {\\n this.node = node;\\n this._graph_node_inputs = [];\\n this._inputs = [];\\n this._has_named_inputs = false;\\n this._minInputsCount = 0;\\n this._maxInputsCount = MAX_INPUTS_COUNT_UNSET;\\n this._maxInputsCountOnInput = MAX_INPUTS_COUNT_UNSET;\\n this._depends_on_inputs = true;\\n }\\n dispose() {\\n if (this._graph_node) {\\n this._graph_node.dispose();\\n }\\n for (let graph_node of this._graph_node_inputs) {\\n if (graph_node) {\\n graph_node.dispose();\\n }\\n }\\n this._on_update_hooks = void 0;\\n this._on_update_hook_names = void 0;\\n }\\n set_depends_on_inputs(depends_on_inputs) {\\n this._depends_on_inputs = depends_on_inputs;\\n }\\n setMinCount(minInputsCount) {\\n this._minInputsCount = minInputsCount;\\n }\\n minCount() {\\n return this._minInputsCount;\\n }\\n setMaxCount(maxInputsCount) {\\n if (this._maxInputsCount == MAX_INPUTS_COUNT_UNSET) {\\n this._maxInputsCountOnInput = maxInputsCount;\\n }\\n this._maxInputsCount = maxInputsCount;\\n this._initGraphNodeInputs();\\n }\\n namedInputConnectionPointsByName(name) {\\n if (this._named_input_connection_points) {\\n for (let connection_point of this._named_input_connection_points) {\\n if (connection_point && connection_point.name() == name) {\\n return connection_point;\\n }\\n }\\n }\\n }\\n setNamedInputConnectionPoints(connection_points) {\\n this._has_named_inputs = true;\\n const connections = this.node.io.connections.inputConnections();\\n if (connections) {\\n for (let connection of connections) {\\n if (connection) {\\n if (connection.input_index >= connection_points.length) {\\n connection.disconnect({setInput: true});\\n }\\n }\\n }\\n }\\n this._named_input_connection_points = connection_points;\\n this.setMinCount(0);\\n this.setMaxCount(connection_points.length);\\n this._initGraphNodeInputs();\\n this.node.emit(NodeEvent.NAMED_INPUTS_UPDATED);\\n }\\n hasNamedInputs() {\\n return this._has_named_inputs;\\n }\\n namedInputConnectionPoints() {\\n return this._named_input_connection_points || [];\\n }\\n _initGraphNodeInputs() {\\n for (let i = 0; i < this._maxInputsCount; i++) {\\n this._graph_node_inputs[i] = this._graph_node_inputs[i] || this._createGraphNodeInput(i);\\n }\\n }\\n _createGraphNodeInput(index) {\\n const graph_input_node = new CoreGraphNode(this.node.scene(), `input_${index}`);\\n if (!this._graph_node) {\\n this._graph_node = new CoreGraphNode(this.node.scene(), \\\\\\\"inputs\\\\\\\");\\n this.node.addGraphInput(this._graph_node, false);\\n }\\n this._graph_node.addGraphInput(graph_input_node, false);\\n return graph_input_node;\\n }\\n maxInputsCount() {\\n return this._maxInputsCount || 0;\\n }\\n maxInputsCountOverriden() {\\n return this._maxInputsCount != this._maxInputsCountOnInput;\\n }\\n inputGraphNode(input_index) {\\n return this._graph_node_inputs[input_index];\\n }\\n setCount(min, max) {\\n if (max == null) {\\n max = min;\\n }\\n this.setMinCount(min);\\n this.setMaxCount(max);\\n this._initConnectionControllerInputs();\\n }\\n _initConnectionControllerInputs() {\\n this.node.io.connections.initInputs();\\n }\\n is_any_input_dirty() {\\n return this._graph_node?.isDirty() || false;\\n }\\n async containers_without_evaluation() {\\n const containers = [];\\n for (let i = 0; i < this._inputs.length; i++) {\\n const input_node = this._inputs[i];\\n let container = void 0;\\n if (input_node) {\\n container = await input_node.compute();\\n }\\n containers.push(container);\\n }\\n return containers;\\n }\\n existing_input_indices() {\\n const existing_input_indices = [];\\n if (this._maxInputsCount > 0) {\\n for (let i = 0; i < this._inputs.length; i++) {\\n if (this._inputs[i]) {\\n existing_input_indices.push(i);\\n }\\n }\\n }\\n return existing_input_indices;\\n }\\n async eval_required_inputs() {\\n let containers = [];\\n if (this._maxInputsCount > 0) {\\n const existing_input_indices = this.existing_input_indices();\\n if (existing_input_indices.length < this._minInputsCount) {\\n this.node.states.error.set(\\\\\\\"inputs are missing\\\\\\\");\\n } else {\\n if (existing_input_indices.length > 0) {\\n const promises = [];\\n let input;\\n for (let i = 0; i < this._inputs.length; i++) {\\n input = this._inputs[i];\\n if (input) {\\n promises.push(this.eval_required_input(i));\\n }\\n }\\n containers = await Promise.all(promises);\\n this._graph_node?.removeDirtyState();\\n }\\n }\\n }\\n return containers;\\n }\\n async eval_required_input(input_index) {\\n let container = void 0;\\n const input_node = this.input(input_index);\\n if (input_node) {\\n container = await input_node.compute();\\n this._graph_node_inputs[input_index].removeDirtyState();\\n }\\n if (container && container.coreContent()) {\\n } else {\\n const input_node2 = this.input(input_index);\\n if (input_node2) {\\n const input_error_message = input_node2.states.error.message();\\n if (input_error_message) {\\n this.node.states.error.set(`input ${input_index} is invalid (error: ${input_error_message})`);\\n }\\n }\\n }\\n return container;\\n }\\n get_named_input_index(name) {\\n if (this._named_input_connection_points) {\\n for (let i = 0; i < this._named_input_connection_points.length; i++) {\\n if (this._named_input_connection_points[i]?.name() == name) {\\n return i;\\n }\\n }\\n }\\n return -1;\\n }\\n get_input_index(input_index_or_name) {\\n if (CoreType.isString(input_index_or_name)) {\\n if (this.hasNamedInputs()) {\\n return this.get_named_input_index(input_index_or_name);\\n } else {\\n throw new Error(`node ${this.node.path()} has no named inputs`);\\n }\\n } else {\\n return input_index_or_name;\\n }\\n }\\n setInput(input_index_or_name, node, output_index_or_name = 0) {\\n const input_index = this.get_input_index(input_index_or_name) || 0;\\n if (input_index < 0) {\\n const message = `invalid input (${input_index_or_name}) for node ${this.node.path()}`;\\n console.warn(message);\\n throw new Error(message);\\n }\\n let output_index = 0;\\n if (node) {\\n if (node.io.outputs.hasNamedOutputs()) {\\n output_index = node.io.outputs.getOutputIndex(output_index_or_name);\\n if (output_index == null || output_index < 0) {\\n const connection_points = node.io.outputs.namedOutputConnectionPoints();\\n const names = connection_points.map((cp) => cp.name());\\n console.warn(`node ${node.path()} does not have an output named ${output_index_or_name}. inputs are: ${names.join(\\\\\\\", \\\\\\\")}`);\\n return;\\n }\\n }\\n }\\n const graph_input_node = this._graph_node_inputs[input_index];\\n if (graph_input_node == null) {\\n const message = `graph_input_node not found at index ${input_index}`;\\n console.warn(message);\\n throw new Error(message);\\n }\\n if (node && this.node.parent() != node.parent()) {\\n return;\\n }\\n const old_input_node = this._inputs[input_index];\\n let old_output_index = null;\\n let old_connection = void 0;\\n if (this.node.io.connections) {\\n old_connection = this.node.io.connections.inputConnection(input_index);\\n }\\n if (old_connection) {\\n old_output_index = old_connection.output_index;\\n }\\n if (node !== old_input_node || output_index != old_output_index) {\\n if (old_input_node != null) {\\n if (this._depends_on_inputs) {\\n graph_input_node.removeGraphInput(old_input_node);\\n }\\n }\\n if (node != null) {\\n if (graph_input_node.addGraphInput(node)) {\\n if (!this._depends_on_inputs) {\\n graph_input_node.removeGraphInput(node);\\n }\\n if (old_connection) {\\n old_connection.disconnect({setInput: false});\\n }\\n this._inputs[input_index] = node;\\n new TypedNodeConnection(node, this.node, output_index, input_index);\\n } else {\\n console.warn(`cannot connect ${node.path()} to ${this.node.path()}`);\\n }\\n } else {\\n this._inputs[input_index] = null;\\n if (old_connection) {\\n old_connection.disconnect({setInput: false});\\n }\\n }\\n this._run_on_set_input_hooks();\\n graph_input_node.setSuccessorsDirty();\\n this.node.emit(NodeEvent.INPUTS_UPDATED);\\n }\\n }\\n remove_input(node) {\\n const inputs = this.inputs();\\n let input;\\n for (let i = 0; i < inputs.length; i++) {\\n input = inputs[i];\\n if (input != null && node != null) {\\n if (input.graphNodeId() === node.graphNodeId()) {\\n this.setInput(i, null);\\n }\\n }\\n }\\n }\\n input(input_index) {\\n return this._inputs[input_index];\\n }\\n named_input(input_name) {\\n if (this.hasNamedInputs()) {\\n const input_index = this.get_input_index(input_name);\\n return this._inputs[input_index];\\n } else {\\n return null;\\n }\\n }\\n named_input_connection_point(input_name) {\\n if (this.hasNamedInputs() && this._named_input_connection_points) {\\n const input_index = this.get_input_index(input_name);\\n return this._named_input_connection_points[input_index];\\n }\\n }\\n has_named_input(name) {\\n return this.get_named_input_index(name) >= 0;\\n }\\n has_input(input_index) {\\n return this._inputs[input_index] != null;\\n }\\n inputs() {\\n return this._inputs;\\n }\\n initInputsClonedState(states) {\\n if (!this._cloned_states_controller) {\\n this._cloned_states_controller = new ClonedStatesController2(this);\\n this._cloned_states_controller.initInputsClonedState(states);\\n }\\n }\\n overrideClonedStateAllowed() {\\n return this._cloned_states_controller?.overrideClonedStateAllowed() || false;\\n }\\n overrideClonedState(state) {\\n this._cloned_states_controller?.overrideClonedState(state);\\n }\\n clonedStateOverriden() {\\n return this._cloned_states_controller?.overriden() || false;\\n }\\n cloneRequired(index) {\\n const state = this._cloned_states_controller?.cloneRequiredState(index);\\n if (state != null) {\\n return state;\\n }\\n return true;\\n }\\n cloneRequiredStates() {\\n const states = this._cloned_states_controller?.cloneRequiredStates();\\n if (states != null) {\\n return states;\\n }\\n return true;\\n }\\n add_on_set_input_hook(name, hook) {\\n this._on_update_hooks = this._on_update_hooks || [];\\n this._on_update_hook_names = this._on_update_hook_names || [];\\n if (!this._on_update_hook_names.includes(name)) {\\n this._on_update_hooks.push(hook);\\n this._on_update_hook_names.push(name);\\n } else {\\n console.warn(`hook with name ${name} already exists`, this.node);\\n }\\n }\\n _run_on_set_input_hooks() {\\n if (this._on_update_hooks) {\\n for (let hook of this._on_update_hooks) {\\n hook();\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/OutputsController.ts\\nvar OutputsController = class {\\n constructor(node) {\\n this.node = node;\\n this._has_outputs = false;\\n this._has_named_outputs = false;\\n }\\n setHasOneOutput() {\\n this._has_outputs = true;\\n }\\n setHasNoOutput() {\\n this._has_outputs = false;\\n }\\n hasOutputs() {\\n return this._has_outputs;\\n }\\n hasNamedOutputs() {\\n return this._has_named_outputs;\\n }\\n hasNamedOutput(name) {\\n return this.getNamedOutputIndex(name) >= 0;\\n }\\n namedOutputConnectionPoints() {\\n return this._named_output_connection_points || [];\\n }\\n namedOutputConnection(index) {\\n if (this._named_output_connection_points) {\\n return this._named_output_connection_points[index];\\n }\\n }\\n getNamedOutputIndex(name) {\\n if (this._named_output_connection_points) {\\n for (let i = 0; i < this._named_output_connection_points.length; i++) {\\n if (this._named_output_connection_points[i]?.name() == name) {\\n return i;\\n }\\n }\\n }\\n return -1;\\n }\\n getOutputIndex(output_index_or_name) {\\n if (output_index_or_name != null) {\\n if (CoreType.isString(output_index_or_name)) {\\n if (this.hasNamedOutputs()) {\\n return this.getNamedOutputIndex(output_index_or_name);\\n } else {\\n console.warn(`node ${this.node.path()} has no named outputs`);\\n return -1;\\n }\\n } else {\\n return output_index_or_name;\\n }\\n }\\n return -1;\\n }\\n namedOutputConnectionPointsByName(name) {\\n if (this._named_output_connection_points) {\\n for (let connection_point of this._named_output_connection_points) {\\n if (connection_point?.name() == name) {\\n return connection_point;\\n }\\n }\\n }\\n }\\n setNamedOutputConnectionPoints(connection_points, set_dirty = true) {\\n this._has_named_outputs = true;\\n const connections = this.node.io.connections.outputConnections();\\n if (connections) {\\n for (let connection of connections) {\\n if (connection) {\\n if (connection.output_index >= connection_points.length) {\\n connection.disconnect({setInput: true});\\n }\\n }\\n }\\n }\\n this._named_output_connection_points = connection_points;\\n if (set_dirty && this.node.scene()) {\\n this.node.setDirty(this.node);\\n }\\n this.node.emit(NodeEvent.NAMED_OUTPUTS_UPDATED);\\n }\\n used_output_names() {\\n const connections_controller = this.node.io.connections;\\n if (connections_controller) {\\n const output_connections = connections_controller.outputConnections();\\n let output_indices = output_connections.map((connection) => connection ? connection.output_index : null);\\n output_indices = ArrayUtils.uniq(output_indices);\\n const used_output_indices = [];\\n output_indices.forEach((index) => {\\n if (CoreType.isNumber(index)) {\\n used_output_indices.push(index);\\n }\\n });\\n const used_output_names = [];\\n for (let index of used_output_indices) {\\n const name = this.namedOutputConnectionPoints()[index]?.name();\\n if (name) {\\n used_output_names.push(name);\\n }\\n }\\n return used_output_names;\\n } else {\\n return [];\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/ConnectionsController.ts\\nvar ConnectionsController = class {\\n constructor(_node) {\\n this._node = _node;\\n this._output_connections = new Map();\\n }\\n initInputs() {\\n const count = this._node.io.inputs.maxInputsCount();\\n this._input_connections = this._input_connections || new Array(count);\\n while (this._input_connections.length < count) {\\n this._input_connections.push(void 0);\\n }\\n }\\n addInputConnection(connection) {\\n if (this._input_connections) {\\n this._input_connections[connection.input_index] = connection;\\n } else {\\n console.warn(`input connections array not initialized`);\\n }\\n }\\n removeInputConnection(connection) {\\n if (this._input_connections) {\\n if (connection.input_index < this._input_connections.length) {\\n this._input_connections[connection.input_index] = void 0;\\n let all_connections_after_are_undefined = true;\\n for (let i = connection.input_index; i < this._input_connections.length; i++) {\\n if (this._input_connections[i]) {\\n all_connections_after_are_undefined = false;\\n }\\n }\\n if (all_connections_after_are_undefined) {\\n this._input_connections = this._input_connections.slice(0, connection.input_index);\\n }\\n } else {\\n console.warn(`attempt to remove an input connection at index ${connection.input_index}`);\\n }\\n } else {\\n console.warn(`input connections array not initialized`);\\n }\\n }\\n inputConnection(index) {\\n if (this._input_connections) {\\n return this._input_connections[index];\\n }\\n }\\n firstInputConnection() {\\n if (this._input_connections) {\\n return ArrayUtils.compact(this._input_connections)[0];\\n } else {\\n return null;\\n }\\n }\\n inputConnections() {\\n return this._input_connections;\\n }\\n existingInputConnections() {\\n const current_connections = this._input_connections;\\n if (current_connections) {\\n while (current_connections.length > 1 && current_connections[current_connections.length - 1] === void 0) {\\n current_connections.pop();\\n }\\n }\\n return current_connections;\\n }\\n addOutputConnection(connection) {\\n const output_index = connection.output_index;\\n const id = connection.id;\\n let connections_by_id = this._output_connections.get(output_index);\\n if (!connections_by_id) {\\n connections_by_id = new Map();\\n this._output_connections.set(output_index, connections_by_id);\\n }\\n connections_by_id.set(id, connection);\\n }\\n removeOutputConnection(connection) {\\n const output_index = connection.output_index;\\n const id = connection.id;\\n let connections_by_id = this._output_connections.get(output_index);\\n if (connections_by_id) {\\n connections_by_id.delete(id);\\n }\\n }\\n outputConnections() {\\n let list = [];\\n this._output_connections.forEach((connections_by_id, output_index) => {\\n connections_by_id.forEach((connection, id) => {\\n if (connection) {\\n list.push(connection);\\n }\\n });\\n });\\n return list;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/SavedConnectionPointsDataController.ts\\nvar SavedConnectionPointsDataController = class {\\n constructor(_node) {\\n this._node = _node;\\n }\\n set_in(data) {\\n this._in = data;\\n }\\n set_out(data) {\\n this._out = data;\\n }\\n clear() {\\n this._in = void 0;\\n this._out = void 0;\\n }\\n in() {\\n return this._in;\\n }\\n out() {\\n return this._out;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/connections/_Base.ts\\nvar BaseConnectionPoint = class {\\n constructor(_name, _type, _init_value) {\\n this._name = _name;\\n this._type = _type;\\n this._init_value = _init_value;\\n }\\n get init_value() {\\n return this._init_value;\\n }\\n name() {\\n return this._name;\\n }\\n type() {\\n return this._type;\\n }\\n are_types_matched(src_type, dest_type) {\\n return true;\\n }\\n toJSON() {\\n return this._json = this._json || this._create_json();\\n }\\n _create_json() {\\n return {\\n name: this._name,\\n type: this._type\\n };\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/connections/Gl.ts\\nvar GlConnectionPointType;\\n(function(GlConnectionPointType2) {\\n GlConnectionPointType2[\\\\\\\"BOOL\\\\\\\"] = \\\\\\\"bool\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"INT\\\\\\\"] = \\\\\\\"int\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"FLOAT\\\\\\\"] = \\\\\\\"float\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"VEC2\\\\\\\"] = \\\\\\\"vec2\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"VEC3\\\\\\\"] = \\\\\\\"vec3\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"VEC4\\\\\\\"] = \\\\\\\"vec4\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"SAMPLER_2D\\\\\\\"] = \\\\\\\"sampler2D\\\\\\\";\\n GlConnectionPointType2[\\\\\\\"SSS_MODEL\\\\\\\"] = \\\\\\\"SSSModel\\\\\\\";\\n})(GlConnectionPointType || (GlConnectionPointType = {}));\\nvar GL_CONNECTION_POINT_TYPES = [\\n GlConnectionPointType.BOOL,\\n GlConnectionPointType.INT,\\n GlConnectionPointType.FLOAT,\\n GlConnectionPointType.VEC2,\\n GlConnectionPointType.VEC3,\\n GlConnectionPointType.VEC4\\n];\\nvar GlConnectionPointTypeToParamTypeMap = {\\n [GlConnectionPointType.BOOL]: ParamType.BOOLEAN,\\n [GlConnectionPointType.INT]: ParamType.INTEGER,\\n [GlConnectionPointType.FLOAT]: ParamType.FLOAT,\\n [GlConnectionPointType.VEC2]: ParamType.VECTOR2,\\n [GlConnectionPointType.VEC3]: ParamType.VECTOR3,\\n [GlConnectionPointType.VEC4]: ParamType.VECTOR4,\\n [GlConnectionPointType.SAMPLER_2D]: ParamType.RAMP,\\n [GlConnectionPointType.SSS_MODEL]: ParamType.STRING\\n};\\nvar GLParamTypeToConnectionPointTypeMap = {\\n [ParamType.BOOLEAN]: GlConnectionPointType.BOOL,\\n [ParamType.COLOR]: GlConnectionPointType.VEC3,\\n [ParamType.INTEGER]: GlConnectionPointType.INT,\\n [ParamType.FLOAT]: GlConnectionPointType.FLOAT,\\n [ParamType.FOLDER]: void 0,\\n [ParamType.VECTOR2]: GlConnectionPointType.VEC2,\\n [ParamType.VECTOR3]: GlConnectionPointType.VEC3,\\n [ParamType.VECTOR4]: GlConnectionPointType.VEC4,\\n [ParamType.BUTTON]: void 0,\\n [ParamType.OPERATOR_PATH]: void 0,\\n [ParamType.PARAM_PATH]: void 0,\\n [ParamType.NODE_PATH]: void 0,\\n [ParamType.RAMP]: void 0,\\n [ParamType.STRING]: void 0\\n};\\nvar GlConnectionPointInitValueMap = {\\n [GlConnectionPointType.BOOL]: false,\\n [GlConnectionPointType.INT]: 0,\\n [GlConnectionPointType.FLOAT]: 0,\\n [GlConnectionPointType.VEC2]: [0, 0],\\n [GlConnectionPointType.VEC3]: [0, 0, 0],\\n [GlConnectionPointType.VEC4]: [0, 0, 0, 0],\\n [GlConnectionPointType.SAMPLER_2D]: RampParam.DEFAULT_VALUE_JSON,\\n [GlConnectionPointType.SSS_MODEL]: \\\\\\\"SSSModel()\\\\\\\"\\n};\\nvar GlConnectionPointComponentsCountMap = {\\n [GlConnectionPointType.BOOL]: 1,\\n [GlConnectionPointType.INT]: 1,\\n [GlConnectionPointType.FLOAT]: 1,\\n [GlConnectionPointType.VEC2]: 2,\\n [GlConnectionPointType.VEC3]: 3,\\n [GlConnectionPointType.VEC4]: 4,\\n [GlConnectionPointType.SAMPLER_2D]: 1,\\n [GlConnectionPointType.SSS_MODEL]: 1\\n};\\nvar GlConnectionPoint = class extends BaseConnectionPoint {\\n constructor(_name, _type, _init_value) {\\n super(_name, _type);\\n this._name = _name;\\n this._type = _type;\\n this._init_value = _init_value;\\n this._init_value = this._init_value || GlConnectionPointInitValueMap[this._type];\\n }\\n type() {\\n return this._type;\\n }\\n are_types_matched(src_type, dest_type) {\\n return src_type == dest_type;\\n }\\n get param_type() {\\n return GlConnectionPointTypeToParamTypeMap[this._type];\\n }\\n get init_value() {\\n return this._init_value;\\n }\\n toJSON() {\\n return this._json = this._json || this._create_json();\\n }\\n _create_json() {\\n return {\\n name: this._name,\\n type: this._type\\n };\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/connections/Js.ts\\nvar JsConnectionPointType;\\n(function(JsConnectionPointType2) {\\n JsConnectionPointType2[\\\\\\\"BOOL\\\\\\\"] = \\\\\\\"bool\\\\\\\";\\n JsConnectionPointType2[\\\\\\\"INT\\\\\\\"] = \\\\\\\"int\\\\\\\";\\n JsConnectionPointType2[\\\\\\\"FLOAT\\\\\\\"] = \\\\\\\"float\\\\\\\";\\n JsConnectionPointType2[\\\\\\\"VEC2\\\\\\\"] = \\\\\\\"vec2\\\\\\\";\\n JsConnectionPointType2[\\\\\\\"VEC3\\\\\\\"] = \\\\\\\"vec3\\\\\\\";\\n JsConnectionPointType2[\\\\\\\"VEC4\\\\\\\"] = \\\\\\\"vec4\\\\\\\";\\n})(JsConnectionPointType || (JsConnectionPointType = {}));\\nvar JS_CONNECTION_POINT_TYPES = [\\n JsConnectionPointType.BOOL,\\n JsConnectionPointType.INT,\\n JsConnectionPointType.FLOAT,\\n JsConnectionPointType.VEC2,\\n JsConnectionPointType.VEC3,\\n JsConnectionPointType.VEC4\\n];\\nvar JsConnectionPointTypeToParamTypeMap = {\\n [JsConnectionPointType.BOOL]: ParamType.BOOLEAN,\\n [JsConnectionPointType.INT]: ParamType.INTEGER,\\n [JsConnectionPointType.FLOAT]: ParamType.FLOAT,\\n [JsConnectionPointType.VEC2]: ParamType.VECTOR2,\\n [JsConnectionPointType.VEC3]: ParamType.VECTOR3,\\n [JsConnectionPointType.VEC4]: ParamType.VECTOR4\\n};\\nvar JsParamTypeToConnectionPointTypeMap = {\\n [ParamType.BOOLEAN]: JsConnectionPointType.BOOL,\\n [ParamType.COLOR]: JsConnectionPointType.VEC3,\\n [ParamType.INTEGER]: JsConnectionPointType.INT,\\n [ParamType.FLOAT]: JsConnectionPointType.FLOAT,\\n [ParamType.FOLDER]: void 0,\\n [ParamType.VECTOR2]: JsConnectionPointType.VEC2,\\n [ParamType.VECTOR3]: JsConnectionPointType.VEC3,\\n [ParamType.VECTOR4]: JsConnectionPointType.VEC4,\\n [ParamType.BUTTON]: void 0,\\n [ParamType.OPERATOR_PATH]: void 0,\\n [ParamType.PARAM_PATH]: void 0,\\n [ParamType.NODE_PATH]: void 0,\\n [ParamType.RAMP]: void 0,\\n [ParamType.STRING]: void 0\\n};\\nvar JsConnectionPointInitValueMap = {\\n [JsConnectionPointType.BOOL]: false,\\n [JsConnectionPointType.INT]: 0,\\n [JsConnectionPointType.FLOAT]: 0,\\n [JsConnectionPointType.VEC2]: [0, 0],\\n [JsConnectionPointType.VEC3]: [0, 0, 0],\\n [JsConnectionPointType.VEC4]: [0, 0, 0, 0]\\n};\\nvar GlConnectionPointComponentsCountMap2 = {\\n [JsConnectionPointType.BOOL]: 1,\\n [JsConnectionPointType.INT]: 1,\\n [JsConnectionPointType.FLOAT]: 1,\\n [JsConnectionPointType.VEC2]: 2,\\n [JsConnectionPointType.VEC3]: 3,\\n [JsConnectionPointType.VEC4]: 4\\n};\\nvar JsConnectionPoint = class extends BaseConnectionPoint {\\n constructor(_name, _type) {\\n super(_name, _type);\\n this._name = _name;\\n this._type = _type;\\n this._init_value = JsConnectionPointInitValueMap[this._type];\\n }\\n type() {\\n return this._type;\\n }\\n are_types_matched(src_type, dest_type) {\\n return src_type == dest_type;\\n }\\n get param_type() {\\n return JsConnectionPointTypeToParamTypeMap[this._type];\\n }\\n get init_value() {\\n return this._init_value;\\n }\\n toJSON() {\\n return this._json = this._json || this._create_json();\\n }\\n _create_json() {\\n return {\\n name: this._name,\\n type: this._type\\n };\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/connections/Event.ts\\nvar EventConnectionPointType;\\n(function(EventConnectionPointType2) {\\n EventConnectionPointType2[\\\\\\\"BASE\\\\\\\"] = \\\\\\\"base\\\\\\\";\\n EventConnectionPointType2[\\\\\\\"DRAG\\\\\\\"] = \\\\\\\"drag\\\\\\\";\\n EventConnectionPointType2[\\\\\\\"KEYBOARD\\\\\\\"] = \\\\\\\"keyboard\\\\\\\";\\n EventConnectionPointType2[\\\\\\\"MOUSE\\\\\\\"] = \\\\\\\"mouse\\\\\\\";\\n EventConnectionPointType2[\\\\\\\"POINTER\\\\\\\"] = \\\\\\\"pointer\\\\\\\";\\n})(EventConnectionPointType || (EventConnectionPointType = {}));\\nvar EventConnectionPoint = class extends BaseConnectionPoint {\\n constructor(_name, _type, _event_listener) {\\n super(_name, _type);\\n this._name = _name;\\n this._type = _type;\\n this._event_listener = _event_listener;\\n }\\n type() {\\n return this._type;\\n }\\n get param_type() {\\n return ParamType.FLOAT;\\n }\\n are_types_matched(src_type, dest_type) {\\n if (dest_type == EventConnectionPointType.BASE) {\\n return true;\\n } else {\\n return src_type == dest_type;\\n }\\n }\\n get event_listener() {\\n return this._event_listener;\\n }\\n toJSON() {\\n return this._json = this._json || this._create_json();\\n }\\n _create_json() {\\n return {\\n name: this._name,\\n type: this._type\\n };\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/connections/ConnectionMap.ts\\nvar DEFAULT_CONNECTION_POINT_ENUM_MAP = {\\n [NodeContext.ANIM]: void 0,\\n [NodeContext.COP]: void 0,\\n [NodeContext.EVENT]: EventConnectionPointType.BASE,\\n [NodeContext.GL]: GlConnectionPointType.FLOAT,\\n [NodeContext.JS]: JsConnectionPointType.FLOAT,\\n [NodeContext.MANAGER]: void 0,\\n [NodeContext.MAT]: void 0,\\n [NodeContext.OBJ]: void 0,\\n [NodeContext.POST]: void 0,\\n [NodeContext.ROP]: void 0,\\n [NodeContext.SOP]: void 0\\n};\\nfunction create_connection_point(context, name, type) {\\n switch (context) {\\n case NodeContext.EVENT: {\\n return new EventConnectionPoint(name, type);\\n }\\n case NodeContext.GL: {\\n return new GlConnectionPoint(name, type);\\n }\\n case NodeContext.JS: {\\n return new JsConnectionPoint(name, type);\\n }\\n default: {\\n return void 0;\\n }\\n }\\n}\\nfunction param_type_to_connection_point_type_map(context) {\\n switch (context) {\\n case NodeContext.EVENT: {\\n return void 0;\\n }\\n case NodeContext.GL: {\\n return GLParamTypeToConnectionPointTypeMap;\\n }\\n case NodeContext.JS: {\\n return JsParamTypeToConnectionPointTypeMap;\\n }\\n default: {\\n return void 0;\\n }\\n }\\n}\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/ConnectionPointsSpareParamsController.ts\\nvar ConnectionPointsSpareParamsController = class {\\n constructor(node, _context2) {\\n this.node = node;\\n this._context = _context2;\\n this._raw_input_serialized_by_param_name = new Map();\\n this._default_value_serialized_by_param_name = new Map();\\n this._initialized = false;\\n }\\n initializeNode() {\\n if (this._initialized) {\\n console.warn(\\\\\\\"already initialized\\\\\\\", this.node);\\n return;\\n }\\n this._initialized = true;\\n this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\", this.create_inputs_from_params.bind(this));\\n }\\n initialized() {\\n return this._initialized;\\n }\\n create_inputs_from_params() {\\n const connection_type_map = param_type_to_connection_point_type_map(this._context);\\n if (!connection_type_map) {\\n return;\\n }\\n const connection_points = [];\\n for (let param_name of this.node.params.names) {\\n let add_input = true;\\n if (this._inputless_param_names && this._inputless_param_names.length > 0 && this._inputless_param_names.includes(param_name)) {\\n add_input = false;\\n }\\n if (add_input) {\\n if (this.node.params.has(param_name)) {\\n const param = this.node.params.get(param_name);\\n if (param && !param.parent_param) {\\n const connection_type = connection_type_map[param.type()];\\n if (connection_type) {\\n const connection_point = create_connection_point(this._context, param.name(), connection_type);\\n if (connection_point) {\\n connection_points.push(connection_point);\\n }\\n }\\n }\\n }\\n }\\n }\\n this.node.io.inputs.setNamedInputConnectionPoints(connection_points);\\n }\\n set_inputless_param_names(names) {\\n return this._inputless_param_names = names;\\n }\\n createSpareParameters() {\\n if (this.node.scene().loadingController.isLoading()) {\\n return;\\n }\\n const current_param_names = this.node.params.spare_names;\\n const params_update_options = {};\\n for (let param_name of current_param_names) {\\n if (this.node.params.has(param_name)) {\\n const param = this.node.params.get(param_name);\\n if (param) {\\n this._raw_input_serialized_by_param_name.set(param_name, param.rawInputSerialized());\\n this._default_value_serialized_by_param_name.set(param_name, param.defaultValueSerialized());\\n params_update_options.namesToDelete = params_update_options.namesToDelete || [];\\n params_update_options.namesToDelete.push(param_name);\\n }\\n }\\n }\\n for (let connection_point of this.node.io.inputs.namedInputConnectionPoints()) {\\n if (connection_point) {\\n const param_name = connection_point.name();\\n const param_type = connection_point.param_type;\\n let init_value = connection_point.init_value;\\n const last_param_init_value = this._default_value_serialized_by_param_name.get(param_name);\\n let default_value_from_name = this.node.paramDefaultValue(param_name);\\n if (default_value_from_name != null) {\\n init_value = default_value_from_name;\\n } else {\\n if (last_param_init_value != null) {\\n init_value = last_param_init_value;\\n } else {\\n init_value = connection_point.init_value;\\n }\\n }\\n if (CoreType.isArray(connection_point.init_value)) {\\n if (CoreType.isNumber(init_value)) {\\n const array = new Array(connection_point.init_value.length);\\n array.fill(init_value);\\n init_value = array;\\n } else if (CoreType.isArray(init_value)) {\\n if (init_value.length == connection_point.init_value.length) {\\n if (last_param_init_value != null) {\\n init_value = connection_point.init_value;\\n }\\n }\\n }\\n }\\n if (init_value != null) {\\n params_update_options.toAdd = params_update_options.toAdd || [];\\n params_update_options.toAdd.push({\\n name: param_name,\\n type: param_type,\\n init_value: ObjectUtils.clone(init_value),\\n raw_input: ObjectUtils.clone(init_value),\\n options: {\\n spare: true\\n }\\n });\\n }\\n }\\n }\\n this.node.params.updateParams(params_update_options);\\n for (let spare_param of this.node.params.spare) {\\n if (!spare_param.parent_param) {\\n const raw_input = this._raw_input_serialized_by_param_name.get(spare_param.name());\\n if (raw_input) {\\n spare_param.set(raw_input);\\n }\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/ConnectionPointsController.ts\\nvar ConnectionPointsController = class {\\n constructor(node, _context2) {\\n this.node = node;\\n this._context = _context2;\\n this._create_spare_params_from_inputs = true;\\n this._functions_overridden = false;\\n this._input_name_function = (index) => {\\n return `in${index}`;\\n };\\n this._output_name_function = (index) => {\\n return index == 0 ? \\\\\\\"val\\\\\\\" : `val${index}`;\\n };\\n this._expected_input_types_function = () => {\\n const type = this.first_input_connection_type() || this.default_connection_type();\\n return [type, type];\\n };\\n this._expected_output_types_function = () => {\\n return [this._expected_input_types_function()[0]];\\n };\\n this._update_signature_if_required_bound = this.update_signature_if_required.bind(this);\\n this._initialized = false;\\n this._spare_params_controller = new ConnectionPointsSpareParamsController(this.node, this._context);\\n }\\n default_connection_type() {\\n return DEFAULT_CONNECTION_POINT_ENUM_MAP[this._context];\\n }\\n create_connection_point(name, type) {\\n return create_connection_point(this._context, name, type);\\n }\\n functions_overridden() {\\n return this._functions_overridden;\\n }\\n initialized() {\\n return this._initialized;\\n }\\n set_create_spare_params_from_inputs(state) {\\n this._create_spare_params_from_inputs = state;\\n }\\n set_input_name_function(func) {\\n this._initialize_if_required();\\n this._input_name_function = func;\\n }\\n set_output_name_function(func) {\\n this._initialize_if_required();\\n this._output_name_function = func;\\n }\\n set_expected_input_types_function(func) {\\n this._initialize_if_required();\\n this._functions_overridden = true;\\n this._expected_input_types_function = func;\\n }\\n set_expected_output_types_function(func) {\\n this._initialize_if_required();\\n this._functions_overridden = true;\\n this._expected_output_types_function = func;\\n }\\n input_name(index) {\\n return this._wrapped_input_name_function(index);\\n }\\n output_name(index) {\\n return this._wrapped_output_name_function(index);\\n }\\n initializeNode() {\\n if (this._initialized) {\\n console.warn(\\\\\\\"already initialized\\\\\\\", this.node);\\n return;\\n }\\n this._initialized = true;\\n this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\", this._update_signature_if_required_bound);\\n this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\", this._update_signature_if_required_bound);\\n this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\", this._update_signature_if_required_bound);\\n this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\", this._update_signature_if_required_bound);\\n if (!this._spare_params_controller.initialized()) {\\n this._spare_params_controller.initializeNode();\\n }\\n }\\n _initialize_if_required() {\\n if (!this._initialized) {\\n this.initializeNode();\\n }\\n }\\n get spare_params() {\\n return this._spare_params_controller;\\n }\\n update_signature_if_required(dirty_trigger) {\\n if (!this.node.lifecycle.creationCompleted() || !this._connections_match_inputs()) {\\n this.update_connection_types();\\n this.node.removeDirtyState();\\n if (!this.node.scene().loadingController.isLoading()) {\\n this.make_successors_update_signatures();\\n }\\n }\\n }\\n make_successors_update_signatures() {\\n const successors = this.node.graphAllSuccessors();\\n if (this.node.childrenAllowed()) {\\n const subnet_inputs = this.node.nodesByType(NetworkChildNodeType.INPUT);\\n const subnet_outputs = this.node.nodesByType(NetworkChildNodeType.OUTPUT);\\n for (let subnet_input of subnet_inputs) {\\n successors.push(subnet_input);\\n }\\n for (let subnet_output of subnet_outputs) {\\n successors.push(subnet_output);\\n }\\n }\\n for (let graph_node of successors) {\\n const node = graph_node;\\n if (node.io && node.io.has_connection_points_controller && node.io.connection_points.initialized()) {\\n node.io.connection_points.update_signature_if_required(this.node);\\n }\\n }\\n }\\n update_connection_types() {\\n const set_dirty = false;\\n const expected_input_types = this._wrapped_expected_input_types_function();\\n const expected_output_types = this._wrapped_expected_output_types_function();\\n const named_input_connection_points = [];\\n for (let i = 0; i < expected_input_types.length; i++) {\\n const type = expected_input_types[i];\\n const point = this.create_connection_point(this._wrapped_input_name_function(i), type);\\n named_input_connection_points.push(point);\\n }\\n const named_output_connect_points = [];\\n for (let i = 0; i < expected_output_types.length; i++) {\\n const type = expected_output_types[i];\\n const point = this.create_connection_point(this._wrapped_output_name_function(i), type);\\n named_output_connect_points.push(point);\\n }\\n this.node.io.inputs.setNamedInputConnectionPoints(named_input_connection_points);\\n this.node.io.outputs.setNamedOutputConnectionPoints(named_output_connect_points, set_dirty);\\n if (this._create_spare_params_from_inputs) {\\n this._spare_params_controller.createSpareParameters();\\n }\\n }\\n _connections_match_inputs() {\\n const current_input_types = this.node.io.inputs.namedInputConnectionPoints().map((c) => c?.type());\\n const current_output_types = this.node.io.outputs.namedOutputConnectionPoints().map((c) => c?.type());\\n const expected_input_types = this._wrapped_expected_input_types_function();\\n const expected_output_types = this._wrapped_expected_output_types_function();\\n if (expected_input_types.length != current_input_types.length) {\\n return false;\\n }\\n if (expected_output_types.length != current_output_types.length) {\\n return false;\\n }\\n for (let i = 0; i < current_input_types.length; i++) {\\n if (current_input_types[i] != expected_input_types[i]) {\\n return false;\\n }\\n }\\n for (let i = 0; i < current_output_types.length; i++) {\\n if (current_output_types[i] != expected_output_types[i]) {\\n return false;\\n }\\n }\\n return true;\\n }\\n _wrapped_expected_input_types_function() {\\n if (this.node.scene().loadingController.isLoading()) {\\n const in_data = this.node.io.saved_connection_points_data.in();\\n if (in_data) {\\n return in_data.map((d) => d.type);\\n }\\n }\\n return this._expected_input_types_function();\\n }\\n _wrapped_expected_output_types_function() {\\n if (this.node.scene().loadingController.isLoading()) {\\n const out_data = this.node.io.saved_connection_points_data.out();\\n if (out_data) {\\n return out_data.map((d) => d.type);\\n }\\n }\\n return this._expected_output_types_function();\\n }\\n _wrapped_input_name_function(index) {\\n if (this.node.scene().loadingController.isLoading()) {\\n const in_data = this.node.io.saved_connection_points_data.in();\\n if (in_data) {\\n return in_data[index].name;\\n }\\n }\\n return this._input_name_function(index);\\n }\\n _wrapped_output_name_function(index) {\\n if (this.node.scene().loadingController.isLoading()) {\\n const out_data = this.node.io.saved_connection_points_data.out();\\n if (out_data) {\\n return out_data[index].name;\\n }\\n }\\n return this._output_name_function(index);\\n }\\n first_input_connection_type() {\\n return this.input_connection_type(0);\\n }\\n input_connection_type(index) {\\n const connections = this.node.io.connections.inputConnections();\\n if (connections) {\\n const connection = connections[index];\\n if (connection) {\\n return connection.src_connection_point().type();\\n }\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/io/IOController.ts\\nvar IOController = class {\\n constructor(node) {\\n this.node = node;\\n this._connections = new ConnectionsController(this.node);\\n }\\n get connections() {\\n return this._connections;\\n }\\n get inputs() {\\n return this._inputs = this._inputs || new InputsController2(this.node);\\n }\\n has_inputs() {\\n return this._inputs != null;\\n }\\n get outputs() {\\n return this._outputs = this._outputs || new OutputsController(this.node);\\n }\\n has_outputs() {\\n return this._outputs != null;\\n }\\n get connection_points() {\\n return this._connection_points = this._connection_points || new ConnectionPointsController(this.node, this.node.context());\\n }\\n get has_connection_points_controller() {\\n return this._connection_points != null;\\n }\\n get saved_connection_points_data() {\\n return this._saved_connection_points_data = this._saved_connection_points_data || new SavedConnectionPointsDataController(this.node);\\n }\\n clear_saved_connection_points_data() {\\n if (this._saved_connection_points_data) {\\n this._saved_connection_points_data.clear();\\n this._saved_connection_points_data = void 0;\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/params/ParamsAccessor.ts\\nvar ParamsAccessor = class {\\n constructor() {\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/_Base.ts\\nvar TypedNode = class extends CoreGraphNode {\\n constructor(scene, name = \\\\\\\"BaseNode\\\\\\\", params_init_value_overrides) {\\n super(scene, name);\\n this.params_init_value_overrides = params_init_value_overrides;\\n this.containerController = new TypedContainerController(this);\\n this.pv = new ParamsValueAccessor();\\n this.p = new ParamsAccessor();\\n this._initialized = false;\\n }\\n copy_param_values(node) {\\n const non_spare = this.params.non_spare;\\n for (let param of non_spare) {\\n const other_param = node.params.get(param.name());\\n if (other_param) {\\n param.copy_value(other_param);\\n }\\n }\\n }\\n get parentController() {\\n return this._parent_controller = this._parent_controller || new HierarchyParentController(this);\\n }\\n static displayedInputNames() {\\n return [];\\n }\\n get childrenControllerContext() {\\n return this._children_controller_context;\\n }\\n _create_children_controller() {\\n if (this._children_controller_context) {\\n return new HierarchyChildrenController(this, this._children_controller_context);\\n }\\n }\\n get childrenController() {\\n return this._children_controller = this._children_controller || this._create_children_controller();\\n }\\n childrenAllowed() {\\n return this._children_controller_context != null;\\n }\\n get uiData() {\\n return this._ui_data = this._ui_data || new UIData(this);\\n }\\n get states() {\\n return this._states = this._states || new StatesController(this);\\n }\\n get lifecycle() {\\n return this._lifecycle = this._lifecycle || new LifeCycleController(this);\\n }\\n get serializer() {\\n return this._serializer = this._serializer || new NodeSerializer(this);\\n }\\n get cookController() {\\n return this._cook_controller = this._cook_controller || new NodeCookController(this);\\n }\\n get io() {\\n return this._io = this._io || new IOController(this);\\n }\\n get nameController() {\\n return this._name_controller = this._name_controller || new NameController(this);\\n }\\n setName(name) {\\n this.nameController.setName(name);\\n }\\n _set_core_name(name) {\\n this._name = name;\\n }\\n get params() {\\n return this._params_controller = this._params_controller || new ParamsController(this);\\n }\\n initialize_base_and_node() {\\n if (!this._initialized) {\\n this._initialized = true;\\n this.displayNodeController?.initializeNode();\\n this.initializeBaseNode();\\n this.initializeNode();\\n if (this.polyNodeController) {\\n this.polyNodeController.initializeNode();\\n }\\n } else {\\n console.warn(\\\\\\\"node already initialized\\\\\\\");\\n }\\n }\\n initializeBaseNode() {\\n }\\n initializeNode() {\\n }\\n static type() {\\n throw \\\\\\\"type to be overriden\\\\\\\";\\n }\\n type() {\\n const c = this.constructor;\\n return c.type();\\n }\\n static context() {\\n console.error(\\\\\\\"node has no node_context\\\\\\\", this);\\n throw \\\\\\\"context requires override\\\\\\\";\\n }\\n context() {\\n const c = this.constructor;\\n return c.context();\\n }\\n static require_webgl2() {\\n return false;\\n }\\n require_webgl2() {\\n const c = this.constructor;\\n return c.require_webgl2();\\n }\\n setParent(parent) {\\n this.parentController.setParent(parent);\\n }\\n parent() {\\n return this.parentController.parent();\\n }\\n firstAncestorWithContext(context) {\\n return this.parentController.firstAncestorWithContext(context);\\n }\\n root() {\\n return this._scene.root();\\n }\\n path(relative_to_parent) {\\n return this.parentController.path(relative_to_parent);\\n }\\n createParams() {\\n }\\n addParam(type, name, default_value, options) {\\n return this._params_controller?.addParam(type, name, default_value, options);\\n }\\n paramDefaultValue(name) {\\n return null;\\n }\\n cook(input_contents) {\\n return null;\\n }\\n onCookEnd(callbackName, callback) {\\n this.cookController.registerOnCookEnd(callbackName, callback);\\n }\\n async compute() {\\n if (this.isDirty() || this.flags?.bypass?.active()) {\\n return await this.containerController.compute();\\n } else {\\n return this.containerController.container();\\n }\\n }\\n _setContainer(content, message = null) {\\n this.containerController.container().set_content(content);\\n if (content != null) {\\n if (!content.name) {\\n content.name = this.path();\\n }\\n if (!content.node) {\\n content.node = this;\\n }\\n }\\n this.cookController.endCook(message);\\n }\\n createNode(nodeClass, params_init_value_overrides) {\\n return this.childrenController?.createNode(nodeClass, params_init_value_overrides);\\n }\\n create_operation_container(type, operation_container_name, params_init_value_overrides) {\\n return this.childrenController?.create_operation_container(type, operation_container_name, params_init_value_overrides);\\n }\\n removeNode(node) {\\n this.childrenController?.removeNode(node);\\n }\\n dispose() {\\n super.dispose();\\n this.setParent(null);\\n this.io.inputs.dispose();\\n this.lifecycle.dispose();\\n this.displayNodeController?.dispose();\\n this.nameController.dispose();\\n this.childrenController?.dispose();\\n this.params.dispose();\\n }\\n children() {\\n return this.childrenController?.children() || [];\\n }\\n node(path) {\\n return this.parentController?.findNode(path) || null;\\n }\\n nodeSibbling(name) {\\n const parent = this.parent();\\n if (parent) {\\n const node = parent.childrenController?.child_by_name(name);\\n if (node) {\\n return node;\\n }\\n }\\n return null;\\n }\\n nodesByType(type) {\\n return this.childrenController?.nodesByType(type) || [];\\n }\\n setInput(input_index_or_name, node, output_index_or_name = 0) {\\n this.io.inputs.setInput(input_index_or_name, node, output_index_or_name);\\n }\\n emit(event_name, data = null) {\\n this.scene().dispatchController.dispatch(this, event_name, data);\\n }\\n toJSON(include_param_components = false) {\\n return this.serializer.toJSON(include_param_components);\\n }\\n async requiredModules() {\\n }\\n usedAssembler() {\\n }\\n integrationData() {\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/obj/_Base.ts\\nvar INPUT_OBJECT_NAME = \\\\\\\"parent object\\\\\\\";\\nvar DEFAULT_INPUT_NAMES = [INPUT_OBJECT_NAME, INPUT_OBJECT_NAME, INPUT_OBJECT_NAME, INPUT_OBJECT_NAME];\\nvar ObjNodeRenderOrder;\\n(function(ObjNodeRenderOrder2) {\\n ObjNodeRenderOrder2[ObjNodeRenderOrder2[\\\\\\\"MANAGER\\\\\\\"] = 0] = \\\\\\\"MANAGER\\\\\\\";\\n ObjNodeRenderOrder2[ObjNodeRenderOrder2[\\\\\\\"CAMERA\\\\\\\"] = 2] = \\\\\\\"CAMERA\\\\\\\";\\n ObjNodeRenderOrder2[ObjNodeRenderOrder2[\\\\\\\"LIGHT\\\\\\\"] = 3] = \\\\\\\"LIGHT\\\\\\\";\\n})(ObjNodeRenderOrder || (ObjNodeRenderOrder = {}));\\nvar TypedObjNode = class extends TypedNode {\\n constructor() {\\n super(...arguments);\\n this.renderOrder = 0;\\n this._children_group = this._create_children_group();\\n this._attachableToHierarchy = true;\\n this._used_in_scene = true;\\n }\\n static context() {\\n return NodeContext.OBJ;\\n }\\n static displayedInputNames() {\\n return DEFAULT_INPUT_NAMES;\\n }\\n _create_children_group() {\\n const group = new Group();\\n group.matrixAutoUpdate = false;\\n return group;\\n }\\n attachableToHierarchy() {\\n return this._attachableToHierarchy;\\n }\\n usedInScene() {\\n return this._used_in_scene;\\n }\\n addObjectToParent(parent) {\\n if (this.attachableToHierarchy()) {\\n parent.add(this.object);\\n }\\n }\\n removeObjectFromParent() {\\n if (this.attachableToHierarchy()) {\\n const parent = this.object.parent;\\n if (parent) {\\n parent.remove(this.object);\\n }\\n }\\n }\\n initializeBaseNode() {\\n this._object = this._create_object_with_attributes();\\n this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this));\\n this.set_object_name();\\n }\\n get children_group() {\\n return this._children_group;\\n }\\n get object() {\\n return this._object;\\n }\\n _create_object_with_attributes() {\\n const object = this.createObject();\\n object.node = this;\\n object.add(this._children_group);\\n return object;\\n }\\n set_object_name() {\\n if (this._object) {\\n this._object.name = this.path();\\n this._children_group.name = `${this.path()}:parented_outputs`;\\n }\\n }\\n createObject() {\\n const object = new Object3D();\\n object.matrixAutoUpdate = false;\\n return object;\\n }\\n isDisplayNodeCooking() {\\n if (this.displayNodeController) {\\n const displayNode = this.displayNodeController.displayNode();\\n if (displayNode) {\\n return displayNode.cookController.isCooking();\\n }\\n }\\n return false;\\n }\\n isDisplayed() {\\n return this.flags?.display?.active() || false;\\n }\\n};\\n\\n// ../../../node_modules/three/src/audio/AudioAnalyser.js\\nvar AudioAnalyser = class {\\n constructor(audio, fftSize = 2048) {\\n this.analyser = audio.context.createAnalyser();\\n this.analyser.fftSize = fftSize;\\n this.data = new Uint8Array(this.analyser.frequencyBinCount);\\n audio.getOutput().connect(this.analyser);\\n }\\n getFrequencyData() {\\n this.analyser.getByteFrequencyData(this.data);\\n return this.data;\\n }\\n getAverageFrequency() {\\n let value = 0;\\n const data = this.getFrequencyData();\\n for (let i = 0; i < data.length; i++) {\\n value += data[i];\\n }\\n return value / data.length;\\n }\\n};\\n\\n// ../../../node_modules/three/src/audio/Audio.js\\nvar Audio2 = class extends Object3D {\\n constructor(listener) {\\n super();\\n this.type = \\\\\\\"Audio\\\\\\\";\\n this.listener = listener;\\n this.context = listener.context;\\n this.gain = this.context.createGain();\\n this.gain.connect(listener.getInput());\\n this.autoplay = false;\\n this.buffer = null;\\n this.detune = 0;\\n this.loop = false;\\n this.loopStart = 0;\\n this.loopEnd = 0;\\n this.offset = 0;\\n this.duration = void 0;\\n this.playbackRate = 1;\\n this.isPlaying = false;\\n this.hasPlaybackControl = true;\\n this.source = null;\\n this.sourceType = \\\\\\\"empty\\\\\\\";\\n this._startedAt = 0;\\n this._progress = 0;\\n this._connected = false;\\n this.filters = [];\\n }\\n getOutput() {\\n return this.gain;\\n }\\n setNodeSource(audioNode) {\\n this.hasPlaybackControl = false;\\n this.sourceType = \\\\\\\"audioNode\\\\\\\";\\n this.source = audioNode;\\n this.connect();\\n return this;\\n }\\n setMediaElementSource(mediaElement) {\\n this.hasPlaybackControl = false;\\n this.sourceType = \\\\\\\"mediaNode\\\\\\\";\\n this.source = this.context.createMediaElementSource(mediaElement);\\n this.connect();\\n return this;\\n }\\n setMediaStreamSource(mediaStream) {\\n this.hasPlaybackControl = false;\\n this.sourceType = \\\\\\\"mediaStreamNode\\\\\\\";\\n this.source = this.context.createMediaStreamSource(mediaStream);\\n this.connect();\\n return this;\\n }\\n setBuffer(audioBuffer) {\\n this.buffer = audioBuffer;\\n this.sourceType = \\\\\\\"buffer\\\\\\\";\\n if (this.autoplay)\\n this.play();\\n return this;\\n }\\n play(delay = 0) {\\n if (this.isPlaying === true) {\\n console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");\\n return;\\n }\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return;\\n }\\n this._startedAt = this.context.currentTime + delay;\\n const source = this.context.createBufferSource();\\n source.buffer = this.buffer;\\n source.loop = this.loop;\\n source.loopStart = this.loopStart;\\n source.loopEnd = this.loopEnd;\\n source.onended = this.onEnded.bind(this);\\n source.start(this._startedAt, this._progress + this.offset, this.duration);\\n this.isPlaying = true;\\n this.source = source;\\n this.setDetune(this.detune);\\n this.setPlaybackRate(this.playbackRate);\\n return this.connect();\\n }\\n pause() {\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return;\\n }\\n if (this.isPlaying === true) {\\n this._progress += Math.max(this.context.currentTime - this._startedAt, 0) * this.playbackRate;\\n if (this.loop === true) {\\n this._progress = this._progress % (this.duration || this.buffer.duration);\\n }\\n this.source.stop();\\n this.source.onended = null;\\n this.isPlaying = false;\\n }\\n return this;\\n }\\n stop() {\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return;\\n }\\n this._progress = 0;\\n this.source.stop();\\n this.source.onended = null;\\n this.isPlaying = false;\\n return this;\\n }\\n connect() {\\n if (this.filters.length > 0) {\\n this.source.connect(this.filters[0]);\\n for (let i = 1, l = this.filters.length; i < l; i++) {\\n this.filters[i - 1].connect(this.filters[i]);\\n }\\n this.filters[this.filters.length - 1].connect(this.getOutput());\\n } else {\\n this.source.connect(this.getOutput());\\n }\\n this._connected = true;\\n return this;\\n }\\n disconnect() {\\n if (this.filters.length > 0) {\\n this.source.disconnect(this.filters[0]);\\n for (let i = 1, l = this.filters.length; i < l; i++) {\\n this.filters[i - 1].disconnect(this.filters[i]);\\n }\\n this.filters[this.filters.length - 1].disconnect(this.getOutput());\\n } else {\\n this.source.disconnect(this.getOutput());\\n }\\n this._connected = false;\\n return this;\\n }\\n getFilters() {\\n return this.filters;\\n }\\n setFilters(value) {\\n if (!value)\\n value = [];\\n if (this._connected === true) {\\n this.disconnect();\\n this.filters = value.slice();\\n this.connect();\\n } else {\\n this.filters = value.slice();\\n }\\n return this;\\n }\\n setDetune(value) {\\n this.detune = value;\\n if (this.source.detune === void 0)\\n return;\\n if (this.isPlaying === true) {\\n this.source.detune.setTargetAtTime(this.detune, this.context.currentTime, 0.01);\\n }\\n return this;\\n }\\n getDetune() {\\n return this.detune;\\n }\\n getFilter() {\\n return this.getFilters()[0];\\n }\\n setFilter(filter) {\\n return this.setFilters(filter ? [filter] : []);\\n }\\n setPlaybackRate(value) {\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return;\\n }\\n this.playbackRate = value;\\n if (this.isPlaying === true) {\\n this.source.playbackRate.setTargetAtTime(this.playbackRate, this.context.currentTime, 0.01);\\n }\\n return this;\\n }\\n getPlaybackRate() {\\n return this.playbackRate;\\n }\\n onEnded() {\\n this.isPlaying = false;\\n }\\n getLoop() {\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return false;\\n }\\n return this.loop;\\n }\\n setLoop(value) {\\n if (this.hasPlaybackControl === false) {\\n console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");\\n return;\\n }\\n this.loop = value;\\n if (this.isPlaying === true) {\\n this.source.loop = this.loop;\\n }\\n return this;\\n }\\n setLoopStart(value) {\\n this.loopStart = value;\\n return this;\\n }\\n setLoopEnd(value) {\\n this.loopEnd = value;\\n return this;\\n }\\n getVolume() {\\n return this.gain.gain.value;\\n }\\n setVolume(value) {\\n this.gain.gain.setTargetAtTime(value, this.context.currentTime, 0.01);\\n return this;\\n }\\n};\\n\\n// ../../../node_modules/three/src/audio/AudioContext.js\\nvar _context;\\nvar AudioContext = {\\n getContext: function() {\\n if (_context === void 0) {\\n _context = new (window.AudioContext || window.webkitAudioContext)();\\n }\\n return _context;\\n },\\n setContext: function(value) {\\n _context = value;\\n }\\n};\\n\\n// ../../../node_modules/three/src/loaders/Cache.js\\nvar Cache = {\\n enabled: false,\\n files: {},\\n add: function(key, file) {\\n if (this.enabled === false)\\n return;\\n this.files[key] = file;\\n },\\n get: function(key) {\\n if (this.enabled === false)\\n return;\\n return this.files[key];\\n },\\n remove: function(key) {\\n delete this.files[key];\\n },\\n clear: function() {\\n this.files = {};\\n }\\n};\\n\\n// ../../../node_modules/three/src/loaders/LoadingManager.js\\nvar LoadingManager = class {\\n constructor(onLoad, onProgress, onError) {\\n const scope = this;\\n let isLoading = false;\\n let itemsLoaded = 0;\\n let itemsTotal = 0;\\n let urlModifier = void 0;\\n const handlers = [];\\n this.onStart = void 0;\\n this.onLoad = onLoad;\\n this.onProgress = onProgress;\\n this.onError = onError;\\n this.itemStart = function(url) {\\n itemsTotal++;\\n if (isLoading === false) {\\n if (scope.onStart !== void 0) {\\n scope.onStart(url, itemsLoaded, itemsTotal);\\n }\\n }\\n isLoading = true;\\n };\\n this.itemEnd = function(url) {\\n itemsLoaded++;\\n if (scope.onProgress !== void 0) {\\n scope.onProgress(url, itemsLoaded, itemsTotal);\\n }\\n if (itemsLoaded === itemsTotal) {\\n isLoading = false;\\n if (scope.onLoad !== void 0) {\\n scope.onLoad();\\n }\\n }\\n };\\n this.itemError = function(url) {\\n if (scope.onError !== void 0) {\\n scope.onError(url);\\n }\\n };\\n this.resolveURL = function(url) {\\n if (urlModifier) {\\n return urlModifier(url);\\n }\\n return url;\\n };\\n this.setURLModifier = function(transform) {\\n urlModifier = transform;\\n return this;\\n };\\n this.addHandler = function(regex, loader) {\\n handlers.push(regex, loader);\\n return this;\\n };\\n this.removeHandler = function(regex) {\\n const index = handlers.indexOf(regex);\\n if (index !== -1) {\\n handlers.splice(index, 2);\\n }\\n return this;\\n };\\n this.getHandler = function(file) {\\n for (let i = 0, l = handlers.length; i < l; i += 2) {\\n const regex = handlers[i];\\n const loader = handlers[i + 1];\\n if (regex.global)\\n regex.lastIndex = 0;\\n if (regex.test(file)) {\\n return loader;\\n }\\n }\\n return null;\\n };\\n }\\n};\\nvar DefaultLoadingManager = new LoadingManager();\\n\\n// ../../../node_modules/three/src/loaders/Loader.js\\nvar Loader = class {\\n constructor(manager) {\\n this.manager = manager !== void 0 ? manager : DefaultLoadingManager;\\n this.crossOrigin = \\\\\\\"anonymous\\\\\\\";\\n this.withCredentials = false;\\n this.path = \\\\\\\"\\\\\\\";\\n this.resourcePath = \\\\\\\"\\\\\\\";\\n this.requestHeader = {};\\n }\\n load() {\\n }\\n loadAsync(url, onProgress) {\\n const scope = this;\\n return new Promise(function(resolve, reject) {\\n scope.load(url, resolve, onProgress, reject);\\n });\\n }\\n parse() {\\n }\\n setCrossOrigin(crossOrigin) {\\n this.crossOrigin = crossOrigin;\\n return this;\\n }\\n setWithCredentials(value) {\\n this.withCredentials = value;\\n return this;\\n }\\n setPath(path) {\\n this.path = path;\\n return this;\\n }\\n setResourcePath(resourcePath) {\\n this.resourcePath = resourcePath;\\n return this;\\n }\\n setRequestHeader(requestHeader) {\\n this.requestHeader = requestHeader;\\n return this;\\n }\\n};\\n\\n// ../../../node_modules/three/src/loaders/FileLoader.js\\nvar loading = {};\\nvar FileLoader = class extends Loader {\\n constructor(manager) {\\n super(manager);\\n }\\n load(url, onLoad, onProgress, onError) {\\n if (url === void 0)\\n url = \\\\\\\"\\\\\\\";\\n if (this.path !== void 0)\\n url = this.path + url;\\n url = this.manager.resolveURL(url);\\n const scope = this;\\n const cached = Cache.get(url);\\n if (cached !== void 0) {\\n scope.manager.itemStart(url);\\n setTimeout(function() {\\n if (onLoad)\\n onLoad(cached);\\n scope.manager.itemEnd(url);\\n }, 0);\\n return cached;\\n }\\n if (loading[url] !== void 0) {\\n loading[url].push({\\n onLoad,\\n onProgress,\\n onError\\n });\\n return;\\n }\\n const dataUriRegex = /^data:(.*?)(;base64)?,(.*)$/;\\n const dataUriRegexResult = url.match(dataUriRegex);\\n let request;\\n if (dataUriRegexResult) {\\n const mimeType = dataUriRegexResult[1];\\n const isBase64 = !!dataUriRegexResult[2];\\n let data = dataUriRegexResult[3];\\n data = decodeURIComponent(data);\\n if (isBase64)\\n data = atob(data);\\n try {\\n let response;\\n const responseType = (this.responseType || \\\\\\\"\\\\\\\").toLowerCase();\\n switch (responseType) {\\n case \\\\\\\"arraybuffer\\\\\\\":\\n case \\\\\\\"blob\\\\\\\":\\n const view = new Uint8Array(data.length);\\n for (let i = 0; i < data.length; i++) {\\n view[i] = data.charCodeAt(i);\\n }\\n if (responseType === \\\\\\\"blob\\\\\\\") {\\n response = new Blob([view.buffer], {type: mimeType});\\n } else {\\n response = view.buffer;\\n }\\n break;\\n case \\\\\\\"document\\\\\\\":\\n const parser = new DOMParser();\\n response = parser.parseFromString(data, mimeType);\\n break;\\n case \\\\\\\"json\\\\\\\":\\n response = JSON.parse(data);\\n break;\\n default:\\n response = data;\\n break;\\n }\\n setTimeout(function() {\\n if (onLoad)\\n onLoad(response);\\n scope.manager.itemEnd(url);\\n }, 0);\\n } catch (error) {\\n setTimeout(function() {\\n if (onError)\\n onError(error);\\n scope.manager.itemError(url);\\n scope.manager.itemEnd(url);\\n }, 0);\\n }\\n } else {\\n loading[url] = [];\\n loading[url].push({\\n onLoad,\\n onProgress,\\n onError\\n });\\n request = new XMLHttpRequest();\\n request.open(\\\\\\\"GET\\\\\\\", url, true);\\n request.addEventListener(\\\\\\\"load\\\\\\\", function(event) {\\n const response = this.response;\\n const callbacks = loading[url];\\n delete loading[url];\\n if (this.status === 200 || this.status === 0) {\\n if (this.status === 0)\\n console.warn(\\\\\\\"THREE.FileLoader: HTTP Status 0 received.\\\\\\\");\\n Cache.add(url, response);\\n for (let i = 0, il = callbacks.length; i < il; i++) {\\n const callback = callbacks[i];\\n if (callback.onLoad)\\n callback.onLoad(response);\\n }\\n scope.manager.itemEnd(url);\\n } else {\\n for (let i = 0, il = callbacks.length; i < il; i++) {\\n const callback = callbacks[i];\\n if (callback.onError)\\n callback.onError(event);\\n }\\n scope.manager.itemError(url);\\n scope.manager.itemEnd(url);\\n }\\n }, false);\\n request.addEventListener(\\\\\\\"progress\\\\\\\", function(event) {\\n const callbacks = loading[url];\\n for (let i = 0, il = callbacks.length; i < il; i++) {\\n const callback = callbacks[i];\\n if (callback.onProgress)\\n callback.onProgress(event);\\n }\\n }, false);\\n request.addEventListener(\\\\\\\"error\\\\\\\", function(event) {\\n const callbacks = loading[url];\\n delete loading[url];\\n for (let i = 0, il = callbacks.length; i < il; i++) {\\n const callback = callbacks[i];\\n if (callback.onError)\\n callback.onError(event);\\n }\\n scope.manager.itemError(url);\\n scope.manager.itemEnd(url);\\n }, false);\\n request.addEventListener(\\\\\\\"abort\\\\\\\", function(event) {\\n const callbacks = loading[url];\\n delete loading[url];\\n for (let i = 0, il = callbacks.length; i < il; i++) {\\n const callback = callbacks[i];\\n if (callback.onError)\\n callback.onError(event);\\n }\\n scope.manager.itemError(url);\\n scope.manager.itemEnd(url);\\n }, false);\\n if (this.responseType !== void 0)\\n request.responseType = this.responseType;\\n if (this.withCredentials !== void 0)\\n request.withCredentials = this.withCredentials;\\n if (request.overrideMimeType)\\n request.overrideMimeType(this.mimeType !== void 0 ? this.mimeType : \\\\\\\"text/plain\\\\\\\");\\n for (const header in this.requestHeader) {\\n request.setRequestHeader(header, this.requestHeader[header]);\\n }\\n request.send(null);\\n }\\n scope.manager.itemStart(url);\\n return request;\\n }\\n setResponseType(value) {\\n this.responseType = value;\\n return this;\\n }\\n setMimeType(value) {\\n this.mimeType = value;\\n return this;\\n }\\n};\\n\\n// ../../../node_modules/three/src/loaders/AudioLoader.js\\nvar AudioLoader = class extends Loader {\\n constructor(manager) {\\n super(manager);\\n }\\n load(url, onLoad, onProgress, onError) {\\n const scope = this;\\n const loader = new FileLoader(this.manager);\\n loader.setResponseType(\\\\\\\"arraybuffer\\\\\\\");\\n loader.setPath(this.path);\\n loader.setRequestHeader(this.requestHeader);\\n loader.setWithCredentials(this.withCredentials);\\n loader.load(url, function(buffer) {\\n try {\\n const bufferCopy = buffer.slice(0);\\n const context = AudioContext.getContext();\\n context.decodeAudioData(bufferCopy, function(audioBuffer) {\\n onLoad(audioBuffer);\\n });\\n } catch (e) {\\n if (onError) {\\n onError(e);\\n } else {\\n console.error(e);\\n }\\n scope.manager.itemError(url);\\n }\\n }, onProgress, onError);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/utils/params/ParamsConfig.ts\\nvar ParamTemplate = class {\\n constructor(type, init_value, options) {\\n this.type = type;\\n this.init_value = init_value;\\n this.options = options;\\n }\\n};\\nvar ParamConfig = class {\\n static BUTTON(init_value, options) {\\n return new ParamTemplate(ParamType.BUTTON, init_value, options);\\n }\\n static BOOLEAN(init_value, options) {\\n return new ParamTemplate(ParamType.BOOLEAN, init_value, options);\\n }\\n static COLOR(init_value, options) {\\n if (init_value instanceof Color) {\\n init_value = init_value.toArray();\\n }\\n return new ParamTemplate(ParamType.COLOR, init_value, options);\\n }\\n static FLOAT(init_value, options) {\\n return new ParamTemplate(ParamType.FLOAT, init_value, options);\\n }\\n static FOLDER(init_value = null, options) {\\n return new ParamTemplate(ParamType.FOLDER, init_value, options);\\n }\\n static INTEGER(init_value, options) {\\n return new ParamTemplate(ParamType.INTEGER, init_value, options);\\n }\\n static RAMP(init_value = RampParam.DEFAULT_VALUE, options) {\\n return new ParamTemplate(ParamType.RAMP, init_value, options);\\n }\\n static STRING(init_value = \\\\\\\"\\\\\\\", options) {\\n return new ParamTemplate(ParamType.STRING, init_value, options);\\n }\\n static VECTOR2(init_value, options) {\\n if (init_value instanceof Vector2) {\\n init_value = init_value.toArray();\\n }\\n return new ParamTemplate(ParamType.VECTOR2, init_value, options);\\n }\\n static VECTOR3(init_value, options) {\\n if (init_value instanceof Vector3) {\\n init_value = init_value.toArray();\\n }\\n return new ParamTemplate(ParamType.VECTOR3, init_value, options);\\n }\\n static VECTOR4(init_value, options) {\\n if (init_value instanceof Vector4) {\\n init_value = init_value.toArray();\\n }\\n return new ParamTemplate(ParamType.VECTOR4, init_value, options);\\n }\\n static OPERATOR_PATH(init_value, options) {\\n return new ParamTemplate(ParamType.OPERATOR_PATH, init_value, options);\\n }\\n static NODE_PATH(init_value, options) {\\n return new ParamTemplate(ParamType.NODE_PATH, init_value, options);\\n }\\n static PARAM_PATH(init_value, options) {\\n return new ParamTemplate(ParamType.PARAM_PATH, init_value, options);\\n }\\n};\\nvar NodeParamsConfig = class {\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/Transform.ts\\nvar TransformTargetType;\\n(function(TransformTargetType2) {\\n TransformTargetType2[\\\\\\\"OBJECTS\\\\\\\"] = \\\\\\\"objects\\\\\\\";\\n TransformTargetType2[\\\\\\\"GEOMETRIES\\\\\\\"] = \\\\\\\"geometries\\\\\\\";\\n})(TransformTargetType || (TransformTargetType = {}));\\nvar TRANSFORM_TARGET_TYPES = [\\n TransformTargetType.GEOMETRIES,\\n TransformTargetType.OBJECTS\\n];\\nvar RotationOrder;\\n(function(RotationOrder2) {\\n RotationOrder2[\\\\\\\"XYZ\\\\\\\"] = \\\\\\\"XYZ\\\\\\\";\\n RotationOrder2[\\\\\\\"XZY\\\\\\\"] = \\\\\\\"XZY\\\\\\\";\\n RotationOrder2[\\\\\\\"YXZ\\\\\\\"] = \\\\\\\"YXZ\\\\\\\";\\n RotationOrder2[\\\\\\\"YZX\\\\\\\"] = \\\\\\\"YZX\\\\\\\";\\n RotationOrder2[\\\\\\\"ZYX\\\\\\\"] = \\\\\\\"ZYX\\\\\\\";\\n RotationOrder2[\\\\\\\"ZXY\\\\\\\"] = \\\\\\\"ZXY\\\\\\\";\\n})(RotationOrder || (RotationOrder = {}));\\nvar ROTATION_ORDERS = [\\n RotationOrder.XYZ,\\n RotationOrder.XZY,\\n RotationOrder.YXZ,\\n RotationOrder.YZX,\\n RotationOrder.ZXY,\\n RotationOrder.ZYX\\n];\\nvar DEFAULT_ROTATION_ORDER = RotationOrder.XYZ;\\nvar CoreTransform = class {\\n constructor() {\\n this._translation_matrix = new Matrix4();\\n this._translation_matrix_q = new Quaternion();\\n this._translation_matrix_s = new Vector3(1, 1, 1);\\n this._matrix = new Matrix4().identity();\\n this._matrix_q = new Quaternion();\\n this._matrix_euler = new Euler();\\n this._matrix_s = new Vector3();\\n this._rotate_geometry_m = new Matrix4();\\n this._rotate_geometry_q = new Quaternion();\\n this._rotate_geometry_vec_dest = new Vector3();\\n }\\n static set_params_from_matrix(matrix, node, options = {}) {\\n let update_scale = options[\\\\\\\"scale\\\\\\\"];\\n if (update_scale == null) {\\n update_scale = true;\\n }\\n matrix.decompose(this.set_params_from_matrix_position, this.set_params_from_matrix_quaternion, this.set_params_from_matrix_scale);\\n this.set_params_from_matrix_euler.setFromQuaternion(this.set_params_from_matrix_quaternion);\\n this.set_params_from_matrix_euler.toVector3(this.set_params_from_matrix_rotation);\\n this.set_params_from_matrix_rotation.divideScalar(Math.PI / 180);\\n this.set_params_from_matrix_position.toArray(this.set_params_from_matrix_t);\\n this.set_params_from_matrix_rotation.toArray(this.set_params_from_matrix_r);\\n this.set_params_from_matrix_scale.toArray(this.set_params_from_matrix_s);\\n node.scene().batchUpdates(() => {\\n node.params.set_vector3(\\\\\\\"t\\\\\\\", this.set_params_from_matrix_t);\\n node.params.set_vector3(\\\\\\\"r\\\\\\\", this.set_params_from_matrix_r);\\n node.params.set_vector3(\\\\\\\"s\\\\\\\", this.set_params_from_matrix_s);\\n if (update_scale) {\\n node.params.set_float(\\\\\\\"scale\\\\\\\", 1);\\n }\\n });\\n }\\n static set_params_from_object(object, node) {\\n object.position.toArray(this.set_params_from_object_position_array);\\n object.rotation.toArray(this.set_params_from_object_rotation_array);\\n this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array);\\n this.set_params_from_object_rotation_deg.multiplyScalar(180 / Math.PI);\\n this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array);\\n node.scene().batchUpdates(() => {\\n node.params.set_vector3(\\\\\\\"t\\\\\\\", this.set_params_from_object_position_array);\\n node.params.set_vector3(\\\\\\\"r\\\\\\\", this.set_params_from_object_rotation_array);\\n });\\n }\\n translation_matrix(t) {\\n this._translation_matrix.compose(t, this._translation_matrix_q, this._translation_matrix_s);\\n return this._translation_matrix;\\n }\\n matrix(t, r, s, scale, rotation_order) {\\n this._matrix_euler.set(degToRad(r.x), degToRad(r.y), degToRad(r.z), rotation_order);\\n this._matrix_q.setFromEuler(this._matrix_euler);\\n this._matrix_s.copy(s).multiplyScalar(scale);\\n this._matrix.compose(t, this._matrix_q, this._matrix_s);\\n return this._matrix;\\n }\\n rotate_geometry(geometry, vec_origin, vec_dest) {\\n this._rotate_geometry_vec_dest.copy(vec_dest);\\n this._rotate_geometry_vec_dest.normalize();\\n this._rotate_geometry_q.setFromUnitVectors(vec_origin, this._rotate_geometry_vec_dest);\\n this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q);\\n geometry.applyMatrix4(this._rotate_geometry_m);\\n }\\n static decompose_matrix(object) {\\n object.matrix.decompose(object.position, object.quaternion, object.scale);\\n }\\n};\\nCoreTransform.set_params_from_matrix_position = new Vector3();\\nCoreTransform.set_params_from_matrix_quaternion = new Quaternion();\\nCoreTransform.set_params_from_matrix_scale = new Vector3();\\nCoreTransform.set_params_from_matrix_euler = new Euler();\\nCoreTransform.set_params_from_matrix_rotation = new Vector3();\\nCoreTransform.set_params_from_matrix_t = [0, 0, 0];\\nCoreTransform.set_params_from_matrix_r = [0, 0, 0];\\nCoreTransform.set_params_from_matrix_s = [0, 0, 0];\\nCoreTransform.set_params_from_object_position_array = [0, 0, 0];\\nCoreTransform.set_params_from_object_rotation_deg = new Vector3();\\nCoreTransform.set_params_from_object_rotation_array = [0, 0, 0];\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/obj/utils/TransformController.ts\\nfunction TransformedParamConfig(Base, default_params) {\\n const matrixAutoUpdate = default_params?.matrixAutoUpdate || false;\\n return class Mixin extends Base {\\n constructor() {\\n super(...arguments);\\n this.transform = ParamConfig.FOLDER();\\n this.keepPosWhenParenting = ParamConfig.BOOLEAN(0);\\n this.rotationOrder = ParamConfig.INTEGER(ROTATION_ORDERS.indexOf(RotationOrder.XYZ), {\\n menu: {\\n entries: ROTATION_ORDERS.map((order, v) => {\\n return {name: order, value: v};\\n })\\n }\\n });\\n this.t = ParamConfig.VECTOR3([0, 0, 0]);\\n this.r = ParamConfig.VECTOR3([0, 0, 0]);\\n this.s = ParamConfig.VECTOR3([1, 1, 1]);\\n this.scale = ParamConfig.FLOAT(1);\\n this.matrixAutoUpdate = ParamConfig.BOOLEAN(matrixAutoUpdate ? 1 : 0);\\n this.updateTransformFromObject = ParamConfig.BUTTON(null, {\\n callback: (node) => {\\n TransformController.PARAM_CALLBACK_update_transform_from_object(node);\\n }\\n });\\n }\\n };\\n}\\nvar TransformedParamsConfig = class extends TransformedParamConfig(NodeParamsConfig) {\\n};\\nvar HOOK_NAME = \\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";\\nvar TransformController = class {\\n constructor(node) {\\n this.node = node;\\n this._cook_main_without_inputs_when_dirty_bound = this._cook_main_without_inputs_when_dirty.bind(this);\\n this._core_transform = new CoreTransform();\\n this._keep_pos_when_parenting_m_object = new Matrix4();\\n this._keep_pos_when_parenting_m_new_parent_inv = new Matrix4();\\n }\\n initializeNode() {\\n if (!this.node.dirtyController.hasHook(HOOK_NAME)) {\\n this.node.dirtyController.addPostDirtyHook(HOOK_NAME, this._cook_main_without_inputs_when_dirty_bound);\\n }\\n }\\n async _cook_main_without_inputs_when_dirty() {\\n await this.node.cookController.cookMainWithoutInputs();\\n }\\n update() {\\n this.update_transform_with_matrix();\\n const object = this.node.object;\\n object.matrixAutoUpdate = isBooleanTrue(this.node.pv.matrixAutoUpdate);\\n }\\n update_transform_with_matrix(matrix) {\\n const object = this.node.object;\\n if (matrix != null && !matrix.equals(object.matrix)) {\\n object.matrix.copy(matrix);\\n object.dispatchEvent({type: \\\\\\\"change\\\\\\\"});\\n } else {\\n this._update_matrix_from_params_with_core_transform();\\n }\\n }\\n _update_matrix_from_params_with_core_transform() {\\n const object = this.node.object;\\n let prev_auto_update = object.matrixAutoUpdate;\\n if (prev_auto_update) {\\n object.matrixAutoUpdate = false;\\n }\\n const matrix = this._core_transform.matrix(this.node.pv.t, this.node.pv.r, this.node.pv.s, this.node.pv.scale, ROTATION_ORDERS[this.node.pv.rotationOrder]);\\n object.matrix.identity();\\n object.applyMatrix4(matrix);\\n this._apply_look_at();\\n object.updateMatrix();\\n if (prev_auto_update) {\\n object.matrixAutoUpdate = true;\\n }\\n object.dispatchEvent({type: \\\\\\\"change\\\\\\\"});\\n }\\n _apply_look_at() {\\n }\\n set_params_from_matrix(matrix, options = {}) {\\n CoreTransform.set_params_from_matrix(matrix, this.node, options);\\n }\\n static update_node_transform_params_if_required(node, new_parent_object) {\\n node.transformController.update_node_transform_params_if_required(new_parent_object);\\n }\\n update_node_transform_params_if_required(new_parent_object) {\\n if (!isBooleanTrue(this.node.pv.keepPosWhenParenting)) {\\n return;\\n }\\n if (!this.node.scene().loadingController.loaded()) {\\n return;\\n }\\n if (new_parent_object == this.node.object.parent) {\\n return;\\n }\\n const object = this.node.object;\\n object.updateMatrixWorld(true);\\n new_parent_object.updateMatrixWorld(true);\\n this._keep_pos_when_parenting_m_object.copy(object.matrixWorld);\\n this._keep_pos_when_parenting_m_new_parent_inv.copy(new_parent_object.matrixWorld);\\n this._keep_pos_when_parenting_m_new_parent_inv.invert();\\n this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv);\\n CoreTransform.set_params_from_matrix(this._keep_pos_when_parenting_m_object, this.node, {scale: true});\\n }\\n update_node_transform_params_from_object(update_matrix = false) {\\n const object = this.node.object;\\n if (update_matrix) {\\n object.updateMatrix();\\n }\\n CoreTransform.set_params_from_matrix(object.matrix, this.node, {scale: true});\\n }\\n static PARAM_CALLBACK_update_transform_from_object(node) {\\n node.transformController.update_node_transform_params_from_object();\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/obj/utils/HierarchyController.ts\\nvar HierarchyController = class {\\n constructor(node) {\\n this.node = node;\\n }\\n initializeNode() {\\n this.node.io.inputs.setCount(0, 1);\\n this.node.io.inputs.set_depends_on_inputs(false);\\n this.node.io.outputs.setHasOneOutput();\\n this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\", () => {\\n this.on_input_updated();\\n });\\n }\\n static on_input_updated(node) {\\n const parent_object = node.root().getParentForNode(node);\\n if (node.transformController && parent_object) {\\n TransformController.update_node_transform_params_if_required(node, parent_object);\\n }\\n if (node.io.inputs.input(0) != null) {\\n node.root().addToParentTransform(node);\\n } else {\\n node.root().removeFromParentTransform(node);\\n }\\n }\\n on_input_updated() {\\n HierarchyController.on_input_updated(this.node);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/loader/_Base.ts\\nvar LOADING_MANAGER = new LoadingManager();\\nLOADING_MANAGER.setURLModifier((url) => {\\n const remapedUrl = Poly.assetUrls.remapedUrl(url);\\n if (remapedUrl) {\\n return remapedUrl;\\n }\\n const blobUrl = Poly.blobs.blobUrl(url);\\n if (blobUrl) {\\n return blobUrl;\\n }\\n return url;\\n});\\nvar CoreBaseLoader2 = class {\\n constructor(_url, _scene, _node) {\\n this._url = _url;\\n this._scene = _scene;\\n this._node = _node;\\n this.loadingManager = LOADING_MANAGER;\\n }\\n static extension(url) {\\n let ext = null;\\n try {\\n const _url = new URL(url);\\n ext = _url.searchParams.get(\\\\\\\"ext\\\\\\\");\\n } catch (e) {\\n }\\n if (!ext) {\\n const url_without_params = url.split(\\\\\\\"?\\\\\\\")[0];\\n const elements = url_without_params.split(\\\\\\\".\\\\\\\");\\n ext = elements[elements.length - 1].toLowerCase();\\n }\\n return ext;\\n }\\n extension() {\\n return CoreBaseLoader2.extension(this._url);\\n }\\n async _urlToLoad() {\\n const {storedUrl, fullUrl} = this._urlData();\\n if (this._node) {\\n await Poly.blobs.fetchBlobForNode({storedUrl, fullUrl, node: this._node});\\n }\\n const blobUrl = Poly.blobs.blobUrl(storedUrl);\\n return blobUrl || fullUrl;\\n }\\n deregisterUrl() {\\n const {storedUrl} = this._urlData();\\n Poly.blobs.deregisterUrl(storedUrl);\\n }\\n _urlData() {\\n let fullUrl = this._url;\\n const storedUrl = this._url.split(\\\\\\\"?\\\\\\\")[0];\\n if (fullUrl[0] != \\\\\\\"h\\\\\\\") {\\n const assets_root = this._scene.assets.root();\\n if (assets_root) {\\n fullUrl = `${assets_root}${fullUrl}`;\\n }\\n }\\n return {fullUrl, storedUrl};\\n }\\n static async _loadMultipleBlobGlobal(options) {\\n const promises = [];\\n for (let file of options.files) {\\n const storedUrl = file.storedUrl;\\n const fullUrl = file.fullUrl;\\n const node = options.node;\\n promises.push(Poly.blobs.fetchBlobGlobal({storedUrl, fullUrl, node}));\\n }\\n const responses = await Promise.all(promises);\\n for (let response of responses) {\\n if (response.error) {\\n options.node.states.error.set(options.error);\\n }\\n }\\n }\\n};\\nvar CoreBaseLoader = CoreBaseLoader2;\\nCoreBaseLoader.loadingManager = LOADING_MANAGER;\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/loader/Audio.ts\\nvar Extension;\\n(function(Extension2) {\\n Extension2[\\\\\\\"MP3\\\\\\\"] = \\\\\\\"mp3\\\\\\\";\\n Extension2[\\\\\\\"WAV\\\\\\\"] = \\\\\\\"wav\\\\\\\";\\n})(Extension || (Extension = {}));\\nvar AUDIO_EXTENSIONS = [Extension.MP3, Extension.WAV];\\nvar CoreLoaderAudio = class extends CoreBaseLoader {\\n async load() {\\n const audioLoader = new AudioLoader(this.loadingManager);\\n const url = await this._urlToLoad();\\n return new Promise((resolve) => {\\n audioLoader.load(url, function(buffer) {\\n resolve(buffer);\\n });\\n });\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/poly/registers/nodes/types/Obj.ts\\nvar ObjType;\\n(function(ObjType2) {\\n ObjType2[\\\\\\\"GEO\\\\\\\"] = \\\\\\\"geo\\\\\\\";\\n ObjType2[\\\\\\\"CUBE_CAMERA\\\\\\\"] = \\\\\\\"cubeCamera\\\\\\\";\\n ObjType2[\\\\\\\"AUDIO_LISTENER\\\\\\\"] = \\\\\\\"audioListener\\\\\\\";\\n ObjType2[\\\\\\\"POSITIONAL_AUDIO\\\\\\\"] = \\\\\\\"positionalAudio\\\\\\\";\\n})(ObjType || (ObjType = {}));\\n\\n// src/engine/nodes/obj/AudioAnalyzer.ts\\nvar AudioAnalyzerParamConfig = class extends NodeParamsConfig {\\n constructor() {\\n super(...arguments);\\n this.url = ParamConfig.STRING(\\\\\\\"\\\\\\\", {\\n fileBrowse: {type: [FileType.AUDIO]}\\n });\\n this.listener = ParamConfig.NODE_PATH(\\\\\\\"\\\\\\\", {\\n nodeSelection: {\\n context: NodeContext.OBJ,\\n types: [ObjType.AUDIO_LISTENER]\\n }\\n });\\n this.fftSize = ParamConfig.INTEGER(128, {\\n range: [1, 1024]\\n });\\n this.output0 = ParamConfig.FLOAT(1);\\n this.output1 = ParamConfig.FLOAT(1);\\n this.play = ParamConfig.BUTTON(null, {\\n callback: (node) => {\\n AudioAnalyzerObjNode.PARAM_CALLBACK_play(node);\\n }\\n });\\n this.pause = ParamConfig.BUTTON(null, {\\n callback: (node) => {\\n AudioAnalyzerObjNode.PARAM_CALLBACK_pause(node);\\n }\\n });\\n }\\n};\\nvar ParamsConfig = new AudioAnalyzerParamConfig();\\nvar AudioAnalyzerObjNode = class extends TypedObjNode {\\n constructor() {\\n super(...arguments);\\n this.paramsConfig = ParamsConfig;\\n this.hierarchyController = new HierarchyController(this);\\n this.flags = new FlagsControllerD(this);\\n }\\n static type() {\\n return \\\\\\\"audioAnalyzer\\\\\\\";\\n }\\n createObject() {\\n const group = new Group();\\n group.matrixAutoUpdate = false;\\n return group;\\n }\\n initializeNode() {\\n this.hierarchyController.initializeNode();\\n }\\n cook() {\\n this.cookController.endCook();\\n }\\n async audioAnalyzer() {\\n return this._audioAnalyzer = this._audioAnalyzer || await this._createAudioAnalyzer();\\n }\\n async _createAudioAnalyzer() {\\n console.log(\\\\\\\"_createAudioAnalyzer\\\\\\\");\\n if (this.p.listener.isDirty()) {\\n await this.p.listener.compute();\\n }\\n const node = this.pv.listener.nodeWithContext(NodeContext.OBJ);\\n if (!node) {\\n return;\\n }\\n console.log(\\\\\\\"_createAudioAnalyzer B\\\\\\\");\\n const listener = node.object;\\n this._audio = new Audio2(listener);\\n if (/(iPad|iPhone|iPod)/g.test(navigator.userAgent)) {\\n const loader2 = new AudioLoader();\\n loader2.load(this.pv.url, (buffer) => {\\n if (this._audio) {\\n this._audio.setBuffer(buffer);\\n this._audio.hasPlaybackControl = true;\\n console.log(\\\\\\\"play A\\\\\\\");\\n }\\n });\\n } else {\\n this._mediaElement = new Audio(this.pv.url);\\n console.log(\\\\\\\"play B\\\\\\\");\\n this._audio.setMediaElementSource(this._mediaElement);\\n this._audio.hasPlaybackControl = true;\\n }\\n const audioAnalyzer = new AudioAnalyser(this._audio, this.pv.fftSize);\\n const loader = new CoreLoaderAudio(this.pv.url, this.scene(), this);\\n this.scene().registerOnBeforeTick(`audioAnalyzer-${this.path()}`, () => {\\n if (this._audioAnalyzer) {\\n this._audioAnalyzer.getFrequencyData();\\n const value = this._audioAnalyzer.data[0];\\n console.log(value, this._audioAnalyzer.data);\\n this.p.output0.set(value);\\n const average = this._audioAnalyzer.getAverageFrequency();\\n this.p.output1.set(average);\\n }\\n });\\n return audioAnalyzer;\\n }\\n isPlaying() {\\n if (this._audio) {\\n return isBooleanTrue(this._audio.isPlaying);\\n }\\n if (this._mediaElement) {\\n return isBooleanTrue(this._mediaElement.isPlaying);\\n }\\n return false;\\n }\\n static PARAM_CALLBACK_play(node) {\\n node.PARAM_CALLBACK_play();\\n }\\n static PARAM_CALLBACK_pause(node) {\\n node.PARAM_CALLBACK_pause();\\n }\\n PARAM_CALLBACK_play() {\\n this.audioAnalyzer();\\n if (!this.isPlaying()) {\\n console.log(\\\\\\\"play !\\\\\\\");\\n this._audio?.play();\\n this._mediaElement?.play();\\n }\\n }\\n PARAM_CALLBACK_pause() {\\n this.audioAnalyzer();\\n if (this.isPlaying()) {\\n console.log(\\\\\\\"-pause-\\\\\\\");\\n this._audio?.pause();\\n this._mediaElement?.pause();\\n }\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/core/geometry/Group.ts\\nvar CoreGroup = class {\\n constructor() {\\n this._objects = [];\\n this._objects_with_geo = [];\\n this.touch();\\n }\\n timestamp() {\\n return this._timestamp;\\n }\\n touch() {\\n const performance2 = Poly.performance.performanceManager();\\n this._timestamp = performance2.now();\\n this.reset();\\n }\\n reset() {\\n this._bounding_box = void 0;\\n this._core_geometries = void 0;\\n this._core_objects = void 0;\\n }\\n clone() {\\n const core_group = new CoreGroup();\\n if (this._objects) {\\n const objects = [];\\n for (let object of this._objects) {\\n objects.push(CoreObject.clone(object));\\n }\\n core_group.setObjects(objects);\\n }\\n return core_group;\\n }\\n setObjects(objects) {\\n this._objects = objects;\\n this._objects_with_geo = objects.filter((obj) => obj.geometry != null);\\n this.touch();\\n }\\n objects() {\\n return this._objects;\\n }\\n objectsWithGeo() {\\n return this._objects_with_geo;\\n }\\n coreObjects() {\\n return this._core_objects = this._core_objects || this._create_core_objects();\\n }\\n _create_core_objects() {\\n if (this._objects) {\\n return this._objects.map((object, i) => new CoreObject(object, i));\\n }\\n return [];\\n }\\n objectsData() {\\n if (this._objects) {\\n return this._objects.map((object) => this._objectData(object));\\n }\\n return [];\\n }\\n _objectData(object) {\\n let points_count = 0;\\n if (object.geometry) {\\n points_count = CoreGeometry.pointsCount(object.geometry);\\n }\\n return {\\n type: objectTypeFromConstructor(object.constructor),\\n name: object.name,\\n children_count: object.children.length,\\n points_count\\n };\\n }\\n geometries() {\\n const list = [];\\n for (let core_object of this.coreObjects()) {\\n const geometry = core_object.object().geometry;\\n if (geometry) {\\n list.push(geometry);\\n }\\n }\\n return list;\\n }\\n coreGeometries() {\\n return this._core_geometries = this._core_geometries || this._createCoreGeometries();\\n }\\n _createCoreGeometries() {\\n const list = [];\\n for (let geometry of this.geometries()) {\\n list.push(new CoreGeometry(geometry));\\n }\\n return list;\\n }\\n static geometryFromObject(object) {\\n if (object.isMesh || object.isLine || object.isPoints) {\\n return object.geometry;\\n }\\n return null;\\n }\\n faces() {\\n const faces = [];\\n for (let object of this.objectsWithGeo()) {\\n if (object.geometry) {\\n const coreGeo = new CoreGeometry(object.geometry);\\n const geoFaces = coreGeo.faces();\\n for (let geoFace of geoFaces) {\\n geoFace.applyMatrix4(object.matrix);\\n faces.push(geoFace);\\n }\\n }\\n }\\n return faces;\\n }\\n points() {\\n return this.coreGeometries().map((g) => g.points()).flat();\\n }\\n pointsCount() {\\n return ArrayUtils.sum(this.coreGeometries().map((g) => g.pointsCount()));\\n }\\n totalPointsCount() {\\n if (this._objects) {\\n let sum = 0;\\n for (let object of this._objects) {\\n object.traverse((object2) => {\\n const geometry = object2.geometry;\\n if (geometry) {\\n sum += CoreGeometry.pointsCount(geometry);\\n }\\n });\\n }\\n return sum;\\n } else {\\n return 0;\\n }\\n }\\n pointsFromGroup(group) {\\n if (group) {\\n const indices = CoreString.indices(group);\\n const points2 = this.points();\\n return ArrayUtils.compact(indices.map((i) => points2[i]));\\n } else {\\n return this.points();\\n }\\n }\\n static _fromObjects(objects) {\\n const core_group = new CoreGroup();\\n core_group.setObjects(objects);\\n return core_group;\\n }\\n objectsFromGroup(group_name) {\\n return this.coreObjectsFromGroup(group_name).map((co) => co.object());\\n }\\n coreObjectsFromGroup(group_name) {\\n group_name = group_name.trim();\\n if (group_name !== \\\\\\\"\\\\\\\") {\\n const index = parseInt(group_name);\\n if (!CoreType.isNaN(index)) {\\n return ArrayUtils.compact([this.coreObjects()[index]]);\\n } else {\\n return this.coreObjects().filter((core_object) => {\\n return CoreString.matchMask(group_name, core_object.name());\\n });\\n }\\n } else {\\n return this.coreObjects();\\n }\\n }\\n boundingBox(forceUpdate = false) {\\n if (forceUpdate) {\\n return this._bounding_box = this._computeBoundingBox();\\n }\\n return this._bounding_box = this._bounding_box || this._computeBoundingBox();\\n }\\n center() {\\n const center = new Vector3();\\n this.boundingBox().getCenter(center);\\n return center;\\n }\\n size() {\\n const size = new Vector3();\\n this.boundingBox().getSize(size);\\n return size;\\n }\\n _computeBoundingBox() {\\n let bbox;\\n if (this._objects) {\\n for (let object of this._objects) {\\n const geometry = object.geometry;\\n if (geometry) {\\n geometry.computeBoundingBox();\\n if (bbox) {\\n bbox.expandByObject(object);\\n } else {\\n if (geometry.boundingBox) {\\n bbox = geometry.boundingBox.clone();\\n }\\n }\\n }\\n }\\n }\\n bbox = bbox || new Box3(new Vector3(-1, -1, -1), new Vector3(1, 1, 1));\\n return bbox;\\n }\\n computeVertexNormals() {\\n for (let object of this.coreObjects()) {\\n object.computeVertexNormals();\\n }\\n }\\n hasAttrib(name) {\\n let first_geometry;\\n if ((first_geometry = this.coreGeometries()[0]) != null) {\\n return first_geometry.hasAttrib(name);\\n } else {\\n return false;\\n }\\n }\\n attribType(name) {\\n const first_core_geometry = this.coreGeometries()[0];\\n if (first_core_geometry != null) {\\n return first_core_geometry.attribType(name);\\n } else {\\n return null;\\n }\\n }\\n objectAttribType(name) {\\n const first_core_object = this.coreObjects()[0];\\n if (first_core_object != null) {\\n return first_core_object.attribType(name);\\n } else {\\n return null;\\n }\\n }\\n renameAttrib(old_name, new_name, attrib_class) {\\n switch (attrib_class) {\\n case CoreConstant.ATTRIB_CLASS.VERTEX:\\n if (this.hasAttrib(old_name)) {\\n if (this._objects) {\\n for (let object of this._objects) {\\n object.traverse((child) => {\\n const geometry = CoreGroup.geometryFromObject(child);\\n if (geometry) {\\n const core_geometry = new CoreGeometry(geometry);\\n core_geometry.renameAttrib(old_name, new_name);\\n }\\n });\\n }\\n }\\n }\\n break;\\n case CoreConstant.ATTRIB_CLASS.OBJECT:\\n if (this.hasAttrib(old_name)) {\\n if (this._objects) {\\n for (let object of this._objects) {\\n object.traverse((child) => {\\n const core_object = new CoreObject(child, 0);\\n core_object.renameAttrib(old_name, new_name);\\n });\\n }\\n }\\n }\\n break;\\n }\\n }\\n attribNames() {\\n let first_geometry;\\n if ((first_geometry = this.coreGeometries()[0]) != null) {\\n return first_geometry.attribNames();\\n } else {\\n return [];\\n }\\n }\\n objectAttribNames() {\\n let first_object;\\n if ((first_object = this.coreObjects()[0]) != null) {\\n return first_object.attribNames();\\n } else {\\n return [];\\n }\\n }\\n attribNamesMatchingMask(masks_string) {\\n const masks = CoreString.attribNames(masks_string);\\n const matching_attrib_names = [];\\n for (let attrib_name of this.attribNames()) {\\n for (let mask of masks) {\\n if (CoreString.matchMask(attrib_name, mask)) {\\n matching_attrib_names.push(attrib_name);\\n } else {\\n const remapped = CoreAttribute.remapName(mask);\\n if (attrib_name == remapped) {\\n matching_attrib_names.push(attrib_name);\\n }\\n }\\n }\\n }\\n return ArrayUtils.uniq(matching_attrib_names);\\n }\\n attribSizes() {\\n let first_geometry;\\n if ((first_geometry = this.coreGeometries()[0]) != null) {\\n return first_geometry.attribSizes();\\n } else {\\n return {};\\n }\\n }\\n objectAttribSizes() {\\n let first_object;\\n if ((first_object = this.coreObjects()[0]) != null) {\\n return first_object.attribSizes();\\n } else {\\n return {};\\n }\\n }\\n attribSize(attrib_name) {\\n let first_geometry;\\n if ((first_geometry = this.coreGeometries()[0]) != null) {\\n return first_geometry.attribSize(attrib_name);\\n } else {\\n return 0;\\n }\\n }\\n addNumericVertexAttrib(name, size, default_value) {\\n if (default_value == null) {\\n default_value = CoreAttribute.default_value(size);\\n }\\n for (let core_geometry of this.coreGeometries()) {\\n core_geometry.addNumericAttrib(name, size, default_value);\\n }\\n }\\n static clone(src_group) {\\n const new_group = new Group();\\n src_group.children.forEach((src_object) => {\\n const new_object = CoreObject.clone(src_object);\\n new_group.add(new_object);\\n });\\n return new_group;\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/_Base.ts\\nvar BaseOperation = class {\\n constructor(_scene, states, _node) {\\n this._scene = _scene;\\n this.states = states;\\n this._node = _node;\\n }\\n static type() {\\n throw \\\\\\\"type to be overriden\\\\\\\";\\n }\\n type() {\\n const c = this.constructor;\\n return c.type();\\n }\\n static context() {\\n console.error(\\\\\\\"operation has no node_context\\\\\\\", this);\\n throw \\\\\\\"context requires override\\\\\\\";\\n }\\n context() {\\n const c = this.constructor;\\n return c.context();\\n }\\n scene() {\\n return this._scene;\\n }\\n cook(input_contents, params) {\\n }\\n};\\nBaseOperation.DEFAULT_PARAMS = {};\\nBaseOperation.INPUT_CLONED_STATE = [];\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/operations/sop/_Base.ts\\nvar BaseSopOperation = class extends BaseOperation {\\n static context() {\\n return NodeContext.SOP;\\n }\\n cook(input_contents, params) {\\n }\\n createCoreGroupFromObjects(objects) {\\n const core_group = new CoreGroup();\\n core_group.setObjects(objects);\\n return core_group;\\n }\\n createCoreGroupFromGeometry(geometry, type = ObjectType.MESH) {\\n const object = BaseSopOperation.createObject(geometry, type);\\n return this.createCoreGroupFromObjects([object]);\\n }\\n createObject(geometry, type, material) {\\n return BaseSopOperation.createObject(geometry, type, material);\\n }\\n static createObject(geometry, type, material) {\\n this.createIndexIfNone(geometry);\\n const object_constructor = OBJECT_CONSTRUCTOR_BY_OBJECT_TYPE[type];\\n material = material || CoreConstant.MATERIALS[type].clone();\\n const object = new object_constructor(geometry, material);\\n object.castShadow = true;\\n object.receiveShadow = true;\\n object.frustumCulled = false;\\n object.matrixAutoUpdate = false;\\n return object;\\n }\\n createIndexIfNone(geometry) {\\n BaseSopOperation.createIndexIfNone(geometry);\\n }\\n static createIndexIfNone(geometry) {\\n CoreGeometryIndexBuilder.createIndexIfNone(geometry);\\n }\\n};\\n\\n// ../../../node_modules/@polygonjs/polygonjs/src/engine/nodes/sop/_Base.ts\\nvar MESSAGE;\\n(function(MESSAGE2) {\\n MESSAGE2[\\\\\\\"FROM_SET_CORE_GROUP\\\\\\\"] = \\\\\\\"from set_core_group\\\\\\\";\\n MESSAGE2[\\\\\\\"FROM_SET_GROUP\\\\\\\"] = \\\\\\\"from set_group\\\\\\\";\\n MESSAGE2[\\\\\\\"FROM_SET_OBJECTS\\\\\\\"] = \\\\\\\"from set_objects\\\\\\\";\\n MESSAGE2[\\\\\\\"FROM_SET_OBJECT\\\\\\\"] = \\\\\\\"from set_object\\\\\\\";\\n MESSAGE2[\\\\\\\"FROM_SET_GEOMETRIES\\\\\\\"] = \\\\\\\"from set_geometries\\\\\\\";\\n MESSAGE2[\\\\\\\"FROM_SET_GEOMETRY\\\\\\\"] = \\\\\\\"from set_geometry\\\\\\\";\\n})(MESSAGE || (MESSAGE = {}));\\nvar INPUT_GEOMETRY_NAME = \\\\\\\"input geometry\\\\\\\";\\nvar DEFAULT_INPUT_NAMES2 = [INPUT_GEOMETRY_NAME, INPUT_GEOMETRY_NAME, INPUT_GEOMETRY_NAME, INPUT_GEOMETRY_NAME];\\nvar TypedSopNode = class extends TypedNode {\\n constructor() {\\n super(...arguments);\\n this.flags = new FlagsControllerDBO(this);\\n }\\n static context() {\\n return NodeContext.SOP;\\n }\\n static displayedInputNames() {\\n return DEFAULT_INPUT_NAMES2;\\n }\\n initializeBaseNode() {\\n this.flags.display.set(false);\\n this.flags.display.onUpdate(() => {\\n if (this.flags.display.active()) {\\n const parent = this.parent();\\n if (parent && parent.displayNodeController) {\\n parent.displayNodeController.setDisplayNode(this);\\n }\\n }\\n });\\n this.io.outputs.setHasOneOutput();\\n }\\n setCoreGroup(core_group) {\\n this._setContainer(core_group, MESSAGE.FROM_SET_CORE_GROUP);\\n }\\n setObject(object) {\\n this._setContainerObjects([object], MESSAGE.FROM_SET_OBJECT);\\n }\\n setObjects(objects) {\\n this._setContainerObjects(objects, MESSAGE.FROM_SET_OBJECTS);\\n }\\n setGeometry(geometry, type = ObjectType.MESH) {\\n const object = this.createObject(geometry, type);\\n this._setContainerObjects([object], MESSAGE.FROM_SET_GEOMETRY);\\n }\\n setGeometries(geometries, type = ObjectType.MESH) {\\n const objects = [];\\n let object;\\n for (let geometry of geometries) {\\n object = this.createObject(geometry, type);\\n objects.push(object);\\n }\\n this._setContainerObjects(objects, MESSAGE.FROM_SET_GEOMETRIES);\\n }\\n _setContainerObjects(objects, message) {\\n const core_group = this.containerController.container().coreContent() || new CoreGroup();\\n core_group.setObjects(objects);\\n core_group.touch();\\n this._setContainer(core_group);\\n }\\n static createObject(geometry, type, material) {\\n return BaseSopOperation.createObject(geometry, type, material);\\n }\\n createObject(geometry, type, material) {\\n return TypedSopNode.createObject(geometry, type, material);\\n }\\n static createIndexIfNone(geometry) {\\n BaseSopOperation.createIndexIfNone(geometry);\\n }\\n _createIndexIfNone(geometry) {\\n TypedSopNode.createIndexIfNone(geometry);\\n }\\n};\\n\\n// src/engine/nodes/sop/AudioAnalyzer.ts\\nvar AudioAnalyzerParamConfig2 = class extends NodeParamsConfig {\\n constructor() {\\n super(...arguments);\\n this.url = ParamConfig.STRING(\\\\\\\"\\\\\\\", {\\n fileBrowse: {type: [FileType.AUDIO]}\\n });\\n this.listener = ParamConfig.NODE_PATH(\\\\\\\"\\\\\\\", {\\n nodeSelection: {\\n context: NodeContext.OBJ,\\n types: [ObjType.AUDIO_LISTENER]\\n }\\n });\\n this.fftSize = ParamConfig.INTEGER(128, {\\n range: [1, 1024]\\n });\\n this.outputMult = ParamConfig.FLOAT(0.1);\\n this.average = ParamConfig.FLOAT(1, {\\n cook: false\\n });\\n this.play = ParamConfig.BUTTON(null, {\\n callback: (node) => {\\n AudioAnalyzerSopNode.PARAM_CALLBACK_play(node);\\n }\\n });\\n this.pause = ParamConfig.BUTTON(null, {\\n callback: (node) => {\\n AudioAnalyzerSopNode.PARAM_CALLBACK_pause(node);\\n }\\n });\\n }\\n};\\nvar ParamsConfig2 = new AudioAnalyzerParamConfig2();\\nvar AudioAnalyzerSopNode = class extends TypedSopNode {\\n constructor() {\\n super(...arguments);\\n this.paramsConfig = ParamsConfig2;\\n }\\n static type() {\\n return \\\\\\\"audioAnalyzer\\\\\\\";\\n }\\n initializeNode() {\\n this.io.inputs.setCount(1);\\n this.addGraphInput(this.scene().timeController.graphNode);\\n this.io.inputs.initInputsClonedState(InputCloneMode.FROM_NODE);\\n }\\n cook(input_contents) {\\n console.log(\\\\\\\"cook\\\\\\\");\\n const core_group = input_contents[0];\\n const objects = core_group.objects();\\n if (this._audioAnalyzer) {\\n const outputMult = this.pv.outputMult;\\n this._audioAnalyzer.getFrequencyData();\\n const data = this._audioAnalyzer.data;\\n const maxCount = Math.min(objects.length, data.length);\\n for (let i = 0; i < maxCount; i++) {\\n const object = objects[i];\\n const dataEntry = data[i] * outputMult;\\n object.position.y = dataEntry;\\n }\\n const average = this._audioAnalyzer.getAverageFrequency();\\n this.p.average.set(average);\\n }\\n this.setCoreGroup(core_group);\\n }\\n async audioAnalyzer() {\\n return this._audioAnalyzer = this._audioAnalyzer || await this._createAudioAnalyzer();\\n }\\n async _createAudioAnalyzer() {\\n console.log(\\\\\\\"_createAudioAnalyzer\\\\\\\");\\n if (this.p.listener.isDirty()) {\\n await this.p.listener.compute();\\n }\\n const node = this.pv.listener.nodeWithContext(NodeContext.OBJ);\\n if (!node) {\\n return;\\n }\\n console.log(\\\\\\\"_createAudioAnalyzer B\\\\\\\");\\n const listener = node.object;\\n this._audio = new Audio2(listener);\\n if (/(iPad|iPhone|iPod)/g.test(navigator.userAgent)) {\\n const loader = new AudioLoader();\\n loader.load(this.pv.url, (buffer) => {\\n if (this._audio) {\\n this._audio.setBuffer(buffer);\\n console.log(\\\\\\\"play A\\\\\\\");\\n }\\n });\\n } else {\\n this._mediaElement = new Audio(this.pv.url);\\n console.log(\\\\\\\"play B\\\\\\\");\\n this._audio.setMediaElementSource(this._mediaElement);\\n }\\n const audioAnalyzer = new AudioAnalyser(this._audio, this.pv.fftSize);\\n return audioAnalyzer;\\n }\\n isPlaying() {\\n if (this._audio) {\\n return isBooleanTrue(this._audio.isPlaying);\\n }\\n if (this._mediaElement) {\\n return isBooleanTrue(this._mediaElement.isPlaying);\\n }\\n return false;\\n }\\n static PARAM_CALLBACK_play(node) {\\n node.PARAM_CALLBACK_play();\\n }\\n static PARAM_CALLBACK_pause(node) {\\n node.PARAM_CALLBACK_pause();\\n }\\n PARAM_CALLBACK_play() {\\n this.audioAnalyzer();\\n if (!this.isPlaying()) {\\n console.log(\\\\\\\"play !\\\\\\\");\\n this._audio?.play();\\n this._mediaElement?.play();\\n }\\n }\\n PARAM_CALLBACK_pause() {\\n if (this.isPlaying()) {\\n this._audio?.pause();\\n this._mediaElement?.pause();\\n }\\n }\\n};\\n\\n// src/polygonjs/PolyConfig.ts\\nfunction configurePolygonjs(poly) {\\n poly.nodesRegister.register(AudioAnalyzerObjNode, \\\\\\\"myNodes\\\\\\\");\\n poly.nodesRegister.register(AudioAnalyzerSopNode, \\\\\\\"myNodes\\\\\\\");\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n configureScene\\n};\\n\"","status":200,"headers":{"content-type":"application/javascript","content-length":"1178636"}},"type":2,"external":true,"timestamp":1723918151157},{"data":{"url":"blob:https://ipfs.arkivo.art/694f8fc5-9607-4522-8949-ba201170809b","body":"\"����\\u0000\\u0018Exif\\u0000\\u0000II*\\u0000\\b\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000��\\u0000\\u0011Ducky\\u0000\\u0001\\u0000\\u0004\\u0000\\u0000\\u0000\\u001e\\u0000\\u0000��\\u0003/http://ns.adobe.com/xap/1.0/\\u0000 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","consoleMessages":[{"text":"Unrecognized Content-Security-Policy directive 'prefetch-src'.","level":"error","timestamp":1723918142775},{"text":"[.WebGL-0x378400fa5500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723918164748},{"text":"[.WebGL-0x378400fa5500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723918164748},{"text":"[.WebGL-0x378400fa5500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723918164748},{"text":"[.WebGL-0x378400fa5500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723918164802}],"screenshotDelay":10000},"timestamp":1723918142323},"created_at":"2024-08-17T18:09:58.710+00:00","updated_at":"2024-08-17T18:09:58.710+00:00"}