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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 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":1723917062737},{"data":{"url":"blob:https://ipfs.arkivo.art/4338a609-7ced-4b4d-93e4-95b04985b333","host":"","path":"https://ipfs.arkivo.art/4338a609-7ced-4b4d-93e4-95b04985b333","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":1723917062738},{"data":{"url":"blob:https://ipfs.arkivo.art/4338a609-7ced-4b4d-93e4-95b04985b333","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":1723917069321}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized Content-Security-Policy directive 'prefetch-src'.","level":"error","timestamp":1723917061933},{"text":"[.WebGL-0x2b9c0136aa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723917073557},{"text":"[.WebGL-0x2b9c0136aa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723917073575},{"text":"[.WebGL-0x2b9c0136aa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723917078928},{"text":"[.WebGL-0x2b9c0136aa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723917083473}],"screenshotDelay":10000},"timestamp":1723917061532},"created_at":"2024-08-17T17:51:43.198+00:00","updated_at":"2024-08-17T17:51:43.198+00:00"}