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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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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 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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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l{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((t,e)=>{console.log(e,t,t.graphPredecessors(),t.graphSuccessors())}))}setScene(t){this._scene=t}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(t){const e=[];for(let n of t){const t=this.nodeFromId(n);t&&e.push(t)}return e}nodeFromId(t){return this._nodes_by_id.get(t)}hasNode(t){return 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 e=this.allSuccessorIds(t);return this.nodesFromIds(e)}_createConnection(t,e){let n=this._successors.get(t);if(n||(n=new Set,this._successors.set(t,n)),n.has(e))return;n.add(e);let i=this._predecessors.get(e);i||(i=new Set,this._predecessors.set(e,i)),i.add(t)}_remove_connection(t,e){let n=this._successors.get(t);n&&(n.delete(e),0==n.size&&this._successors.delete(t));let i=this._predecessors.get(e);i&&(i.delete(t),0==i.size&&this._predecessors.delete(e))}allNextIds(t,e){const n=new Map,i=[];let s=this[e](t.graphNodeId());for(;s.length>0;){const t=[];for(let n of s)for(let i of this[e](n))t.push(i);for(let t of s)n.set(t,!0);for(let e of t)s.push(e);s=t}return n.forEach(((t,e)=>{i.push(e)})),i}_hasPredecessor(t,e){const n=this.predecessorIds(t);if(n){if(n.includes(e))return!0;for(let t of n)return this._hasPredecessor(t,e)}return!1}}class 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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u.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return u.difference(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static toSet(t){const e=new Set;for(let n of t)e.add(n);return e}static isEqual(t,e){if(t.length!=e.length)return!1;const n=t.length;for(let i=0;i{s[r]=t,r++})),m.isString(s[0])?s.sort():s.sort(((t,e)=>t-e));const o=new Array(t.length);r=0;for(let t of s){const e=n.get(t);if(e)for(let t of e)o[r]=t,r++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),s=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),h.push(a/f),h.push(1-r/g),E+=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}}F.prototype.isShaderMaterial=!0;var D=n(6),B=n(14),z=\\\\\\\"\\\\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\\\\\\\",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 D.a(1118481)},shininess:{value:30}}]),vertexShader:U.meshphong_vert,fragmentShader:U.meshphong_frag},standard:{uniforms:R([V.common,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.roughnessmap,V.metalnessmap,V.fog,V.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag},toon:{uniforms:R([V.common,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.gradientmap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshtoon_vert,fragmentShader:U.meshtoon_frag},matcap:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,V.fog,{matcap:{value:null}}]),vertexShader:U.meshmatcap_vert,fragmentShader:U.meshmatcap_frag},points:{uniforms:R([V.points,V.fog]),vertexShader:U.points_vert,fragmentShader:U.points_frag},dashed:{uniforms:R([V.common,V.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:U.linedashed_vert,fragmentShader:U.linedashed_frag},depth:{uniforms:R([V.common,V.displacementmap]),vertexShader:U.depth_vert,fragmentShader:U.depth_frag},normal:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,{opacity:{value:1}}]),vertexShader:U.meshnormal_vert,fragmentShader:U.meshnormal_frag},sprite:{uniforms:R([V.sprite,V.fog]),vertexShader:U.sprite_vert,fragmentShader:U.sprite_frag},background:{uniforms:{uvTransform:{value:new G.a},t2D:{value:null}},vertexShader:U.background_vert,fragmentShader:U.background_frag},cube:{uniforms:R([V.envmap,{opacity:{value:1}}]),vertexShader:U.cube_vert,fragmentShader:U.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:U.equirect_vert,fragmentShader:U.equirect_frag},distanceRGBA:{uniforms:R([V.common,V.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:U.distanceRGBA_vert,fragmentShader:U.distanceRGBA_frag},shadow:{uniforms:R([V.lights,V.fog,{color:{value:new D.a(0)},opacity:{value:1}}]),vertexShader:U.shadow_vert,fragmentShader:U.shadow_frag}};function j(t,e,n,i,s){const r=new D.a(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===w.q)?(void 0===a&&(a=new B.a(new N(1,1,1),new F({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:P(H.cube.uniforms),vertexShader:H.cube.vertexShader,fragmentShader:H.cube.fragmentShader,side:w.i,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 B.a(new L(2,2),new F({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:P(H.background.uniforms),vertexShader:H.background.vertexShader,fragmentShader:H.background.fragmentShader,side:w.H,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 W(t,e,n,i){const s=t.getParameter(t.MAX_VERTEX_ATTRIBS),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(t.FRAGMENT_SHADER,t.HIGH_FLOAT).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(t.VERTEX_SHADER,t.MEDIUM_FLOAT).precision>0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.MEDIUM_FLOAT).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(t.MAX_TEXTURE_IMAGE_UNITS),u=t.getParameter(t.MAX_VERTEX_TEXTURE_IMAGE_UNITS),d=t.getParameter(t.MAX_TEXTURE_SIZE),p=t.getParameter(t.MAX_CUBE_MAP_TEXTURE_SIZE),_=t.getParameter(t.MAX_VERTEX_ATTRIBS),m=t.getParameter(t.MAX_VERTEX_UNIFORM_VECTORS),f=t.getParameter(t.MAX_VARYING_VECTORS),g=t.getParameter(t.MAX_FRAGMENT_UNIFORM_VECTORS),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(t.MAX_SAMPLES):0}}H.physical={uniforms:R([H.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new d.a(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new D.a(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new d.a},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new D.a(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new D.a(1,1,1)},specularTintMap:{value:null}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag};var Y=n(34);function $(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new Y.a,a=new G.a,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.length\\\\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 N(5,5,5),s=new F({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:P(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});s.uniforms.tEquirect.value=e;const r=new B.a(i,s),o=e.minFilter;e.minFilter===w.Y&&(e.minFilter=w.V);return new nt(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)}}function rt(t){let e=new WeakMap;function n(t,e){return e===w.D?t.mapping=w.o:e===w.E&&(t.mapping=w.p),t}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;if(r===w.D||r===w.E){if(e.has(s)){return n(e.get(s).texture,s.mapping)}{const r=s.image;if(r&&r.height>0){const o=t.getRenderTarget(),a=new st(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}}}st.prototype.isWebGLCubeRenderTarget=!0;var ot=n(37);class at extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}at.prototype.isRawShaderMaterial=!0;var lt=n(29);const ct=Math.pow(2,8),ht=[.125,.215,.35,.446,.526,.582],ut=5+ht.length,dt=20,pt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},_t=new ot.a,{_lodPlanes:mt,_sizeLods:ft,_sigmas:gt}=Mt(),vt=new D.a;let yt=null;const xt=(1+Math.sqrt(5))/2,bt=1/xt,wt=[new p.a(1,1,1),new p.a(-1,1,1),new p.a(1,1,-1),new p.a(-1,1,-1),new p.a(0,xt,bt),new p.a(0,xt,-bt),new p.a(bt,0,xt),new p.a(-bt,0,xt),new p.a(xt,bt,0),new p.a(-xt,bt,0)];class 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 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:w.ub,depthTest:!1,depthWrite:!1})}(dt),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){yt=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=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 Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let A,M;return s.deleteShader(b),s.deleteShader(T),this.getUniforms=function(){return void 0===A&&(A=new Xe(s,m)),A},this.getAttributes=function(){return void 0===M&&(M=function(t,e){const n={},i=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES);for(let s=0;s0,N=r.clearcoat>0;return{isWebGL2:l,shaderID:T,shaderName:r.type,vertexShader:M,fragmentShader:E,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!==S?f(S.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===w.q||b.mapping===w.r),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:r.normalMapType===w.zb,tangentSpaceNormalMap:r.normalMapType===w.Uc,clearcoat:N,clearcoatMap:N&&!!r.clearcoatMap,clearcoatRoughnessMap:N&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:N&&!!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:C,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&&A>0,maxBones:A,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:w.vb,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:r.side===w.z,flipSided:r.side===w.i,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 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 d.visible=i.visible,d.wireframe=i.wireframe,h===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.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 b(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&&a===w.gd)&&(!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&&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 i=null;const s=new _.a(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(e){e?k(t.DEPTH_TEST):U(t.DEPTH_TEST)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case w.tb:t.depthFunc(t.NEVER);break;case w.g:t.depthFunc(t.ALWAYS);break;case w.S:t.depthFunc(t.LESS);break;case w.T:t.depthFunc(t.LEQUAL);break;case w.C:t.depthFunc(t.EQUAL);break;case w.L:t.depthFunc(t.GEQUAL);break;case w.K:t.depthFunc(t.GREATER);break;case w.yb:t.depthFunc(t.NOTEQUAL);break;default:t.depthFunc(t.LEQUAL)}else t.depthFunc(t.LEQUAL);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(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 R=t.getParameter(t.SCISSOR_BOX),I=t.getParameter(t.VIEWPORT),F=(new _.a).fromArray(R),D=(new _.a).fromArray(I);function B(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(e,t.TEXTURE_MAG_FILTER,t.NEAREST);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?On.g: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 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extends J.a{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,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new tt.a;A.layers.enable(1),A.viewport=new _.a;const E=new tt.a;E.layers.enable(2),E.viewport=new _.a;const S=[A,E],C=new In;C.layers.enable(1),C.layers.enable(2);let N=null,L=null;function O(t){const e=T.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function P(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),h=null,f=null,g=null,v=null,d=null,u=null,c=null,s=null,z.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function R(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,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}register(t,e,n){const i=t.context(),s=t.type().toLowerCase();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;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&&null!=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=n.type().toLowerCase(),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[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Yn{constructor(t){this.poly=t,this._operation_register=new Map}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=t.type().toLowerCase();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 n.get(e.toLowerCase())}}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){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,li.playerMode()),li.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}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 $n,this.modulesRegister=new Jn,this.assemblersRegister=new Zn,this.pluginsRegister=new Qn(this),this.camerasRegister=new Kn(this),this.blobs=new ei,this.assetUrls=new ni,this.selfContainedScenesLoader=new ri,this.performance=new oi,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new ai;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new qn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const li=ai._instance_();class ci{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=li.performance.performanceManager(),i=n.now();e();const s=n.now()-i;console.log(`${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}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(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.resolve_missing_references(),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.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();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}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());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.warn(`[${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.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(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)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}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,ss,rs;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Qi||(Qi={}));class os{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)os.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 as{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 ls{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class cs{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 ls.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 hs(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 ps{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}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){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={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ss||(ss={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.PLAYER=\\\\\\\"playerControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rs||(rs={}));const _s=[rs.DEVICE_ORIENTATION,rs.MAP,rs.ORBIT,rs.FIRST_PERSON,rs.MOBILE_JOYSTICK];class ms{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 fs=n(69);class gs 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}}gs.prototype.isScene=!0;var vs=n(49),ys=n(52),xs=n(42),bs=n(55),ws=n(62),Ts=n(24),As=n(35);const Ms=new p.a,Es=new p.a;class Ss 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){Ms.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ms);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ms.setFromMatrixPosition(t.matrixWorld),Es.setFromMatrixPosition(this.matrixWorld);const n=Ms.distanceTo(Es)/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*sr}static radians_to_degrees(t){return t/sr}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)))}}rr.Easing=ir,rr.fract=t=>t-Math.floor(t),rr._vec={x:0,y:136574},rr._triangle_mid=new p.a,rr._triangle_mid_to_corner=new p.a;class or{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.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 Qs(this._core_geometry,e[n+0]),new Qs(this._core_geometry,e[n+1]),new Qs(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){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 Ks.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){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}random_position(t){let e=[rr.randFloat(t),rr.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]))}attrib_value_at_position(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 interpolated_value(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 ar{from_points(t){t=this._filter_points(t);const e=new S.a,n=new _r(e),i=t[0];if(null!=i){const s=i.geometry(),r=i.core_geometry(),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 _r(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=hr(t),r=new _r(s);return Object.keys(i).forEach((t=>{const e=i[t];r.setIndexedAttributeValues(t,e)})),s&&delete s.userData.mergedUserData,s}}class _r{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 _r.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=qs.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=qs.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=qs.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Bs.STRING:Bs.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return _r.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=os.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)os.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=qs.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 or(this,t)))}}var mr;_r._mesh_builder=new class extends ar{_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 gr{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=fr}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 vr=\\\\\\\"name\\\\\\\";class yr extends Xs{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 _r(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=os.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){yr.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=qs.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 ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==vr)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==vr?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return yr.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return yr.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(vr)}humanType(){return Hs.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)?Bs.STRING:Bs.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:qs.attribSizeFromValue(e)}clone(){return yr.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return yr.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=_r.clone(t);const e=r.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(r.material){r.material=s.material,gr.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 _r(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(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class br{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=xr[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 ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const wr=li.performance.performanceManager();class Tr{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=wr.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=wr.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=wr.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=wr.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=wr.now())}recordCookEnd(){this.active()&&(this._data.cookTime=wr.now()-this._cook_start,this._cooksCount+=1)}}class Ar{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new Tr(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${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 Er,Sr;!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\\\\\\\"}(Er||(Er={})),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\\\\\\\"}(Sr||(Sr={}));const Cr=\\\\\\\"dependentOnFoundNode\\\\\\\",Nr=\\\\\\\"visibleIf\\\\\\\";var Lr,Or;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Lr||(Lr={})),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\\\\\\\"}(Or||(Or={}));class Pr{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!(Cr 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(Sr.VISIBLE_UPDATED)}}}class Rr{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 Ir{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 Fr{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Dr{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(Sr.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Br{constructor(t){this.param=t,this.timeDependent=new Fr(this.param),this.error=new Dr(this.param)}}class zr 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.update_from_method_dependency_name_change()}}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 zr(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 kr=[];class Ur extends Mi{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Pr(this),this._emit_controller=new Rr(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Pr(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rr(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ir(this)}get states(){return this._states=this._states||new Br(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof zr&&t.dispose();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 Er.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(Sr.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 kr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Gr=n(94),Vr=n.n(Gr);Vr.a.addUnaryOp(\\\\\\\"@\\\\\\\");Vr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Hr{constructor(){}parse_expression(t){try{this.reset(),this.node=Vr()(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=Hr.string_value_elements(t),n=[];for(let t=0;t{e.push(Wr.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(Wr.assign_item_size_line(t)),e.push(Wr.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=Wr.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return Wr.var_attribute_size(t)}var_array(t){return Wr.var_array(t)}}const qr={math_random:\\\\\\\"random\\\\\\\"},Xr=Object.keys(ir),Yr={};[\\\\\\\"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=>{Yr[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),Object.keys(qr).forEach((t=>{const e=qr[t];Yr[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Yr[t]=`Core.Math.${t}`})),Xr.forEach((t=>{Yr[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Yr[t]=`Core.String.${t}`}));const $r={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},Jr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`}));const Zr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Qr extends jr{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new Wr,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.reset(),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 ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_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}`)}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=[]}function_body(){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${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\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 = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rr,String:os};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=$r[n];if(i)return i(e);const s=`${e.join(\\\\\\\", \\\\\\\")}`,r=Yr[n];if(r)return`${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)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,s=t.object,r=t.property;e=s.name,n=r.name;break}}if(e){if(e=qs.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Zr[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=Jr[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: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void li.warn(e)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const t=r.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Kr extends jr{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${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 to{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 eo{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Hr,this.function_generator=new Qr(this.param),this.dependencies_controller=new to(this.param)}parse_expression(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 Hr,this.reset(),this.param.type()==Er.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Kr(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 no{constructor(t){this.param=t}dispose(){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.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new eo(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class io extends Ur{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${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.compute_expression();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(Sr.VALUE_UPDATED),this.removeDirtyState()}}class so extends io{static type(){return Er.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)&&os.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(os.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=so.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends Ur{constructor(){super(...arguments),this._components_contructor=so}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\\\\\\\"}(oo||(oo={}));oo.NONE,oo.GAMMA_TO_LINEAR,oo.LINEAR_TO_GAMMA,oo.SRGB_TO_LINEAR,oo.LINEAR_TO_SRGB;class ao{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 lo=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class co extends io{static type(){return Er.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)&&os.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(os.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=co.convert(t);return e?this.options.ensureInRange(e):e}}class ho{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 uo extends Ur{constructor(){super(...arguments),this.decomposed_path=new ho}}var po;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(po||(po={}));class _o extends uo{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 Er.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(Sr.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()?po.PARAM:po.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(s==po.PARAM?n=bi.findParam(this.node,t,this.decomposed_path):e=bi.findNode(this.node,t,this.decomposed_path));const r=s==po.PARAM?this._found_param:this._found_node,o=s==po.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(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==po.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())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var mo,fo=n(33),go=n(70);class vo{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new vo;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new vo(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(mo||(mo={}));class yo{constructor(t=mo.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(On.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(vo.from_json(n));return new yo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new yo;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.is_equal(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.is_equal_json(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(!vo.are_equal_json(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.from_json(n):this._points.push(vo.from_json(n)),e+=1}}}const xo=1024;class bo extends Ur{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new fo.a(this._texture_data,xo,1,w.ic)}static type(){return Er.RAMP}defaultValueSerialized(){return this._default_value instanceof yo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof yo?t.clone():yo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof yo?this._raw_input.toJSON():yo.from_json(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 yo?e instanceof yo?t.is_equal(e):t.is_equal_json(e):e instanceof yo?e.is_equal_json(t):yo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof yo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof yo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=yo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Sr.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/xo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new go.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){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 bo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}bo.DEFAULT_VALUE=new yo(mo.LINEAR,[new vo(0,0),new vo(1,1)]),bo.DEFAULT_VALUE_JSON=bo.DEFAULT_VALUE.toJSON();class wo extends Ur{static type(){return Er.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 no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.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.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Hr.string_value_elements(t)}}const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Mo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Er.BOOLEAN]:class extends io{static type(){return Er.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(os.isBoolean(t))return os.toBoolean(t);if(os.isNumber(t)){return parseFloat(t)>=1}}return null}},[Er.BUTTON]:class extends Ur{static type(){return Er.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()}},[Er.COLOR]:class extends ro{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 Er.COLOR}componentNames(){return lo}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!=oo.NONE){switch(t){case oo.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case oo.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case oo.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case oo.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ls.unreachable(t)}this._value_serialized_dirty=!0}},[Er.FLOAT]:so,[Er.FOLDER]:class extends Ur{static type(){return Er.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}},[Er.INTEGER]:co,[Er.OPERATOR_PATH]:_o,[Er.PARAM_PATH]:class extends uo{static type(){return Er.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(Sr.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.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(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)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.NODE_PATH]:class extends uo{static type(){return Er.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(Sr.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.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(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()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.RAMP]:bo,[Er.STRING]:wo,[Er.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Er.VECTOR2}componentNames(){return To}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}},[Er.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Er.VECTOR3}componentNames(){return Ao}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}},[Er.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Er.VECTOR4}componentNames(){return Mo}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 So{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 Er.STRING:return this._handle_string_param(t);case Er.OPERATOR_PATH:return this._handle_operator_path_param(t);case Er.NODE_PATH:return this._handle_node_path_param(t);case Er.PARAM_PATH:return this._handle_param_path_param(t);case Er.FLOAT:case Er.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 Co{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 So}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.notify_params_updated(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 ${n}`)}set_float(t,e){this.set_with_type(t,e,Er.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Er.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Er.FLOAT)}get_operator_path(t){return this.param_with_type(t,Er.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Er.BOOLEAN)}float(t){return this.value_with_type(t,Er.FLOAT)}integer(t){return this.value_with_type(t,Er.INTEGER)}string(t){return this.value_with_type(t,Er.STRING)}vector2(t){return this.value_with_type(t,Er.VECTOR2)}vector3(t){return this.value_with_type(t,Er.VECTOR3)}color(t){return this.value_with_type(t,Er.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(li.warn(`tried to access param '${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=Eo[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 No{constructor(){}}class Lo{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=Lo._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)}}Lo._next_id=0;class Oo{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 ls.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 Lo(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 Oo(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 Ro{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 Io{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 Fo{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 Do{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 Bo;!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\\\\\\\"}(Bo||(Bo={}));const zo=[Bo.BOOL,Bo.INT,Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4],ko={[Bo.BOOL]:Er.BOOLEAN,[Bo.INT]:Er.INTEGER,[Bo.FLOAT]:Er.FLOAT,[Bo.VEC2]:Er.VECTOR2,[Bo.VEC3]:Er.VECTOR3,[Bo.VEC4]:Er.VECTOR4,[Bo.SAMPLER_2D]:Er.RAMP,[Bo.SSS_MODEL]:Er.STRING},Uo={[Er.BOOLEAN]:Bo.BOOL,[Er.COLOR]:Bo.VEC3,[Er.INTEGER]:Bo.INT,[Er.FLOAT]:Bo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:Bo.VEC2,[Er.VECTOR3]:Bo.VEC3,[Er.VECTOR4]:Bo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Go={[Bo.BOOL]:!1,[Bo.INT]:0,[Bo.FLOAT]:0,[Bo.VEC2]:[0,0],[Bo.VEC3]:[0,0,0],[Bo.VEC4]:[0,0,0,0],[Bo.SAMPLER_2D]:bo.DEFAULT_VALUE_JSON,[Bo.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Vo={[Bo.BOOL]:1,[Bo.INT]:1,[Bo.FLOAT]:1,[Bo.VEC2]:2,[Bo.VEC3]:3,[Bo.VEC4]:4,[Bo.SAMPLER_2D]:1,[Bo.SSS_MODEL]:1};class Ho extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Go[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return ko[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 jo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(jo||(jo={}));const Wo=[jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4],qo={[jo.BOOL]:Er.BOOLEAN,[jo.INT]:Er.INTEGER,[jo.FLOAT]:Er.FLOAT,[jo.VEC2]:Er.VECTOR2,[jo.VEC3]:Er.VECTOR3,[jo.VEC4]:Er.VECTOR4},Xo={[Er.BOOLEAN]:jo.BOOL,[Er.COLOR]:jo.VEC3,[Er.INTEGER]:jo.INT,[Er.FLOAT]:jo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:jo.VEC2,[Er.VECTOR3]:jo.VEC3,[Er.VECTOR4]:jo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Yo={[jo.BOOL]:!1,[jo.INT]:0,[jo.FLOAT]:0,[jo.VEC2]:[0,0],[jo.VEC3]:[0,0,0],[jo.VEC4]:[0,0,0,0]};jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4;class $o extends Do{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Yo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return qo[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 Jo;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}(Jo||(Jo={}));class Zo extends Do{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 Er.FLOAT}are_types_matched(t,e){return e==Jo.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 Qo={[ts.ANIM]:void 0,[ts.COP]:void 0,[ts.EVENT]:Jo.BASE,[ts.GL]:Bo.FLOAT,[ts.JS]:jo.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 Ko(t,e,n){switch(t){case ts.EVENT:return new Zo(e,n);case ts.GL:return new Ho(e,n);case ts.JS:return new $o(e,n);default:return}}class ta{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 Uo;case ts.JS:return Xo;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=Ko(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 ea{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 ta(this.node,this._context)}default_connection_type(){return Qo[this._context]}create_connection_point(t,e){return Ko(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.creation_completed&&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 na{constructor(t){this.node=t,this._connections=new Io(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Po(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ea(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 Fo(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 ia{constructor(){}}class sa extends Mi{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new br(this),this.pv=new No,this.p=new ia,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 ds(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 ps(this)}get serializer(){return this._serializer=this._serializer||new Mr(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ar(this)}get io(){return this._io=this._io||new na(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 Co(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()}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 ra extends sa{static context(){return ts.MANAGER}}class oa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class aa{static BUTTON(t,e){return new oa(Er.BUTTON,t,e)}static BOOLEAN(t,e){return new oa(Er.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new oa(Er.COLOR,t,e)}static FLOAT(t,e){return new oa(Er.FLOAT,t,e)}static FOLDER(t=null,e){return new oa(Er.FOLDER,t,e)}static INTEGER(t,e){return new oa(Er.INTEGER,t,e)}static RAMP(t=bo.DEFAULT_VALUE,e){return new oa(Er.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new oa(Er.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new oa(Er.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new oa(Er.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new oa(Er.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new oa(Er.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new oa(Er.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new oa(Er.PARAM_PATH,t,e)}}class la{}class ca{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)}/${e}`;if(os.matchMask(s,t))return i;const r=this.findObjectByMaskInObject(t,i,s);if(r)return r}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let s of e.children){const e=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;os.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 ha={computeOnDirty:!1,callback:t=>{da.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=aa.BOOLEAN(1,ha)}}}ua(la);class da{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 pa;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(pa||(pa={}));const _a=[pa.NONE,pa.COLOR,pa.TEXTURE],ma={computeOnDirty:!1,callback:t=>{ga.update(t)}};function fa(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=aa.INTEGER(_a.indexOf(pa.NONE),{menu:{entries:_a.map(((t,e)=>({name:t,value:e})))},...ma}),this.bgColor=aa.COLOR([0,0,0],{visibleIf:{backgroundMode:_a.indexOf(pa.COLOR)},...ma}),this.bgTexture=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:_a.indexOf(pa.TEXTURE)},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...ma})}}}fa(la);class ga{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==_a.indexOf(pa.NONE))t.background=null;else if(e.backgroundMode==_a.indexOf(pa.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 va={computeOnDirty:!1,callback:t=>{xa.update(t)}};function ya(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=aa.BOOLEAN(0,va),this.environment=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...va})}}}ya(la);class xa{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 ba{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new ba(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}ba.prototype.isFog=!0;class wa{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new wa(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}wa.prototype.isFogExp2=!0;const Ta={computeOnDirty:!1,callback:t=>{Sa.update(t)}};var Aa;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Aa||(Aa={}));const Ma=[Aa.LINEAR,Aa.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0,Ta),this.fogType=aa.INTEGER(Ma.indexOf(Aa.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Ma.map(((t,e)=>({name:t,value:e})))},...Ta}),this.fogColor=aa.COLOR([1,1,1],{visibleIf:{useFog:1},...Ta}),this.fogNear=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogFar=aa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogDensity=aa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.EXPONENTIAL)},...Ta})}}}Ea(la);class Sa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Ma.indexOf(Aa.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 ba(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new wa(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Ca={computeOnDirty:!1,callback:t=>{La.update(t)}};function Na(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=aa.BOOLEAN(0,Ca),this.overrideMaterial=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,...Ca})}}}Na(la);class La{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 Oa extends(Na(ya(Ea(fa(ua(la)))))){}const Pa=new Oa;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Pa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.sceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(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 gs;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 Ia{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(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();os.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 Fa{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new Mr(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 Da;!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\\\\\\\"}(Da||(Da={}));const Ba=[Da.auxclick,Da.click,Da.contextmenu,Da.dblclick,Da.mousedown,Da.mouseenter,Da.mouseleave,Da.mousemove,Da.mouseover,Da.mouseout,Da.mouseup,Da.pointerlockchange,Da.pointerlockerror,Da.select,Da.wheel];class za extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}}class ka extends sa{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 Ua;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(Ua||(Ua={}));const Ga=[Ua.CANVAS,Ua.DOCUMENT];class Va{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==Ua.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 Ha={visibleIf:{active:1},callback:t=>{Wa.PARAM_CALLBACK_updateRegister(t)}};class ja extends ka{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:Ga[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class Wa extends ja{acceptedEventTypes(){return[]}}const qa=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Xa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=aa.BOOLEAN(0,Ha),this.click=aa.BOOLEAN(0,Ha),this.contextmenu=aa.BOOLEAN(0,Ha),this.dblclick=aa.BOOLEAN(0,Ha),this.mousedown=aa.BOOLEAN(1,Ha),this.mouseenter=aa.BOOLEAN(0,Ha),this.mouseleave=aa.BOOLEAN(0,Ha),this.mousemove=aa.BOOLEAN(1,Ha),this.mouseover=aa.BOOLEAN(0,Ha),this.mouseout=aa.BOOLEAN(0,Ha),this.mouseup=aa.BOOLEAN(1,Ha),this.pointerlockchange=aa.BOOLEAN(0,Ha),this.pointerlockerror=aa.BOOLEAN(0,Ha),this.select=aa.BOOLEAN(0,Ha),this.wheel=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Xa extends ja{constructor(){super(...arguments),this.paramsConfig=qa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ba.map((t=>new Zo(t,Jo.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 Ya;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Ya||(Ya={}));const $a=[Ya.pointerdown,Ya.pointermove,Ya.pointerup];class Ja extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}}const Za=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=aa.BOOLEAN(1,Ha),this.pointermove=aa.BOOLEAN(0,Ha),this.pointerup=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Qa extends ja{constructor(){super(...arguments),this.paramsConfig=Za}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints($a.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ka,tl;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Ka||(Ka={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(tl||(tl={}));const el=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{nl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.sceneLoaded=aa.BOOLEAN(1,Ha),this.play=aa.BOOLEAN(1,Ha),this.pause=aa.BOOLEAN(1,Ha),this.tick=aa.BOOLEAN(1,{separatorAfter:!0,...Ha}),this.treachedTime=aa.BOOLEAN(0,{callback:t=>{nl.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=aa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=aa.INTEGER(1,{range:[0,100]}),this.setFrame=aa.BUTTON(null,{callback:t=>{nl.PARAM_CALLBACK_setFrame(t)}})}};class nl extends ja{constructor(){super(...arguments),this.paramsConfig=el}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Ka.SET_FRAME,Jo.BASE,this._onSetFrame.bind(this)),new Zo(_i.PLAY,Jo.BASE,this._play.bind(this)),new Zo(_i.PAUSE,Jo.BASE,this._pause.bind(this))]);const t=mi.map((t=>new Zo(t,Jo.BASE)));t.push(new Zo(tl.TIME_REACHED,Jo.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(tl.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 il;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(il||(il={}));const sl=[il.keydown,il.keypress,il.keyup];class rl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}}const ol=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{al.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=aa.BOOLEAN(1,Ha),this.keypress=aa.BOOLEAN(0,Ha),this.keyup=aa.BOOLEAN(0,Ha),this.keyCodes=aa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Ha),this.ctrlKey=aa.BOOLEAN(0,Ha),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class al extends ja{constructor(){super(...arguments),this.paramsConfig=ol}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(sl.map((t=>new Zo(t,Jo.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 ll;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(ll||(ll={}));const cl=[ll.resize];class hl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}}const ul=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{dl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.resize=aa.BOOLEAN(1,Ha)}};class dl extends ja{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(cl.map((t=>new Zo(t,Jo.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 pl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(pl||(pl={}));const _l=[pl.dragover];class ml extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}}var fl;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(fl||(fl={}));const gl=[fl.touchstart,fl.touchmove,fl.touchend];class vl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}}const yl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{xl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=aa.BOOLEAN(1,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class xl extends ja{constructor(){super(...arguments),this.paramsConfig=yl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(_l.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const bl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{wl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=aa.BOOLEAN(1,Ha),this.touchmove=aa.BOOLEAN(0,Ha),this.touchend=aa.BOOLEAN(0,Ha)}};class wl extends ja{constructor(){super(...arguments),this.paramsConfig=bl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(gl.map((t=>new Zo(t,Jo.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 Tl{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 al.type():return this.keyboardEventsController;case Xa.type():return this.mouseEventsController;case xl.type():return this.dragEventsController;case Qa.type():return this.pointerEventsController;case nl.type():return this.sceneEventsController;case wl.type():return this.touchEventsController;case dl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(za)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(ml)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(Ja)}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(hl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(vl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Al{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}set_reference_from_param(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)}set_named_nodes_from_param(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)}reset_reference_from_param(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([Er.OPERATOR_PATH,Er.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 _o){const i=t.found_node(),s=t.found_param();return i&&e.set(i.graphNodeId(),i),void(s&&n.push(s))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Ml;!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\\\\\\\"}(Ml||(Ml={}));class El{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,Ml.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Ml.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),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)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(El.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 El.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 Cl{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 Nl{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 Ll{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 Ol{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 Pl{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 Ia(this),this._objects_controller=new ca(this),this._references_controller=new Al(this),this._time_controller=new El(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=os.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 Ol}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 Tl(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 Cl(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 Fa(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 Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(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)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Rl{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 Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{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){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{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}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of r)Dl.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(Il);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=Rl.non_spare_params_data_value(n.params),r=Dl.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 Dl.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 Bl{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}=Dl.child_names_by_optimized_state(e),r=[];for(let n of s){const i=e[n],s=i.type.toLowerCase(),o=Rl.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 ${s}`,e);const i=os.camelCase(s);try{const t=this._node.createNode(i,o);t&&(t.setName(n),r.push(t))}catch(e){const a=`${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 '${a}'`;t.report.addWarning(n),li.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==ts.SOP){const t=Object.keys(e);let s;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const t=r.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(s)){const t=`node '${`${this._node.path()}/${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=Wl.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 zl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class kl{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 Bl(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&&Wl.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 zl)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 Ul extends Rl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Rl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Rl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends kl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new kl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof wo?new Vl(t):t instanceof bo?new Hl(t):new Rl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Pl;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||El.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.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 Yl{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 ah.a&&this._target_value instanceof ah.a){const t={value:0},e=o,n=(new ah.a).copy(o),i=this._target_value;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._target_value[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._target_value instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,ah.a),{target_property:n,to_target:i,property_names: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._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):li.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Er.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Er.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Er.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Er.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Er.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);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._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);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._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamVector2(t,e,n){if(!(this._target_value instanceof d.a))return;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._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;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._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;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._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),i.w=this.withOp(t.value.w,this._target_value.w,o),this._startTimeline(e,n,i,s)}withOp(t,e,n){switch(n){case N_.SET:return e;case N_.ADD:return t+e;case N_.SUBSTRACT:return t-e}ls.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||oh.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 dh(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function ph(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var _h,mh,fh,gh,vh,yh,xh,bh,wh,Th,Ah,Mh,Eh,Sh={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Ch={duration:.5,overwrite:!1,delay:0},Nh=1e8,Lh=1e-8,Oh=2*Math.PI,Ph=Oh/4,Rh=0,Ih=Math.sqrt,Fh=Math.cos,Dh=Math.sin,Bh=function(t){return\\\\\\\"string\\\\\\\"==typeof t},zh=function(t){return\\\\\\\"function\\\\\\\"==typeof 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N_,L_=pp.registerPlugin(C_)||pp;L_.core.Tween;!function(t){t.SET=\\\\\\\"set\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\"}(N_||(N_={}));const O_=[N_.SET,N_.ADD,N_.SUBSTRACT];class P_{constructor(){this._timeline_builders=[],this._duration=1,this._operation=N_.SET,this._delay=0,this._debug=!1}setDebug(t){this._debug=t}_printDebug(t){this._debug&&console.log(t)}addTimelineBuilder(t){this._timeline_builders.push(t),t.setParent(this)}timelineBuilders(){return this._timeline_builders}setParent(t){this._parent=t}parent(){return this._parent}setTarget(t){this._target=t;for(let e of this._timeline_builders)e.setTarget(t)}target(){return this._target}setDuration(t){if(t>=0){this._duration=t;for(let e of this._timeline_builders)e.setDuration(t)}}duration(){return this._duration}setEasing(t){this._easing=t;for(let e of this._timeline_builders)e.setEasing(t)}easing(){return this._easing}setOperation(t){this._operation=t;for(let e of this._timeline_builders)e.setOperation(t)}operation(){return this._operation}setRepeatParams(t){this._repeat_params=t;for(let e of this._timeline_builders)e.setRepeatParams(t)}repeatParams(){return this._repeat_params}setDelay(t){this._delay=t;for(let e of this._timeline_builders)e.setDelay(t)}delay(){return this._delay}setPosition(t){this._position=t}position(){return this._position}setUpdateCallback(t){this._update_callback=t}updateCallback(){return this._update_callback}clone(){const t=new P_;if(t.setDuration(this._duration),t.setOperation(this._operation),t.setDelay(this._delay),this._target&&t.setTarget(this._target.clone()),this._easing&&t.setEasing(this._easing),this._delay&&t.setDelay(this._delay),this._update_callback&&t.setUpdateCallback(this._update_callback.clone()),this._repeat_params&&t.setRepeatParams({count:this._repeat_params.count,delay:this._repeat_params.delay,yoyo:this._repeat_params.yoyo}),this._property){const e=this._property.name();e&&t.setPropertyName(e);const n=this._property.targetValue();null!=n&&t.setPropertyValue(n)}this._position&&t.setPosition(this._position.clone());for(let e of this._timeline_builders){const n=e.clone();t.addTimelineBuilder(n)}return t}setPropertyName(t){this.property().setName(t)}property(){return this._property=this._property||new uh}propertyName(){return this.property().name()}setPropertyValue(t){this.property().setTargetValue(t)}populate(t){var e;this._printDebug([\\\\\\\"populate\\\\\\\",this,t]);for(let n of this._timeline_builders){const i=L_.timeline();n.setDebug(this._debug),n.populate(i);const s=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,s)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const R_=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends ih{constructor(){super(...arguments),this.paramsConfig=R_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new P_;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new P_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const B_=new class extends la{constructor(){super(...arguments),this.duration=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class z_ extends ih{constructor(){super(...arguments),this.paramsConfig=B_}static type(){return\\\\\\\"duration\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.duration])}))}))}cook(t){const e=t[0]||new P_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const k_=new class extends la{constructor(){super(...arguments),this.name=aa.INTEGER(lh.indexOf(oh.POWER4),{menu:{entries:lh.map(((t,e)=>({name:t,value:e})))}}),this.inOut=aa.INTEGER(hh.indexOf(ch.OUT),{menu:{entries:hh.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends ih{constructor(){super(...arguments),this.paramsConfig=k_}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.inOut],(()=>this.easing_full_name()))}))}))}easing_full_name(){const t=lh[this.pv.name];if(t==oh.NONE)return t;return`${t}.${hh[this.pv.inOut]}`}cook(t){const e=t[0]||new P_,n=this.easing_full_name();e.setEasing(n),this.setTimelineBuilder(e)}}var G_;!function(t){t.RELATIVE=\\\\\\\"relative\\\\\\\",t.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(G_||(G_={}));const V_=[G_.RELATIVE,G_.ABSOLUTE];var H_;!function(t){t.START=\\\\\\\"start\\\\\\\",t.END=\\\\\\\"end\\\\\\\"}(H_||(H_={}));const j_=[H_.START,H_.END];class W_{constructor(){this._mode=G_.RELATIVE,this._relativeTo=H_.END,this._offset=0}clone(){const t=new W_;return t.setMode(this._mode),t.setRelativeTo(this._relativeTo),t.setOffset(this._offset),t}setMode(t){this._mode=t}mode(){return this._mode}setRelativeTo(t){this._relativeTo=t}relativeTo(){return this._relativeTo}setOffset(t){this._offset=t}offset(){return this._offset}toParameter(){switch(this._mode){case G_.RELATIVE:return this._relative_position_param();case G_.ABSOLUTE:return this._absolutePositionParam()}ls.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ls.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0,{range:[-1,1]}),this.overridePositions=aa.BOOLEAN(0),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends ih{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new P_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ls.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends la{constructor(){super(...arguments),this.play=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=aa.BOOLEAN(0)}};class Z_ extends ih{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0]||new P_;this.setTimelineBuilder(e)}async play(){return new Promise((async t=>{const e=await this.compute();e&&(this._timeline_builder=e.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline({onComplete:t}),this.pv.debug&&console.log(`play from '${this.path()}'`),this._timeline_builder.setDebug(this.pv.debug),this._timeline_builder.populate(this._timeline)))}))}async pause(){this._timeline&&this._timeline.pause()}static PARAM_CALLBACK_play(t){t.play()}static PARAM_CALLBACK_pause(t){t.pause()}}const Q_=new class extends la{constructor(){super(...arguments),this.operation=aa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends ih{constructor(){super(...arguments),this.paramsConfig=Q_}static type(){return\\\\\\\"operation\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>O_[this.pv.operation]))}))}))}cook(t){const e=t[0]||new P_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=aa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0)}};class em extends ih{constructor(){super(...arguments),this.paramsConfig=tm}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(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new P_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"position\\\\\\\")}};class im extends ih{constructor(){super(...arguments),this.paramsConfig=nm}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 P_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var sm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(sm||(sm={}));const rm=[sm.CUSTOM,sm.FROM_SCENE_GRAPH,sm.FROM_NODE],om=rm.indexOf(sm.CUSTOM),am=rm.indexOf(sm.FROM_SCENE_GRAPH),lm=rm.indexOf(sm.FROM_NODE);const cm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(om,{menu:{entries:rm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=aa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=aa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{hm.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=aa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=aa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=aa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=aa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=aa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=aa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class hm extends ih{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new P_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(rm.indexOf(t))}async _prepare_timeline_builder(t){const e=rm[this.pv.mode];switch(e){case sm.CUSTOM:return this._prepare_timebuilder_custom(t);case sm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case sm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ls.unreachable(e)}_prepare_timebuilder_custom(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)}_prepare_timebuilder_from_scene_graph(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 ah.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(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_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const um=new class extends la{constructor(){super(...arguments),this.unlimited=aa.BOOLEAN(0),this.count=aa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=aa.FLOAT(0),this.yoyo=aa.BOOLEAN(0)}};class dm extends ih{constructor(){super(...arguments),this.paramsConfig=um}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 P_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends ih{constructor(){super(...arguments),this.paramsConfig=pm}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 mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(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 fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=aa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends ih{constructor(){super(...arguments),this.paramsConfig=bm}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=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ls.unreachable(t)}))}))}))}cook(t){const e=t[0]||new P_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ls.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ls.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new P_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends sa{static context(){return ts.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Mm extends Am{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 Em extends Am{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 Sm extends Am{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 Cm extends Am{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 Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){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 Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Pm(),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=Pm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Pm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Rm={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 Im{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 Fm=new ot.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class Bm{constructor(t){this._mesh=new B.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class zm extends Im{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 Bm(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 km extends Im{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 Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{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===Rm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===zm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new zm(Rm),this.clock=new Om}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]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends la{constructor(){super(...arguments),this.prependRenderPass=aa.BOOLEAN(1),this.useRenderTarget=aa.BOOLEAN(1),this.tmagFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=aa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=aa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{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 Gm(e,t)}else n=new Gm(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 Hm(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 Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 Km extends Am{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 tf=n(44);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var sf=new Uint16Array(32),rf=0;rf<32;rf++)sf[rf]=28898;const of=new fo.a(sf,32,1,w.gb,w.M);class af extends sa{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ki(this)}static context(){return ts.COP}static displayedInputNames(){return nf}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(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class hf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${os.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=os.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),m.isNumber(i)&&(i=os.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return os.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:os.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return os.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:os.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const uf=/\\\\/+/g;class df extends sa{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_${this.path(this.material_node).replace(uf,\\\\\\\"_\\\\\\\")}_${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 hf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return hf.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 pf=new class extends la{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const ff=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=aa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=aa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,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 ss.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())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[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 gf.INPUT_NAME}get output_name(){return gf.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(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.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(gf.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())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{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 yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{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 vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Bo.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Mf extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Ef={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,s;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(!r)return;const o=t.glVarName(e),a=r.type(),l=new Mf(t,a,o);n.addDefinitions(t,[l]);const c=null===(s=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!c)return;const h=c.shader_config(n.current_shader_name);if(!h)return;const u=h.dependencies(),d=[],p=`${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 Ef[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var s,r;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Mf(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const u=new Map;u.set(xf.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 Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.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 Cf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const Nf=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,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((()=>[Cf[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 Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Mf(this,e,n),s=`${n} = ${hf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[s],xf.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(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{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(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Pf{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 Rf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Rf||(Rf={}));class If{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=Pf.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 Xf{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 qf).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 Jf extends Xf{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,tg.handle_dependencies(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:()=>{$f.callback(n,i)}})}}}class Zf{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 Qf=[256,256];const Kf=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2(Qf),this.useCameraRenderer=aa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(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 gs,this._texture_camera=new tf.a,this._children_controller_context=ts.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.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 Sf;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=Of.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),tg.handle_dependencies(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 handle_dependencies(t,e,n){const i=t.scene(),s=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(s,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(s))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(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 If(Rf.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:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{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}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],sg=[{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}],rg=[{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 og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=aa.BOOLEAN(0,{...lg}),this.encoding=aa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=aa.BOOLEAN(0,{...cg}),this.mapping=aa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=aa.BOOLEAN(0,{...hg}),this.wrapS=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...hg}),this.wrapT=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...hg}),this.tminFilter=aa.BOOLEAN(0,{...ug}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...ug}),this.tmagFilter=aa.BOOLEAN(0,{...ug}),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...ug}),this.tanisotropy=aa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=aa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=aa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=aa.BOOLEAN(0,{...pg}),this.flipY=aa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=aa.BOOLEAN(0,{..._g}),this.offset=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=aa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=aa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=aa.BOOLEAN(0,{...yg}),this.tformat=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=aa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=aa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(la)){}new bg;class wg{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=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.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 Ff(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 eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const s=Object.keys(i)[0];i[s]==e[n]&&t.push(`${n}: ${s}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=aa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=aa.BUTTON(null,{cook:!1,callback:t=>{Eg.PARAM_CALLBACK_update(t)}})}}}(la))){}const Mg=new Ag;class Eg extends af{constructor(){super(...arguments),this.paramsConfig=Mg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${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 Tg(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 Sg=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=aa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}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 Ig extends af{constructor(){super(...arguments),this.paramsConfig=Rg}static type(){return Lg.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;Pg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const Bg=new class extends la{constructor(){super(...arguments),this.useCameraRenderer=aa.BOOLEAN(1),this.mode=aa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class zg extends af{constructor(){super(...arguments),this.paramsConfig=Bg}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 Ff(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 If(Rf.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){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class kg 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)}}kg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(28);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=li.assetUrls.remapedUrl(t);if(e)return e;const n=li.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}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 jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await li.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return li.blobs.blobUrl(e)||t}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)}}jg.loadingManager=Hg;const Wg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\",\\\\\\\"ogg\\\\\\\"];var qg;!function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(qg||(qg={}));const Xg=[qg.JPEG,qg.JPG,qg.PNG,qg.EXR,qg.BASIS,qg.HDR];function Yg(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");return e[e.length-1]}class $g extends jg{constructor(t,e,n,i,s){super(t,i,n),this._param=e,this._node=n,this._scene=i,this._forceVideo=!1,this._forceImage=!1,this._forceVideo=(null==s?void 0:s.forceVideo)||this._forceVideo,this._forceImage=(null==s?void 0:s.forceImage)||this._forceImage}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=bi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),s=await this._urlToLoad();if(this._forceVideo||Wg.includes(i)){e(await this._loadVideo(s))}else if(this._forceImage||Xg.includes(i))try{e(await this._loadImage(s,t))}catch(t){n()}}))}_loadImage(t,e){return new Promise((async(n,i)=>{const s=this.extension();this.loader_for_ext(s,e).then((async e=>{e?($g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed(),e.load(t,(e=>{$g.decrementInProgressLoadsCount();const i=$g._onTextureLoadedCallback;i&&i(t,e),n(e)}),void 0,(t=>{$g.decrementInProgressLoadsCount(),li.warn(\\\\\\\"error\\\\\\\",t),i()}))):i()}))}))}_loadVideo(t){return new Promise((async(e,n)=>{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const n=new kg(i);$g.decrementInProgressLoadsCount();const s=$g._onTextureLoadedCallback;s&&s(t,n),e(n)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=jg.extension(t);let o=$g.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||$g._default_video_source_type(t),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(s);let a=t;a=\\\\\\\"mp4\\\\\\\"==r?$g.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):$g.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=$g.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||$g._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static module_names(t){switch(t){case qg.EXR:return[Hn.EXRLoader];case qg.HDR:return[Hn.RGBELoader];case qg.BASIS:return[Hn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case qg.EXR:return await this._exr_loader(e);case qg.HDR:return await this._hdr_loader(e);case qg.BASIS:return await $g._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await li.modulesRegister.module(Hn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await li.modulesRegister.module(Hn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await li.modulesRegister.module(Hn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.BASISPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const r=await li.renderersController.waitForRenderer();return r?n.detectSupport(r):li.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}static _default_video_source_type(t){return`video/${jg.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():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.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)}))}}$g.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,$g.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,$g.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},$g.MAX_CONCURRENT_LOADS_COUNT=$g._init_max_concurrent_loads_count(),$g.CONCURRENT_LOADS_DELAY=$g._init_concurrent_loads_delay(),$g.in_progress_loads_count=0,$g._queue=[];var Jg=n(114);class Zg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{Kg.PARAM_CALLBACK_reload(t)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{Kg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=aa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Kg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(la))){}const Qg=new Zg;class Kg extends af{constructor(){super(...arguments),this.paramsConfig=Qg,this.textureParamsController=new wg(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=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(\\\\\\\"gif\\\\\\\"!=Yg(this.pv.url).toLowerCase())this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const t=await fetch(this.pv.url),e=await t.arrayBuffer(),n=await Object(Jg.parseGIF)(e),i=!0;this._parsedFrames=await Object(Jg.decompressFrames)(n,i);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),$g.decrementInProgressLoadsCount(),this._gifCanvasElement){const t=new Tg(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 ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}function tv(t){return class extends t{constructor(){super(...arguments),this.checkFileType=aa.BOOLEAN(!0)}}}class ev extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{iv.PARAM_CALLBACK_reload(t,e)}})}}}(la)))){}const nv=new ev;class iv extends af{constructor(){super(...arguments),this.paramsConfig=nv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.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,!Xg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.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.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new $g(t,n,this,this.scene(),{forceImage:!this.pv.checkFileType});try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var sv=n(32);const rv=.005;class ov{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new gs,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:rv,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 ah.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.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 Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:rv}},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:rv}};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 av=new class extends la{constructor(){super(...arguments),this.update=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=aa.BOOLEAN(1),this.lightMapRes=aa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=aa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=aa.FLOAT(rv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=aa.BOOLEAN(1),this.blurAmount=aa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=aa.FLOAT(1,{range:[0,10]}),this.objectsMask=aa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=aa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class lv extends af{constructor(){super(...arguments),this.paramsConfig=av,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 ov(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 If(Rf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(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 sv.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 sv.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 cv=new la;class hv extends af{constructor(){super(...arguments),this.paramsConfig=cv}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)}}const uv=[is.ORTHOGRAPHIC,is.PERSPECTIVE];class dv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:uv}}),this.resolution=aa.VECTOR2([1024,1024]),this.useCameraRenderer=aa.BOOLEAN(1),this.render=aa.BUTTON(null,{callback:t=>{_v.PARAM_CALLBACK_render(t)}})}}}(la))){}const pv=new dv;class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv,this.textureParamsController=new wg(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&&uv.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 Ff(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 If(Rf.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:Zf.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 mv=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class fv extends af{constructor(){super(...arguments),this.paramsConfig=mv}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 ${t}`);this.cookController.endCook()}}class gv extends(xg(la)){}const vv=new gv;class yv extends af{constructor(){super(...arguments),this.paramsConfig=vv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class xv extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_VIDEO]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{wv.PARAM_CALLBACK_reload(t,e)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=aa.FLOAT(0,{cook:!1}),this.setVideoTime=aa.BUTTON(null,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_time(t)}})}}}(la)))){}const bv=new xv;class wv extends af{constructor(){super(...arguments),this.paramsConfig=bv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.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,!Wg.includes(Yg(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&&wg.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 $g(t,n,this,this.scene(),{forceVideo:!this.pv.checkFileType});try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class Tv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=aa.VECTOR2([1024,1024])}}}(la))){}const Av=new Tv;class Mv extends af{constructor(){super(...arguments),this.paramsConfig=Av,this.textureParamsController=new wg(this)}static type(){return Lg.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 kg(this._video),n=t[0];if(n&&wg.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 Ev extends sa{static context(){return ts.COP}cook(){this.cookController.endCook()}}class Sv extends Ev{}class Cv extends Sv{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 Nv extends Sv{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 Lv extends Sv{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 Ov extends Sv{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 Pv extends Ev{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 Rv extends Sv{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 Iv,Fv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Iv||(Iv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Fv||(Fv={}));const Dv=new class extends la{constructor(){super(...arguments),this.animation=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.ANIM},dependentOnFoundNode:!1}),this.play=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_pause(t)}})}};class Bv extends ka{constructor(){super(...arguments),this.paramsConfig=Dv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Iv.START,Jo.BASE,this._play.bind(this)),new Zo(Iv.STOP,Jo.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Fv.START,Jo.BASE),new Zo(Fv.COMPLETE,Jo.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._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Fv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Fv.COMPLETE,t)}}const zv=\\\\\\\"event\\\\\\\";const kv=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(1),this.inputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Uv extends ka{constructor(){super(...arguments),this.paramsConfig=kv}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((()=>zv)),this.io.connection_points.set_expected_output_types_function((()=>[Jo.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill(Jo.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(zv,t)}}const Gv=new class extends la{constructor(){super(...arguments),this.blocking=aa.BOOLEAN(1)}};class Vv extends ka{constructor(){super(...arguments),this.paramsConfig=Gv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Vv.OUTPUT,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Vv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Hv;Vv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Hv||(Hv={}));const jv=new class extends la{constructor(){super(...arguments),this.dispatch=aa.BUTTON(null,{callback:t=>{Wv.PARAM_CALLBACK_execute(t)}})}};class Wv extends ka{constructor(){super(...arguments),this.paramsConfig=jv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hv.OUT,Jo.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Hv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class qv extends ka{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const s=await this.createControlsInstance(t,i),r=this._controls_by_viewer.get(e);r&&r.dispose(),this._controls_by_viewer.set(e,s);const o=li.performance.performanceManager().now();return s.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setupControls(s),s}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Xv=n(27);const Yv=new p.a(0,0,1),$v=new Xv.a,Jv=new ah.a,Zv=new ah.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Qv={type:\\\\\\\"change\\\\\\\"};class Kv 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 ah.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){$v.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler($v),t.multiply(Zv),t.multiply(Jv.setFromAxisAngle(Yv,-s))}(e.object.quaternion,i,s,r,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Qv))}},this.dispose=function(){e.disconnect()},this.connect()}}const ty=new class extends la{constructor(){super(...arguments),this.enabled=aa.BOOLEAN(1)}};class ey extends qv{constructor(){super(...arguments),this.paramsConfig=ty,this._controls_by_element_id=new Map}static type(){return rs.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async createControlsInstance(t,e){const n=new Kv(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 ny{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 iy={type:\\\\\\\"change\\\\\\\"},sy={type:\\\\\\\"start\\\\\\\"},ry={type:\\\\\\\"end\\\\\\\"};class oy 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(iy),n.update(),s=i.NONE},this.update=function(){const e=new p.a,u=(new ah.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=u.clone().invert(),_=new p.a,m=new ah.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(iy),_.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 ny,a=new ny;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($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),s=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),s=i.TOUCH_PAN;break;default:s=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&B(),n.enablePan&&D(),s=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&B(),n.enableRotate&&F(),s=i.TOUCH_DOLLY_ROTATE;break;default:s=i.NONE}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),s=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),s=i.PAN}else{if(!1===n.enableRotate)return;R(t),s=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;R(t),s=i.ROTATE}else{if(!1===n.enablePan)return;I(t),s=i.PAN}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(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(ry),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(sy),function(t){t.deltaY<0?P(M()):t.deltaY>0&&O(M()),n.update()}(t),n.dispatchEvent(ry))}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{py.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=aa.BOOLEAN(0),this.keysMode=aa.INTEGER(uy.indexOf(hy.PAN),{visibleIf:{enableKeys:1},menu:{entries:uy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=aa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.PAN)}}),this.keysRotateSpeedVertical=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}}),this.keysRotateSpeedHorizontal=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}})}};class py extends qv{constructor(){super(...arguments),this.paramsConfig=dy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rs.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(ly,Jo.BASE),new Zo(cy,Jo.BASE),new Zo(\\\\\\\"end\\\\\\\",Jo.BASE)])}async createControlsInstance(t,e){const n=new oy(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(ly,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(cy,{})})),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=uy[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 _y extends py{static type(){return rs.MAP}async create_controls_instance(t,e){const n=new ay(t,e);return this._bind_listeners_to_controls_instance(n),n}}const my=new class extends la{constructor(){super(...arguments),this.delay=aa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class fy extends ka{constructor(){super(...arguments),this.paramsConfig=my}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(\\\\\\\"out\\\\\\\",Jo.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const gy={type:\\\\\\\"change\\\\\\\"},vy={type:\\\\\\\"lock\\\\\\\"},yy={type:\\\\\\\"unlock\\\\\\\"},xy=Math.PI/2,by=new p.a,wy=new ny;class Ty 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 Xv.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(xy-this.maxPolarAngle,Math.min(xy-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this._computeAzimuthalAngle(),this.dispatchEvent(gy)}}_computeAzimuthalAngle(){this.camera.updateMatrixWorld(),by.set(0,0,1),this.camera.localToWorld(by),by.sub(this.camera.position),wy.setFromVector3(by),this._azimuthalAngle=wy.theta}onPointerlockChange(){this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(vy),this.isLocked=!0):(this.dispatchEvent(yy),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 Ay=n(16);const My=new p.a,Ey=new p.a;class Sy{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){My.subVectors(t,this.start),Ey.subVectors(this.end,this.start);const n=Ey.dot(Ey);let i=Ey.dot(My)/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 Cy=new p.a;function Ny(t,e,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),l=Math.PI/4;Cy.copy(e),Cy[i]=0,Cy.normalize();const c=.5*o/(o+a),h=1-Cy.angleTo(t)/l;if(1===Math.sign(Cy[n]))return h*c;return a/(o+a)+c+c*(1-h)}class Ly 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=Oy({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=Oy(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=Oy({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),ky.copy(this.startRotation).multiplyScalar(On.a),this.object.rotation.setFromVector3(ky)}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(Dy),intersectsTriangle:t=>{const e=Iy,n=Fy,s=t.closestPointToSegment(zy,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}}function Gy(t){t.preventDefault()}class Vy{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),Gy(t);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!0),Gy(t);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!0),Gy(t);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!0),Gy(t);break;case\\\\\\\"Space\\\\\\\":this.player.jump(),Gy(t);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!0),Gy(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),Gy(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)}}const Hy=\\\\\\\"lock\\\\\\\",jy=\\\\\\\"change\\\\\\\",Wy=\\\\\\\"unlock\\\\\\\";function qy(){return{cook:!1,callback:t=>{Yy.PARAM_CALLBACK_updatePlayerParams(t)}}}const Xy=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.lock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_lockControls(t)}}),this.unlock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_unlockControls(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...qy()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...qy()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...qy()}),this.gravity=aa.VECTOR3([0,-30,0],{...qy()}),this.translateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.rotateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.jumpAllowed=aa.BOOLEAN(!0,{...qy()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...qy()}),this.runAllowed=aa.BOOLEAN(!0,{...qy()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...qy()}),this.startRotation=aa.VECTOR3([0,0,0],{...qy()}),this.reset=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]})}};class Yy extends qv{constructor(){super(...arguments),this.paramsConfig=Xy,this._controls_by_element_id=new Map}static type(){return rs.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Hy,Jo.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hy,Jo.BASE),new Zo(jy,Jo.BASE),new Zo(Wy,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new Ty(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())}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._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(Hy,(()=>{this.dispatchEventToOutput(Hy,{})})),t.addEventListener(jy,(()=>{this.dispatchEventToOutput(jy,{})})),t.addEventListener(Wy,(()=>{this.dispatchEventToOutput(Wy,{})}))}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(){var t,e;const n=this._firstControls();n&&(n.unlock(),null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),null===(e=this._player)||void 0===e||e.stop())}lockControls(){const t=this._firstControls();t&&(this._player&&(this._corePlayerKeyEvents=this._corePlayerKeyEvents||new Vy(this._player),this._corePlayerKeyEvents.addEvents()),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 $y,Jy;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}($y||($y={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(Jy||(Jy={}));const Zy=new class extends la{constructor(){super(...arguments),this.maxCount=aa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=aa.BUTTON(null,{callback:t=>{Qy.PARAM_CALLBACK_reset(t)}})}};class Qy extends ka{constructor(){super(...arguments),this.paramsConfig=Zy,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo($y.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this)),new Zo($y.RESET,Jo.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Jy.OUT,Jo.BASE),new Zo(Jy.LAST,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class nx{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}}const ix=100,sx=301,rx=302,ox=303,ax=304,lx=306,cx=307,hx=1e3,ux=1001,dx=1002,px=1003,_x=1004,mx=1005,fx=1006,gx=1007,vx=1008,yx=1009,xx=1012,bx=1014,wx=1015,Tx=1016,Ax=1020,Mx=1022,Ex=1023,Sx=1026,Cx=1027,Nx=2300,Lx=2301,Ox=2302,Px=2400,Rx=2401,Ix=2402,Fx=2500,Dx=3e3,Bx=3001,zx=3007,kx=3002,Ux=7680,Gx=35044,Vx=35048,Hx=\\\\\\\"300 es\\\\\\\";class jx{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]+Yx[t>>16&255]+Yx[t>>24&255]+\\\\\\\"-\\\\\\\"+Yx[255&e]+Yx[e>>8&255]+\\\\\\\"-\\\\\\\"+Yx[e>>16&15|64]+Yx[e>>24&255]+\\\\\\\"-\\\\\\\"+Yx[63&n|128]+Yx[n>>8&255]+\\\\\\\"-\\\\\\\"+Yx[n>>16&255]+Yx[n>>24&255]+Yx[255&i]+Yx[i>>8&255]+Yx[i>>16&255]+Yx[i>>24&255]).toUpperCase()}function Zx(t,e,n){return Math.max(e,Math.min(n,t))}function Qx(t,e){return(t%e+e)%e}function Kx(t,e,n){return(1-n)*t+n*e}function tb(t){return 0==(t&t-1)&&0!==t}function eb(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function nb(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var ib=Object.freeze({__proto__:null,DEG2RAD:qx,RAD2DEG:Xx,generateUUID:Jx,clamp:Zx,euclideanModulo:Qx,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:Kx,damp:function(t,e,n,i){return Kx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(Qx(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&&(Wx=t%2147483647),Wx=16807*Wx%2147483647,(Wx-1)/2147483646},degToRad:function(t){return t*qx},radToDeg:function(t){return t*Xx},isPowerOfTwo:tb,ceilPowerOfTwo:eb,floorPowerOfTwo:nb,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 sb{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}}sb.prototype.isVector2=!0;class rb{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 ob(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}rb.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function ab(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let lb;class cb{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===lb&&(lb=ab(\\\\\\\"canvas\\\\\\\")),lb.width=t.width,lb.height=t.height;const n=lb.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=lb}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 hb=0;class ub extends jx{constructor(t=ub.DEFAULT_IMAGE,e=ub.DEFAULT_MAPPING,n=1001,i=1001,s=1006,r=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:hb++}),this.uuid=Jx(),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 sb(0,0),this.repeat=new sb(1,1),this.center=new sb(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new rb,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=Jx()),!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 hx:t.x=t.x-Math.floor(t.x);break;case ux:t.x=t.x<0?0:1;break;case dx: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 hx:t.y=t.y-Math.floor(t.y);break;case ux:t.y=t.y<0?0:1;break;case dx: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 db(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?cb.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.\\\\\\\"),{})}ub.DEFAULT_IMAGE=void 0,ub.DEFAULT_MAPPING=300,ub.prototype.isTexture=!0;class pb{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(Zx(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(){}}fb.prototype.isQuaternion=!0;class gb{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(yb.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(yb.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 vb.copy(this).projectOnVector(t),this.sub(vb)}reflect(t){return this.sub(vb.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(Zx(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}}gb.prototype.isVector3=!0;const vb=new gb,yb=new fb;class xb{constructor(t=new gb(1/0,1/0,1/0),e=new gb(-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,wb),wb.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(Lb),Ob.subVectors(this.max,Lb),Ab.subVectors(t.a,Lb),Mb.subVectors(t.b,Lb),Eb.subVectors(t.c,Lb),Sb.subVectors(Mb,Ab),Cb.subVectors(Eb,Mb),Nb.subVectors(Ab,Eb);let e=[0,-Sb.z,Sb.y,0,-Cb.z,Cb.y,0,-Nb.z,Nb.y,Sb.z,0,-Sb.x,Cb.z,0,-Cb.x,Nb.z,0,-Nb.x,-Sb.y,Sb.x,0,-Cb.y,Cb.x,0,-Nb.y,Nb.x,0];return!!Ib(e,Ab,Mb,Eb,Ob)&&(e=[1,0,0,0,1,0,0,0,1],!!Ib(e,Ab,Mb,Eb,Ob)&&(Pb.crossVectors(Sb,Cb),e=[Pb.x,Pb.y,Pb.z],Ib(e,Ab,Mb,Eb,Ob)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return wb.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(wb).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()||(bb[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),bb[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),bb[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),bb[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),bb[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),bb[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),bb[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),bb[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(bb)),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)}}xb.prototype.isBox3=!0;const bb=[new gb,new gb,new gb,new gb,new gb,new gb,new gb,new gb],wb=new gb,Tb=new xb,Ab=new gb,Mb=new gb,Eb=new gb,Sb=new gb,Cb=new gb,Nb=new gb,Lb=new gb,Ob=new gb,Pb=new gb,Rb=new gb;function Ib(t,e,n,i,s){for(let r=0,o=t.length-3;r<=o;r+=3){Rb.fromArray(t,r);const o=s.x*Math.abs(Rb.x)+s.y*Math.abs(Rb.y)+s.z*Math.abs(Rb.z),a=e.dot(Rb),l=n.dot(Rb),c=i.dot(Rb);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Fb=new xb,Db=new gb,Bb=new gb,zb=new gb;class kb{constructor(t=new gb,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):Fb.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){zb.subVectors(t,this.center);const e=zb.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(zb.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Bb.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Db.copy(t.center).add(Bb)),this.expandByPoint(Db.copy(t.center).sub(Bb)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Ub=new gb,Gb=new gb,Vb=new gb,Hb=new gb,jb=new gb,Wb=new gb,qb=new gb;class Xb{constructor(t=new gb,e=new gb(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,Ub)),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=Ub.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Ub.copy(this.direction).multiplyScalar(e).add(this.origin),Ub.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Gb.copy(t).add(e).multiplyScalar(.5),Vb.copy(e).sub(t).normalize(),Hb.copy(this.origin).sub(Gb);const s=.5*t.distanceTo(e),r=-this.direction.dot(Vb),o=Hb.dot(this.direction),a=-Hb.dot(Vb),l=Hb.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(Vb).multiplyScalar(u).add(Gb),d}intersectSphere(t,e){Ub.subVectors(t.center,this.origin);const n=Ub.dot(this.direction),i=Ub.dot(Ub)-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,Ub)}intersectTriangle(t,e,n,i,s){jb.subVectors(e,t),Wb.subVectors(n,t),qb.crossVectors(jb,Wb);let r,o=this.direction.dot(qb);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}Hb.subVectors(this.origin,t);const a=r*this.direction.dot(Wb.crossVectors(Hb,Wb));if(a<0)return null;const l=r*this.direction.dot(jb.cross(Hb));if(l<0)return null;if(a+l>o)return null;const c=-r*Hb.dot(qb);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 Yb{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 Yb).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/$b.setFromMatrixColumn(t,0).length(),s=1/$b.setFromMatrixColumn(t,1).length(),r=1/$b.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(Zb,t,Qb)}lookAt(t,e,n){const i=this.elements;return ew.subVectors(t,e),0===ew.lengthSq()&&(ew.z=1),ew.normalize(),Kb.crossVectors(n,ew),0===Kb.lengthSq()&&(1===Math.abs(n.z)?ew.x+=1e-4:ew.z+=1e-4,ew.normalize(),Kb.crossVectors(n,ew)),Kb.normalize(),tw.crossVectors(ew,Kb),i[0]=Kb.x,i[4]=tw.x,i[8]=ew.x,i[1]=Kb.y,i[5]=tw.y,i[9]=ew.y,i[2]=Kb.z,i[6]=tw.z,i[10]=ew.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=$b.set(i[0],i[1],i[2]).length();const r=$b.set(i[4],i[5],i[6]).length(),o=$b.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],Jb.copy(this);const a=1/s,l=1/r,c=1/o;return Jb.elements[0]*=a,Jb.elements[1]*=a,Jb.elements[2]*=a,Jb.elements[4]*=l,Jb.elements[5]*=l,Jb.elements[6]*=l,Jb.elements[8]*=c,Jb.elements[9]*=c,Jb.elements[10]*=c,e.setFromRotationMatrix(Jb),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}}Yb.prototype.isMatrix4=!0;const $b=new gb,Jb=new Yb,Zb=new gb(0,0,0),Qb=new gb(1,1,1),Kb=new gb,tw=new gb,ew=new gb,nw=new Yb,iw=new fb;class sw{constructor(t=0,e=0,n=0,i=sw.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(Zx(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(-Zx(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(Zx(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(-Zx(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(Zx(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(-Zx(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 nw.makeRotationFromQuaternion(t),this.setFromRotationMatrix(nw,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return iw.setFromEuler(this),this.setFromQuaternion(iw,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 gb(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}sw.prototype.isEuler=!0,sw.DefaultOrder=\\\\\\\"XYZ\\\\\\\",sw.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class rw{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){xw.subVectors(i,e),bw.subVectors(n,e),ww.subVectors(t,e);const r=xw.dot(xw),o=xw.dot(bw),a=xw.dot(ww),l=bw.dot(bw),c=bw.dot(ww),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,Tw),Tw.x>=0&&Tw.y>=0&&Tw.x+Tw.y<=1}static getUV(t,e,n,i,s,r,o,a){return this.getBarycoord(t,e,n,i,Tw),a.set(0,0),a.addScaledVector(s,Tw.x),a.addScaledVector(r,Tw.y),a.addScaledVector(o,Tw.z),a}static isFrontFacing(t,e,n,i){return xw.subVectors(n,e),bw.subVectors(t,e),xw.cross(bw).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 xw.subVectors(this.c,this.b),bw.subVectors(this.a,this.b),.5*xw.cross(bw).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Lw.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Lw.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return Lw.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return Lw.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Lw.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;Aw.subVectors(i,n),Mw.subVectors(s,n),Sw.subVectors(t,n);const a=Aw.dot(Sw),l=Mw.dot(Sw);if(a<=0&&l<=0)return e.copy(n);Cw.subVectors(t,i);const c=Aw.dot(Cw),h=Mw.dot(Cw);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(Aw,r);Nw.subVectors(t,s);const d=Aw.dot(Nw),p=Mw.dot(Nw);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(Mw,o);const m=c*p-d*h;if(m<=0&&h-c>=0&&d-p>=0)return Ew.subVectors(s,i),o=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Ew,o);const f=1/(m+_+u);return r=_*f,o=u*f,e.copy(n).addScaledVector(Aw,r).addScaledVector(Mw,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Ow=0;class Pw extends jx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Ow++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=Ex,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=ix,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=Ux,this.stencilZFail=Ux,this.stencilZPass=Ux,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!==Ex&&(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++}}Pw.prototype.isMaterial=!0;const Rw={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},Iw={h:0,s:0,l:0},Fw={h:0,s:0,l:0};function Dw(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 Bw(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function zw(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class kw{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=Qx(t,1),e=Zx(e,0,1),n=Zx(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=Dw(s,i,t+1/3),this.g=Dw(s,i,t),this.b=Dw(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=Rw[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=Bw(t.r),this.g=Bw(t.g),this.b=Bw(t.b),this}copyLinearToSRGB(t){return this.r=zw(t.r),this.g=zw(t.g),this.b=zw(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?Ww:jw)(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 rb).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 Yw.makeRotationFromQuaternion(t),this.applyMatrix4(Yw),this}rotateX(t){return Yw.makeRotationX(t),this.applyMatrix4(Yw),this}rotateY(t){return Yw.makeRotationY(t),this.applyMatrix4(Yw),this}rotateZ(t){return Yw.makeRotationZ(t),this.applyMatrix4(Yw),this}translate(t,e,n){return Yw.makeTranslation(t,e,n),this.applyMatrix4(Yw),this}scale(t,e,n){return Yw.makeScale(t,e,n),this.applyMatrix4(Yw),this}lookAt(t){return $w.lookAt(t),$w.updateMatrix(),this.applyMatrix4($w.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Jw).negate(),this.translate(Jw.x,Jw.y,Jw.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(),iT.copy(n.boundingSphere),iT.applyMatrix4(s),!1===t.ray.intersectsSphere(iT))return;if(eT.copy(s).invert(),nT.copy(t.ray).applyMatrix4(eT),null!==n.boundingBox&&!1===nT.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:gT.clone(),object:t}}(t,e,n,i,sT,rT,oT,fT);if(p){a&&(pT.fromBufferAttribute(a,c),_T.fromBufferAttribute(a,h),mT.fromBufferAttribute(a,u),p.uv=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb)),l&&(pT.fromBufferAttribute(l,c),_T.fromBufferAttribute(l,h),mT.fromBufferAttribute(l,u),p.uv2=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb));const t={a:c,b:h,c:u,normal:new gb,materialIndex:0};Lw.getNormal(sT,rT,oT,t.normal),p.face=t}return p}vT.prototype.isMesh=!0;class xT extends tT{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 gb;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}}AT.prototype.isShaderMaterial=!0;class MT extends yw{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new Yb,this.projectionMatrix=new Yb,this.projectionMatrixInverse=new Yb}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)}}MT.prototype.isCamera=!0;class ET extends MT{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*Xx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Xx*Math.atan(Math.tan(.5*qx*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*qx*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}}ET.prototype.isPerspectiveCamera=!0;const ST=90;class CT extends yw{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 ET(ST,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new gb(1,0,0)),this.add(i);const s=new ET(ST,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new gb(-1,0,0)),this.add(s);const r=new ET(ST,1,t,e);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new gb(0,1,0)),this.add(r);const o=new ET(ST,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new gb(0,-1,0)),this.add(o);const a=new ET(ST,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new gb(0,0,1)),this.add(a);const l=new ET(ST,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new gb(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 NT extends ub{constructor(t,e,n,i,s,r,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:sx,n,i,s,r,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}NT.prototype.isCubeTexture=!0;class LT extends _b{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 NT(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:fx,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=Ex,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 xT(5,5,5),s=new AT({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:bT(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});s.uniforms.tEquirect.value=e;const r=new vT(i,s),o=e.minFilter;e.minFilter===vx&&(e.minFilter=fx);return new CT(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)}}LT.prototype.isWebGLCubeRenderTarget=!0;const OT=new gb,PT=new gb,RT=new rb;class IT{constructor(t=new gb(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=OT.subVectors(n,e).cross(PT.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(OT),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||RT.getNormalMatrix(t),i=this.coplanarPoint(OT).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)}}IT.prototype.isPlane=!0;const FT=new kb,DT=new gb;class BT{constructor(t=new IT,e=new IT,n=new IT,i=new IT,s=new IT,r=new IT){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(),FT.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSprite(t){return FT.center.set(0,0,0),FT.radius=.7071067811865476,FT.applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}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,DT.y=i.normal.y>0?t.max.y:t.min.y,DT.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(DT)<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 zT(){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 kT(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}\\\\\\\"},VT={common:{diffuse:{value:new kw(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new rb},uv2Transform:{value:new rb},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 sb(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 kw(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 kw(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}},sprite:{diffuse:{value:new kw(16777215)},opacity:{value:1},center:{value:new sb(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}}},HT={basic:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.fog]),vertexShader:GT.meshbasic_vert,fragmentShader:GT.meshbasic_frag},lambert:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshlambert_vert,fragmentShader:GT.meshlambert_frag},phong:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},specular:{value:new kw(1118481)},shininess:{value:30}}]),vertexShader:GT.meshphong_vert,fragmentShader:GT.meshphong_frag},standard:{uniforms:wT([VT.common,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.roughnessmap,VT.metalnessmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag},toon:{uniforms:wT([VT.common,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.gradientmap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshtoon_vert,fragmentShader:GT.meshtoon_frag},matcap:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,{matcap:{value:null}}]),vertexShader:GT.meshmatcap_vert,fragmentShader:GT.meshmatcap_frag},points:{uniforms:wT([VT.points,VT.fog]),vertexShader:GT.points_vert,fragmentShader:GT.points_frag},dashed:{uniforms:wT([VT.common,VT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:GT.linedashed_vert,fragmentShader:GT.linedashed_frag},depth:{uniforms:wT([VT.common,VT.displacementmap]),vertexShader:GT.depth_vert,fragmentShader:GT.depth_frag},normal:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,{opacity:{value:1}}]),vertexShader:GT.meshnormal_vert,fragmentShader:GT.meshnormal_frag},sprite:{uniforms:wT([VT.sprite,VT.fog]),vertexShader:GT.sprite_vert,fragmentShader:GT.sprite_frag},background:{uniforms:{uvTransform:{value:new rb},t2D:{value:null}},vertexShader:GT.background_vert,fragmentShader:GT.background_frag},cube:{uniforms:wT([VT.envmap,{opacity:{value:1}}]),vertexShader:GT.cube_vert,fragmentShader:GT.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:GT.equirect_vert,fragmentShader:GT.equirect_frag},distanceRGBA:{uniforms:wT([VT.common,VT.displacementmap,{referencePosition:{value:new gb},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:GT.distanceRGBA_vert,fragmentShader:GT.distanceRGBA_frag},shadow:{uniforms:wT([VT.lights,VT.fog,{color:{value:new kw(0)},opacity:{value:1}}]),vertexShader:GT.shadow_vert,fragmentShader:GT.shadow_frag}};function jT(t,e,n,i,s){const r=new kw(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===lx)?(void 0===a&&(a=new vT(new xT(1,1,1),new AT({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:bT(HT.cube.uniforms),vertexShader:HT.cube.vertexShader,fragmentShader:HT.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 vT(new UT(2,2),new AT({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:bT(HT.background.uniforms),vertexShader:HT.background.vertexShader,fragmentShader:HT.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 WT(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 YT(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new IT,a=new rb,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 LT(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}}}HT.physical={uniforms:wT([HT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new sb(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new kw(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new sb},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new kw(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new kw(1,1,1)},specularTintMap:{value:null}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag};class JT extends MT{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}}JT.prototype.isOrthographicCamera=!0;class ZT extends AT{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ZT.prototype.isRawShaderMaterial=!0;const QT=Math.pow(2,8),KT=[.125,.215,.35,.446,.526,.582],tA=5+KT.length,eA=20,nA={[Dx]:0,[Bx]:1,[kx]:2,3004:3,3005:4,3006:5,[zx]:6},iA=new JT,{_lodPlanes:sA,_sizeLods:rA,_sigmas:oA}=_A(),aA=new kw;let lA=null;const cA=(1+Math.sqrt(5))/2,hA=1/cA,uA=[new gb(1,1,1),new gb(-1,1,1),new gb(1,1,-1),new gb(-1,1,-1),new gb(0,cA,hA),new gb(0,cA,-hA),new gb(hA,0,cA),new gb(-hA,0,cA),new gb(cA,hA,0),new gb(-cA,hA,0)];class dA{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new gb(0,1,0);return new ZT({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:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),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${xA()}\\\\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})}(eA),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){lA=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=vA(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=gA(),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?QT:0,QT,QT),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===Ex&&e.type===yx&&e.encoding===Bx?t.value=nA[3e3]:t.value=nA[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=vA()):null==this._equirectShader&&(this._equirectShader=gA());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new vT(sA[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),fA(e,0,0,3*QT,2*QT),n.setRenderTarget(e),n.render(s,iA)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eeA&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,iA)}}function pA(t){return void 0!==t&&t.type===yx&&(t.encoding===Dx||t.encoding===Bx||t.encoding===zx)}function _A(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=KT[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new tT;y.setAttribute(\\\\\\\"position\\\\\\\",new Hw(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new Hw(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new Hw(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function mA(t){const e=new _b(3*QT,3*QT,t);return e.texture.mapping=lx,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function fA(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function gA(){const t=new sb(1,1);return new ZT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\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 vA(){return new ZT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),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${xA()}\\\\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 yA(){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 xA(){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 bA(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 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XM(t,e){const n=GT[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return qM(n)}const YM=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,$M=/#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 JM(t){return t.replace($M,QM).replace(YM,ZM)}function ZM(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(VM).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[KM(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=FM(s,35633,v+f+o),b=FM(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=zM(s,x,\\\\\\\"vertex\\\\\\\"),n=zM(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 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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===lx||b.mapping===cx),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 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||iE),s.length>1&&s.sort(e||sE),r.length>1&&r.sort(e||sE)}}}function oE(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new rE(t),e.set(n,[s])):i>=e.get(n).length?(s=new rE(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function aE(){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 gb,color:new kw};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new gb,direction:new gb,color:new kw,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new gb,color:new kw,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new gb,skyColor:new kw,groundColor:new kw};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new kw,position:new gb,halfWidth:new gb,halfHeight:new gb}}return t[e.id]=n,n}}}let lE=0;function cE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function hE(t,e){const n=new aE,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 sb};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb,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 gb);const r=new gb,o=new Yb,a=new Yb;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(cE);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=VT.LTC_FLOAT_1,s.rectAreaLTC2=VT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_HALF_1,s.rectAreaLTC2=VT.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=lE++)},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 uE(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class pE extends Pw{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}}pE.prototype.isMeshDepthMaterial=!0;class _E extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new gb,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}}_E.prototype.isMeshDistanceMaterial=!0;function mE(t,e,n){let i=new BT;const s=new sb,r=new sb,o=new pb,a=new pE({depthPacking:3201}),l=new _E,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new AT({uniforms:{shadow_pass:{value:null},resolution:{value:new sb},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 tT;_.setAttribute(\\\\\\\"position\\\\\\\",new Hw(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new vT(_,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:fx,magFilter:fx,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new _b(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:px,magFilter:px,format:Ex};u.map=new _b(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 fE(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new pb;let i=null;const s=new pb(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 pb).fromArray(O),I=(new pb).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?nb: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 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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,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.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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 ME(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=ab(\\\\\\\"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 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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. 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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 THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),h=35866}ut.pixelStorei(37440,i.flipY),ut.pixelStorei(37441,i.premultiplyAlpha),ut.pixelStorei(3317,i.unpackAlignment);const u=ut.getParameter(3314),d=ut.getParameter(32878),p=ut.getParameter(3316),_=ut.getParameter(3315),f=ut.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ut.pixelStorei(3314,g.width),ut.pixelStorei(32878,g.height),ut.pixelStorei(3316,t.min.x),ut.pixelStorei(3315,t.min.y),ut.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed 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 ME{}).prototype.isWebGL1Renderer=!0;class EE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.density=e}clone(){return new EE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}EE.prototype.isFogExp2=!0;class SE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.near=e,this.far=n}clone(){return new SE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}SE.prototype.isFog=!0;class CE extends yw{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}}CE.prototype.isScene=!0;class NE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Gx,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=Jx()}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:IE.clone(),uv:Lw.getUV(IE,UE,GE,VE,HE,jE,WE,new sb),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 XE(t,e,n,i,s,r){BE.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(zE.x=r*BE.x-s*BE.y,zE.y=s*BE.x+r*BE.y):zE.copy(BE),t.copy(e),t.x+=zE.x,t.y+=zE.y,t.applyMatrix4(kE)}qE.prototype.isSprite=!0;const YE=new gb,$E=new pb,JE=new pb,ZE=new gb,QE=new Yb;class KE extends vT{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new Yb,this.bindMatrixInverse=new Yb}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 pb,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.\\\\\\\")}}}_S.prototype.isLine=!0;const mS=new gb,fS=new gb;class gS extends _S{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 MS(t,e,n,i,s,r,o){const a=bS.distanceSqToPoint(t);if(as.far)return;r.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}AS.prototype.isPoints=!0;(class extends ub{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:Mx,this.minFilter=void 0!==r?r:fx,this.magFilter=void 0!==s?s:fx,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 ES extends ub{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}}ES.prototype.isCompressedTexture=!0;(class extends ub{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 ub{constructor(t,e,n,i,s,r,o,a,l,c){if((c=void 0!==c?c:Sx)!==Sx&&c!==Cx)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===Sx&&(n=xx),void 0===n&&c===Cx&&(n=Ax),super(null,i,s,r,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:px,this.minFilter=void 0!==a?a:px,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new gb,new gb,new gb,new Lw;class SS{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 sb:new gb);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 gb,i=[],s=[],r=[],o=new gb,a=new Yb;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new gb)}s[0]=new gb,r[0]=new gb;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(Zx(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(Zx(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 CS extends SS{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 sb,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]:(OS.subVectors(i[0],i[1]).add(i[0]),o=OS);const 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hC(t.prev,t,t.next)<0?hC(t,e,t.next)>=0&&hC(t,t.prev,e)>=0:hC(t,e,t.prev)<0||hC(t,t.next,e)<0}function fC(t,e){const n=new yC(t.i,t.x,t.y),i=new yC(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 gC(t,e,n,i){const s=new yC(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 vC(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 yC(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 xC{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 wC(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 sb(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 sb(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}}CC.prototype.isMeshPhysicalMaterial=!0;class NC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new kw(16777215),this.specular=new kw(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}NC.prototype.isMeshPhongMaterial=!0;class LC extends Pw{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}LC.prototype.isMeshToonMaterial=!0;class OC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}OC.prototype.isMeshNormalMaterial=!0;class PC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(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}}PC.prototype.isMeshLambertMaterial=!0;class RC extends Pw{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new kw(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}RC.prototype.isMeshMatcapMaterial=!0;class IC extends lS{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}}IC.prototype.isLineDashedMaterial=!0;const FC={arraySlice:function(t,e,n){return FC.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=FC.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(r),d=FC.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===s){(new fb).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=FC.arraySlice(n,s,r),this.values=FC.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&&FC.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=FC.arraySlice(this.times),e=FC.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Ox,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=FC.arraySlice(t,0,r),this.values=FC.arraySlice(e,0,r*n)):(this.times=t,this.values=e),this}clone(){const t=FC.arraySlice(this.times,0),e=FC.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}UC.prototype.TimeBufferType=Float32Array,UC.prototype.ValueBufferType=Float32Array,UC.prototype.DefaultInterpolation=Lx;class GC extends UC{}GC.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",GC.prototype.ValueBufferType=Array,GC.prototype.DefaultInterpolation=Nx,GC.prototype.InterpolantFactoryMethodLinear=void 0,GC.prototype.InterpolantFactoryMethodSmooth=void 0;class VC extends UC{}VC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class HC extends UC{}HC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class jC extends DC{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)fb.slerpFlat(s,0,r,l-o,r,l,a);return s}}class WC extends UC{InterpolantFactoryMethodLinear(t){return new jC(this.times,this.values,this.getValueSize(),t)}}WC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",WC.prototype.DefaultInterpolation=Lx,WC.prototype.InterpolantFactoryMethodSmooth=void 0;class qC extends UC{}qC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",qC.prototype.ValueBufferType=Array,qC.prototype.DefaultInterpolation=Nx,qC.prototype.InterpolantFactoryMethodLinear=void 0,qC.prototype.InterpolantFactoryMethodSmooth=void 0;class XC extends UC{}XC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class YC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=Jx(),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($C(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(UC.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=[];FC.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){fb.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;fb.multiplyQuaternionsFlat(t,r,t,e,t,n),fb.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 ON=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",PN=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),RN=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",IN=\\\\\\\"[^\\\\\\\"+ON.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",FN=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",RN),DN=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",IN),BN=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",RN),zN=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",RN),kN=new RegExp(\\\\\\\"^\\\\\\\"+FN+DN+BN+zN+\\\\\\\"$\\\\\\\"),UN=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class GN{constructor(t,e,n){this.path=e,this.parsedPath=n||GN.parseTrackName(e),this.node=GN.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 GN.Composite(t,e,n):new GN(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(PN,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=kN.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!==UN.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 Fx: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=Rx,i.endingEnd=Rx):(i.endingStart=t?this.zeroSlopeAtStart?Rx:Px:Ix,i.endingEnd=e?this.zeroSlopeAtEnd?Rx:Px:Ix)}_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 jx{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 LN(GN.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 jN.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)}}WN.prototype.isBox2=!0;const qN=new gb,XN=new gb;class YN{constructor(t=new gb,e=new gb){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){qN.subVectors(t,this.start),XN.subVectors(this.end,this.start);const n=XN.dot(XN);let i=XN.dot(qN)/n;return e&&(i=Zx(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 yw{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 $N=new gb,JN=new Yb,ZN=new Yb;function QN(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=sL.copy(t.start).lerp(t.end,s);if(e.containsPoint(r))return{normal:oL.normal.clone(),point:r.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=aL.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 dL(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 _L={rotateSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI}},mL={type:\\\\\\\"change\\\\\\\"},fL=new p.a,gL=new ny;class vL extends pL{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)},this._startCameraRotation=new Xv.a,this._rotationSpeed=_L.rotateSpeed,this._rotationRange={min:_L.rotationRange.min,max:_L.rotationRange.max},this._azimuthalAngle=0,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),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._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),s=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${s}px`,t.style.left=`${s}px`,t}_addEvents(){var t;nx.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),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;nx.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),null===(t=this._translateDomElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}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=rr.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this._computeAzimuthalAngle(),this.dispatchEvent(mL)}_computeAzimuthalAngle(){this._camera.updateMatrixWorld(),fL.set(0,0,1),this._camera.localToWorld(fL),fL.sub(this._camera.position),gL.setFromVector3(fL),this._azimuthalAngle=gL.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(mL))}_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))}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}_getTouch(t,e){for(let n=0;n{AL.PARAM_CALLBACK_updatePlayerParams(t)}}}const TL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...wL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...wL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...wL()}),this.gravity=aa.VECTOR3([0,-30,0],{...wL()}),this.translateSpeed=aa.FLOAT(1),this.rotateSpeed=aa.FLOAT(_L.rotateSpeed),this.updateCollider=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...wL()}),this.startRotation=aa.VECTOR3([0,0,0],{...wL()}),this.reset=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(\\\\\\\"-$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]})}};class AL extends qv{constructor(){super(...arguments),this.paramsConfig=TL,this._controls_by_element_id=new Map}static type(){return rs.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(yL,Jo.BASE),new Zo(xL,Jo.BASE),new Zo(bL,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new vL(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())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=!1,this._player.runAllowed=!1,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._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(yL,(()=>{this.dispatchEventToOutput(yL,{})})),t.addEventListener(xL,(()=>{this.dispatchEventToOutput(xL,{})})),t.addEventListener(bL,(()=>{this.dispatchEventToOutput(bL,{})}))}updateRequired(){return!0}setupControls(t){t.setRotationSpeed(this.pv.rotateSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle})}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 ML;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(ML||(ML={}));const EL=[ML.ALL_TOGETHER,ML.BATCH];const SL=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{CL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=aa.BOOLEAN(0),this.cookMode=aa.INTEGER(EL.indexOf(ML.ALL_TOGETHER),{menu:{entries:EL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=aa.INTEGER(1,{visibleIf:{cookMode:EL.indexOf(ML.BATCH)},separatorAfter:!0}),this.updateResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_print_resolve(t)}})}};class CL extends ka{constructor(){super(...arguments),this.paramsConfig=SL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(CL.INPUT_TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(CL.OUTPUT_FIRST_NODE,Jo.BASE),new Zo(CL.OUTPUT_EACH_NODE,Jo.BASE),new Zo(CL.OUTPUT_ALL_NODES,Jo.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=EL[this.pv.cookMode];switch(t){case ML.ALL_TOGETHER:return this._cook_nodes_all_together();case ML.BATCH:return this._cook_nodes_batch()}ls.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(CL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var NL,LL;CL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",CL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",CL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",CL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(NL||(NL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(LL||(LL={}));const OL=new class extends la{};class PL extends ka{constructor(){super(...arguments),this.paramsConfig=OL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(NL.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(LL.OUT,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(LL.OUT,t)}}const RL=\\\\\\\"init\\\\\\\",IL=\\\\\\\"dispose\\\\\\\",FL=\\\\\\\"reset\\\\\\\";function DL(){return{callback:t=>{GL.PARAM_CALLBACK_updatePlayerParams(t)}}}const BL=new p.a,zL=new p.a,kL=new ny;const UL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.playerObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{types:[is.PERSPECTIVE,is.ORTHOGRAPHIC],context:ts.OBJ}}),this.initPlayer=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_initPlayer(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...DL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...DL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...DL()}),this.gravity=aa.VECTOR3([0,-30,0],{...DL()}),this.speed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.jumpAllowed=aa.BOOLEAN(!0,{...DL()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...DL()}),this.runAllowed=aa.BOOLEAN(!0,{...DL()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_updateCollider(t)}}),this.mesh=aa.FOLDER(),this.useMesh=aa.BOOLEAN(!0,{callback:t=>{GL.PARAM_CALLBACK_updatePlayerMesh(t)}}),this.material=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},callback:t=>{GL.PARAM_CALLBACK_updatePlayerMaterial(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,5,0],{...DL()}),this.reset=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_resetPlayer(t)}})}};class GL extends ka{constructor(){super(...arguments),this.paramsConfig=UL}static type(){return rs.PLAYER}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(RL,Jo.BASE,this._initPlayer.bind(this)),new Zo(IL,Jo.BASE,this._disposePlayer.bind(this)),new Zo(FL,Jo.BASE,this._resetPlayer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(RL,Jo.BASE),new Zo(IL,Jo.BASE),new Zo(FL,Jo.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 Vy(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(RL,{})}_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(IL,{})}_resetPlayer(){this._player&&this._player.reset(),this.dispatchEventToOutput(FL,{})}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._getCollider();if(!i)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:n,collider:i,meshName:this.path()})}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_getAzimuthalAngle(){if(!this._cameraObject||!this._player)return 0;const t=this._cameraObject.position,e=this._player.object.position;return BL.copy(t),zL.copy(e),BL.sub(zL),kL.setFromVector3(BL),kL.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 VL,HL=n(39),jL=n(36);class WL{constructor(t,e,n=0,i=1/0){this.ray=new HL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new jL.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 XL(t,this,n,e),n.sort(qL),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=JL[this._node.pv.intersectWith];switch(e){case $L.GEOMETRY:return this._intersect_with_geometry(t);case $L.PLANE:return this._intersect_with_plane(t)}ls.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=zs[this._node.pv.geoAttributeType],n=QL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Bs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Bs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ls.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Ls(t.object.constructor)){case Cs.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Cs.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 Bs.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=Ks.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Bs.STRING:{const t=new _r(i).points()[0];return t?t.stringAttribValue(e):void 0}}ls.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Bs.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Bs.STRING:{const n=new _r(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ls.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(Er.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Er.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=lO[this._node.pv.targetType];switch(t){case aO.NODE:return this._update_target_from_node();case aO.SCENE_GRAPH:return this._update_target_from_scene_graph()}ls.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)}}QL._vA=new p.a,QL._vB=new p.a,QL._vC=new p.a,QL._uvA=new d.a,QL._uvB=new d.a,QL._uvC=new d.a,QL._hitUV=new d.a;class KL{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 tO=1e3/60;var eO;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(eO||(eO={}));const nO=[eO.CPU,eO.GPU];function iO(t={}){return t.mode=nO.indexOf(eO.CPU),{visibleIf:t}}function sO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.GEOMETRY),{visibleIf:t}}function rO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.PLANE),{visibleIf:t}}function oO(t={}){return t.mode=nO.indexOf(eO.GPU),{visibleIf:t}}var aO;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(aO||(aO={}));const lO=[aO.SCENE_GRAPH,aO.NODE];const cO=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(nO.indexOf(eO.CPU),{menu:{entries:nO.map(((t,e)=>({name:t,value:e})))}}),this.mouse=aa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=aa.BOOLEAN(0),this.overrideRay=aa.BOOLEAN(0,{visibleIf:{mode:nO.indexOf(eO.CPU),overrideCamera:1}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=aa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=aa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=aa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{KL.PARAM_CALLBACK_update_material(t)},...oO()}),this.pixelValue=aa.VECTOR4([0,0,0,0],{cook:!1,...oO()}),this.hitThreshold=aa.FLOAT(.5,{cook:!1,...oO()}),this.intersectWith=aa.INTEGER(JL.indexOf($L.GEOMETRY),{menu:{entries:JL.map(((t,e)=>({name:t,value:e})))},...iO()}),this.pointsThreshold=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...iO()}),this.planeDirection=aa.VECTOR3([0,1,0],{...rO()}),this.planeOffset=aa.FLOAT(0,{...rO()}),this.targetType=aa.INTEGER(0,{menu:{entries:lO.map(((t,e)=>({name:t,value:e})))},...sO()}),this.targetNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.NODE)})}),this.objectMask=aa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=aa.BUTTON(null,{callback:(t,e)=>{QL.PARAM_CALLBACK_print_resolve(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.traverseChildren=aa.BOOLEAN(!0,{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO(),separatorAfter:!0}),this.tpositionTarget=aa.BOOLEAN(0,{cook:!1,...iO()}),this.position=aa.VECTOR3([0,0,0],{cook:!1,...iO({tpositionTarget:0})}),this.positionTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tpositionTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.tvelocity=aa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=aa.BOOLEAN(0,{cook:!1,...iO({tvelocity:1})}),this.velocity=aa.VECTOR3([0,0,0],{cook:!1,...iO({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tvelocity:1,tvelocityTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.geoAttribute=aa.BOOLEAN(0,sO()),this.geoAttributeName=aa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...sO({geoAttribute:1})}),this.geoAttributeType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},...sO({geoAttribute:1})}),this.geoAttributeValue1=aa.FLOAT(0,{cook:!1,...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.NUMERIC)})}),this.geoAttributeValues=aa.STRING(\\\\\\\"\\\\\\\",{...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.STRING)})})}};class hO extends ka{constructor(){super(...arguments),this.paramsConfig=cO,this.cpuController=new QL(this),this.gpuController=new KL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(hO.INPUT_TRIGGER,Jo.BASE,this._process_trigger_event_throttled.bind(this)),new Zo(hO.INPUT_MOUSE,Jo.MOUSE,this._process_mouse_event.bind(this)),new Zo(hO.INPUT_UPDATE_OBJECTS,Jo.BASE,this._process_trigger_update_objects.bind(this)),new Zo(hO.INPUT_TRIGGER_VEL_RESET,Jo.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(hO.OUTPUT_HIT,Jo.BASE),new Zo(hO.OUTPUT_MISS,Jo.BASE)])}trigger_hit(t){this.dispatchEventToOutput(hO.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(hO.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==nO.indexOf(eO.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)}),tO-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.velocity_controller.reset()}}var uO;hO.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",hO.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",hO.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",hO.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",hO.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",hO.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(uO||(uO={}));const dO=[uO.SET,uO.TOGGLE];const pO=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=aa.BOOLEAN(0),this.displayMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tdisplay:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.display=aa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:dO.indexOf(uO.SET)},separatorAfter:!0}),this.tbypass=aa.BOOLEAN(0),this.bypassMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tbypass:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.bypass=aa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:dO.indexOf(uO.SET)}}),this.execute=aa.BUTTON(null,{callback:t=>{_O.PARAM_CALLBACK_execute(t)}})}};class _O extends ka{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.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=dO[this.pv.displayMode];switch(i){case uO.SET:return void n.set(this.pv.display);case uO.TOGGLE:return void n.set(!n.active())}ls.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=dO[this.pv.bypassMode];switch(i){case uO.SET:return void n.set(this.pv.bypass);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var mO;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(mO||(mO={}));const fO=[mO.BOOLEAN,mO.BUTTON,mO.NUMBER,mO.VECTOR2,mO.VECTOR3,mO.VECTOR4,mO.STRING],gO=fO.indexOf(mO.BOOLEAN),vO=fO.indexOf(mO.NUMBER),yO=fO.indexOf(mO.VECTOR2),xO=fO.indexOf(mO.VECTOR3),bO=fO.indexOf(mO.VECTOR4),wO=fO.indexOf(mO.STRING),TO=\\\\\\\"output\\\\\\\";const AO=new class extends la{constructor(){super(...arguments),this.param=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=aa.INTEGER(vO,{menu:{entries:fO.map(((t,e)=>({name:t,value:e})))}}),this.toggle=aa.BOOLEAN(0,{visibleIf:{type:gO}}),this.boolean=aa.BOOLEAN(0,{visibleIf:{type:gO,toggle:0}}),this.number=aa.FLOAT(0,{visibleIf:{type:vO}}),this.vector2=aa.VECTOR2([0,0],{visibleIf:{type:yO}}),this.vector3=aa.VECTOR3([0,0,0],{visibleIf:{type:xO}}),this.vector4=aa.VECTOR4([0,0,0,0],{visibleIf:{type:bO}}),this.increment=aa.BOOLEAN(0,{visibleIf:[{type:vO},{type:yO},{type:xO},{type:bO}]}),this.string=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:wO}}),this.execute=aa.BUTTON(null,{callback:t=>{MO.PARAM_CALLBACK_execute(t)}})}};class MO extends ka{constructor(){super(...arguments),this.paramsConfig=AO,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 Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(TO,Jo.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(TO,t)}async _new_param_value(t){const e=fO[this.pv.type];switch(e){case mO.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case mO.BUTTON:return t.options.executeCallback();case mO.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Er.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case mO.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Er.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 mO.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Er.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 mO.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Er.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 mO.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ls.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 EO=new class extends la{constructor(){super(...arguments),this.outputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class SO extends ka{constructor(){super(...arguments),this.paramsConfig=EO}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[Jo.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(Jo.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 RO extends sa{static context(){return ts.EVENT}cook(){this.cookController.endCook()}}class IO extends RO{}class FO extends IO{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 DO extends IO{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 BO extends IO{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 zO extends IO{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 kO extends RO{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 UO extends IO{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 GO=\\\\\\\"int\\\\\\\";const VO=new class extends la{constructor(){super(...arguments),this.float=aa.FLOAT(0)}};class HO extends df{constructor(){super(...arguments),this.paramsConfig=VO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(GO)} = int(${hf.float(e)})`;t.addBodyLines(this,[n])}}const jO=\\\\\\\"float\\\\\\\";const WO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class qO extends df{constructor(){super(...arguments),this.paramsConfig=WO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(jO,Bo.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(jO)} = float(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const XO=\\\\\\\"bool\\\\\\\";const YO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class $O extends df{constructor(){super(...arguments),this.paramsConfig=YO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(XO,Bo.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(XO)} = bool(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const JO=new class extends la{constructor(){super(...arguments),this.bool=aa.BOOLEAN(0)}};class ZO extends df{constructor(){super(...arguments),this.paramsConfig=JO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(GO)} = int(${hf.bool(e)})`;t.addBodyLines(this,[n])}}const QO=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0)}};class KO extends df{constructor(){super(...arguments),this.paramsConfig=QO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(KO.OUTPUT_NAME,Bo.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(KO.OUTPUT_NAME)} = ${hf.float2(e,n)}`;t.addBodyLines(this,[i])}}KO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const tP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0)}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(eP.OUTPUT_NAME,Bo.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(eP.OUTPUT_NAME)} = ${hf.float3(e,n,i)}`;t.addBodyLines(this,[s])}}eP.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const nP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0),this.w=aa.FLOAT(0)}};class iP extends df{constructor(){super(...arguments),this.paramsConfig=nP}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(iP.OUTPUT_NAME,Bo.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(iP.OUTPUT_NAME)} = ${hf.float4(e,n,i,s)}`;t.addBodyLines(this,[r])}}iP.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const sP=new class extends la{};class rP extends df{constructor(){super(...arguments),this.paramsConfig=sP}}function oP(t,e){const n=e.components,i=e.param_type;return class extends rP{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Ho(t,Bo.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 aP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],lP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],cP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class hP extends(oP(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Er.VECTOR2})){}class uP extends(oP(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Er.VECTOR3})){}class dP extends(oP(\\\\\\\"vec4ToFloat\\\\\\\",{components:cP,param_type:Er.VECTOR4})){}class pP extends rP{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(pP.OUTPUT_NAME_VEC3,Bo.VEC3),new Ho(pP.OUTPUT_NAME_W,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR4,pP.INPUT_NAME_VEC4,cP.map((t=>0)))}setLines(t){const e=[],n=pP.INPUT_NAME_VEC4,i=pP.OUTPUT_NAME_VEC3,s=pP.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)}}pP.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",pP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",pP.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class _P extends rP{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(_P.OUTPUT_NAME_VEC2,Bo.VEC2),new Ho(_P.OUTPUT_NAME_Z,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR3,_P.INPUT_NAME_VEC3,lP.map((t=>0)))}setLines(t){const e=[],n=_P.INPUT_NAME_VEC3,i=_P.OUTPUT_NAME_VEC2,s=_P.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)}}_P.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",_P.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",_P.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class mP extends rP{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(mP.OUTPUT_NAME_VEC3,Bo.VEC3)])}createParams(){this.addParam(Er.VECTOR2,mP.INPUT_NAME_VEC2,aP.map((t=>0))),this.addParam(Er.FLOAT,mP.INPUT_NAME_Z,0)}setLines(t){const e=[],n=mP.INPUT_NAME_VEC2,i=mP.INPUT_NAME_Z,s=mP.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)}}mP.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",mP.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",mP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class fP extends rP{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(fP.OUTPUT_NAME_VEC4,Bo.VEC4)])}createParams(){this.addParam(Er.VECTOR3,fP.INPUT_NAME_VEC3,lP.map((t=>0))),this.addParam(Er.FLOAT,fP.INPUT_NAME_W,0)}setLines(t){const e=[],n=fP.INPUT_NAME_VEC3,i=fP.INPUT_NAME_W,s=fP.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),t.addBodyLines(this,e)}}fP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",fP.INPUT_NAME_W=\\\\\\\"w\\\\\\\",fP.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const gP=new class extends la{};class vP extends df{constructor(){super(...arguments),this.paramsConfig=gP}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${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 yP extends vP{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Bo.FLOAT]}}class xP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}}class bP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t]}}class wP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t]}}class TP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t,t]}}function AP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||\\\\\\\"in\\\\\\\";return class extends yP{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 MP extends(AP(\\\\\\\"abs\\\\\\\")){}class EP extends(AP(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class SP extends(AP(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class CP extends(AP(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class NP extends(AP(\\\\\\\"ceil\\\\\\\")){}class LP extends(AP(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class OP extends(AP(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class PP extends(AP(\\\\\\\"exp\\\\\\\")){}class RP extends(AP(\\\\\\\"exp2\\\\\\\")){}class IP extends(AP(\\\\\\\"floor\\\\\\\")){}class FP extends(AP(\\\\\\\"fract\\\\\\\")){}class DP extends(AP(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class BP extends(AP(\\\\\\\"log\\\\\\\")){}class zP extends(AP(\\\\\\\"log2\\\\\\\")){}class kP extends(AP(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class UP extends(AP(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class GP extends(AP(\\\\\\\"sign\\\\\\\")){}class VP extends(AP(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class HP extends(AP(\\\\\\\"sqrt\\\\\\\")){}class jP extends(AP(\\\\\\\"tan\\\\\\\")){}function WP(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 xP{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 Tf(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||Bo.FLOAT;return[e,e]}}}class qP extends(WP(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class XP extends(WP(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class YP extends(WP(\\\\\\\"max\\\\\\\")){}class $P extends(WP(\\\\\\\"min\\\\\\\")){}class JP extends(WP(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Bo.FLOAT;return[t,t]}}class ZP extends(WP(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class QP extends(WP(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Bo.VEC3})){}class KP extends(WP(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function tR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=e.default||{},o=e.out_type||Bo.FLOAT,a=e.functions||[];return class extends bP{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 Tf(this,t)))}}}class eR extends(tR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class nR extends(tR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class iR extends(tR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function sR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.operation,r=e.allowed_in_types;return class extends xP{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 hf.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||Bo.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 hf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function hR(t,e){return class extends cR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class uR extends(hR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class dR extends(hR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var pR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(pR||(pR={}));var _R,mR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(_R||(_R={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(mR||(mR={}));const fR=[_R.POSITION,_R.VELOCITY,_R.MASS,_R.FORCE],gR=[mR.POSITION,mR.VELOCITY],vR={[_R.POSITION]:[0,0,0],[_R.VELOCITY]:[0,0,0],[_R.MASS]:1,[_R.FORCE]:[0,-9.8,0]};const yR=new class extends la{};class xR extends df{constructor(){super(...arguments),this.paramsConfig=yR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(mR.POSITION,Bo.VEC3),new Ho(mR.VELOCITY,Bo.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()||Bo.VEC3;return[t,t,Bo.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return fR[t]}_gl_output_name(t){return gR[t]}paramDefaultValue(t){return vR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Bo.FLOAT,pR.DELTA_TIME),i=new Tf(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=hf.any(this.variableForInput(_R.POSITION)),r=hf.any(this.variableForInput(_R.VELOCITY)),o=hf.float(this.variableForInput(_R.MASS)),a=hf.any(this.variableForInput(_R.FORCE)),l=this.glVarName(mR.POSITION),c=this.glVarName(mR.VELOCITY),h=`${e} ${c} = compute_velocity_from_acceleration(${[r,a,o,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,u=`${e} ${l} = compute_position_from_velocity(${[s,c,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[h,u])}}var bR,wR=\\\\\\\"\\\\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\\\\\\\"}(bR||(bR={}));const TR=[bR.DIR,bR.UP],AR={[bR.DIR]:[0,0,1],[bR.UP]:[0,1,0]};class MR extends xP{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>TR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return AR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var ER;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(ER||(ER={}));const SR=[ER.LINEAR,ER.GAMMA,ER.SRGB,ER.RGBE,ER.RGBM,ER.RGBD,ER.LogLuv];const CR=new class extends la{constructor(){super(...arguments),this.color=aa.VECTOR4([1,1,1,1]),this.from=aa.INTEGER(SR.indexOf(ER.LINEAR),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.to=aa.INTEGER(SR.indexOf(ER.GAMMA),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=aa.FLOAT(2.2)}};class NR extends df{constructor(){super(...arguments),this.paramsConfig=CR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(NR.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=SR[this.pv.from],n=SR[this.pv.to],i=this.glVarName(NR.OUTPUT_NAME),s=hf.any(this.variableForInput(NR.INPUT_NAME)),r=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(s),e==ER.GAMMA||n==ER.GAMMA){const t=hf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}r.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);t.addBodyLines(this,r)}}var LR,OR;NR.INPUT_NAME=\\\\\\\"color\\\\\\\",NR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",NR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(LR||(LR={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(OR||(OR={}));const PR=[LR.EQUAL,LR.LESS_THAN,LR.GREATER_THAN,LR.LESS_THAN_OR_EQUAL,LR.GREATER_THAN_OR_EQUAL,LR.NOT_EQUAL],RR=[OR.EQUAL,OR.LESS_THAN,OR.GREATER_THAN,OR.LESS_THAN_OR_EQUAL,OR.GREATER_THAN_OR_EQUAL,OR.NOT_EQUAL],IR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const FR=new class extends la{constructor(){super(...arguments),this.test=aa.INTEGER(0,{menu:{entries:PR.map(((t,e)=>({name:`${RR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class DR extends df{constructor(){super(...arguments),this.paramsConfig=FR}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((()=>[Bo.BOOL]))}set_test_name(t){this.p.test.set(PR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=RR[this.pv.test],s=hf.any(this.variableForInput(this._gl_input_name(0))),r=hf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Vo[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=aa.BOOLEAN(0,zR(Bo.BOOL)),this.int=aa.INTEGER(0,zR(Bo.INT)),this.float=aa.FLOAT(0,zR(Bo.FLOAT)),this.vec2=aa.VECTOR2([0,0],zR(Bo.VEC2)),this.vec3=aa.VECTOR3([0,0,0],zR(Bo.VEC3)),this.vec4=aa.VECTOR4([0,0,0,0],zR(Bo.VEC4))}};class UR extends df{constructor(){super(...arguments),this.paramsConfig=kR,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>UR.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=hf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=hf.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=zo[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([[Bo.BOOL,this.p.bool],[Bo.INT,this.p.int],[Bo.FLOAT,this.p.float],[Bo.VEC2,this.p.vec2],[Bo.VEC3,this.p.vec3],[Bo.VEC4,this.p.vec4]]);const t=zo[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(UR.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(zo.indexOf(t))}}UR.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const GR=\\\\\\\"cross\\\\\\\";const VR=new class extends la{constructor(){super(...arguments),this.x=aa.VECTOR3([0,0,1]),this.y=aa.VECTOR3([0,1,0])}};class HR extends df{constructor(){super(...arguments),this.paramsConfig=VR}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(GR,Bo.VEC3)])}setLines(t){const e=hf.float(this.variableForInputParam(this.p.x)),n=hf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(GR)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class jR extends(tR(\\\\\\\"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 WR=\\\\\\\"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 qR=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR2([0,0]),this.center=aa.VECTOR2([0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class XR extends df{constructor(){super(...arguments),this.paramsConfig=qR}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}var YR=\\\\\\\"\\\\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 $R=[\\\\\\\"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\\\\\\\"],JR={\\\\\\\"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\\\\\\\":YR,\\\\\\\"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\\\\\\\"},ZR={\\\\\\\"bounce-in\\\\\\\":[YR],\\\\\\\"bounce-in-out\\\\\\\":[YR]},QR={\\\\\\\"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\\\\\\\"},KR=$R.indexOf(\\\\\\\"sine-in-out\\\\\\\");const tI=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(KR,{menu:{entries:$R.map(((t,e)=>({name:t,value:e})))}}),this.input=aa.FLOAT(0)}};class eI extends df{constructor(){super(...arguments),this.paramsConfig=tI}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.FLOAT)])}setLines(t){const e=$R[this.pv.type],n=QR[e];let i=[new Tf(this,JR[e])];const s=(ZR[e]||[]).map((t=>new Tf(this,t)));s&&(i=s.concat(i));const r=hf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var nI=\\\\\\\"//\\\\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 iI={srcMin:0,srcMax:1,destMin:0,destMax:1};class sI extends TP{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return iI[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const rI={srcMin:0,srcMax:1};class oI extends bP{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return rI[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const aI={destMin:0,destMax:1};class lI extends bP{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return aI[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const cI={center:.5,variance:.5};class hI extends bP{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return cI[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const uI=\\\\\\\"color\\\\\\\";const dI=new class extends la{constructor(){super(...arguments),this.mvPosition=aa.VECTOR4([0,0,0,0]),this.baseColor=aa.COLOR([0,0,0]),this.fogColor=aa.COLOR([1,1,1]),this.near=aa.FLOAT(0),this.far=aa.FLOAT(0)}};class pI extends df{constructor(){super(...arguments),this.paramsConfig=dI}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(uI,Bo.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Mf(this,Bo.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const s=new Tf(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=hf.vector4(this.variableForInputParam(this.p.mvPosition)),o=hf.vector3(this.variableForInputParam(this.p.baseColor)),a=hf.vector3(this.variableForInputParam(this.p.fogColor)),l=hf.vector3(this.variableForInputParam(this.p.near)),c=hf.vector3(this.variableForInputParam(this.p.far)),h=`vec3 ${this.glVarName(uI)} = compute_fog(${[r,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,s]),t.addBodyLines(this,[h])}}}const _I=new class extends la{};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}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${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),s=hf.any(this.variableForInput(i.name())),r=`\\\\t${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class fI extends df{constructor(){super(...arguments),this._children_controller_context=ts.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(mI.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const e=t.dest_connection_point(),i=hf.any(this.variableForInput(e.name())),s=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,l=hf.float(r),c=hf.float(o),h=hf.float(a),u=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${u} = ${l}; ${u} < ${c}; ${u}+= ${h}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(yI.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 TI=new class extends la{};class AI extends df{constructor(){super(...arguments),this.paramsConfig=TI}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 MI=new class extends la{constructor(){super(...arguments),this.hsluv=aa.VECTOR3([1,1,1])}};class EI extends df{constructor(){super(...arguments),this.paramsConfig=MI}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(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=hf.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 SI=new class extends la{constructor(){super(...arguments),this.hsv=aa.VECTOR3([1,1,1])}};class CI extends df{constructor(){super(...arguments),this.paramsConfig=SI}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(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=hf.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 NI=\\\\\\\"condition\\\\\\\";const LI=new class extends la{};class OI extends vI{constructor(){super(...arguments),this.paramsConfig=LI}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=[Bo.BOOL],e=Bo.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()||Bo.FLOAT;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const YI=\\\\\\\"mvMult\\\\\\\";const $I=new class extends la{constructor(){super(...arguments),this.vector=aa.VECTOR3([0,0,0])}};class JI extends df{constructor(){super(...arguments),this.paramsConfig=$I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(YI,Bo.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=hf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(YI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const ZI={mult:1};var QI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(QI||(QI={}));class KI extends wP{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[QI.VALUE,QI.PRE_ADD,QI.MULT,QI.POST_ADD][t]}paramDefaultValue(t){return ZI[t]}setLines(t){const e=hf.any(this.variableForInput(QI.VALUE)),n=hf.any(this.variableForInput(QI.PRE_ADD)),i=hf.any(this.variableForInput(QI.MULT)),s=hf.any(this.variableForInput(QI.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${s}`;t.addBodyLines(this,[a])}}class tF extends yP{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var eF;!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\\\\\\\"}(eF||(eF={}));const nF=[eF.CLASSIC_PERLIN_2D,eF.CLASSIC_PERLIN_3D,eF.CLASSIC_PERLIN_4D,eF.NOISE_2D,eF.NOISE_3D,eF.NOISE_4D],iF={[eF.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',[eF.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',[eF.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',[eF.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\\\\\\\",[eF.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\\\\\\\",[eF.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\\\\\\\"},sF={[eF.CLASSIC_PERLIN_2D]:Bo.VEC2,[eF.CLASSIC_PERLIN_3D]:Bo.VEC3,[eF.CLASSIC_PERLIN_4D]:Bo.VEC4,[eF.NOISE_2D]:Bo.VEC2,[eF.NOISE_3D]:Bo.VEC3,[eF.NOISE_4D]:Bo.VEC4},rF={[eF.CLASSIC_PERLIN_2D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_3D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_4D]:Bo.FLOAT,[eF.NOISE_2D]:Bo.FLOAT,[eF.NOISE_3D]:Bo.FLOAT,[eF.NOISE_4D]:Bo.FLOAT},oF={[eF.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[eF.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var aF;!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\\\\\\\"}(aF||(aF={}));const lF=[aF.NoChange,aF.Float,aF.Vec2,aF.Vec3,aF.Vec4],cF={[aF.NoChange]:\\\\\\\"Same as noise\\\\\\\",[aF.Float]:\\\\\\\"Float\\\\\\\",[aF.Vec2]:\\\\\\\"Vec2\\\\\\\",[aF.Vec3]:\\\\\\\"Vec3\\\\\\\",[aF.Vec4]:\\\\\\\"Vec4\\\\\\\"},hF={[aF.NoChange]:Bo.FLOAT,[aF.Float]:Bo.FLOAT,[aF.Vec2]:Bo.VEC2,[aF.Vec3]:Bo.VEC3,[aF.Vec4]:Bo.VEC4},uF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],dF=\\\\\\\"noise\\\\\\\",pF=nF.indexOf(eF.NOISE_3D),_F=aF.NoChange,mF={amp:1,freq:1};var fF;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(fF||(fF={}));const gF=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(pF,{menu:{entries:nF.map(((t,e)=>({name:`${t} (output: ${rF[t]})`,value:e})))}}),this.outputType=aa.INTEGER(_F,{menu:{entries:lF.map((t=>{const e=lF[t];return{name:cF[e],value:e}}))}}),this.octaves=aa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class vF extends df{constructor(){super(...arguments),this.paramsConfig=gF}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 Ho(dF,Bo.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((()=>dF))}_gl_input_name(t){return[fF.AMP,fF.POSITION,fF.FREQ,fF.OFFSET][t]}paramDefaultValue(t){return mF[t]}_expected_input_types(){const t=nF[this.pv.type],e=this._expected_output_types()[0],n=sF[t];return[e,n,n,n]}_expected_output_types(){const t=nF[this.pv.type],e=lF[this.pv.outputType];return e==aF.NoChange?[sF[t]]:[hF[e]]}setLines(t){const e=[],n=[],i=nF[this.pv.type],s=iF[i],r=rF[i];e.push(new Tf(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 Tf(this,s)),e.push(new Tf(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=Vo[o],e=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;rhf.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const r=`vec${t} ${s} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(r)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=nF[this.pv.type];return`fbm_${oF[t]}_${this.name()}`}fbm_function(){const t=nF[this.pv.type],e=oF[t],n=sF[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 < ${hf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${hf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${hf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),s=hf.any(this.variableForInput(fF.AMP)),r=hf.any(this.variableForInput(fF.POSITION)),o=hf.any(this.variableForInput(fF.FREQ));let a=hf.any(this.variableForInput(fF.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(dF),h=`${s}*${i}(${l})`;if(e)return`float ${c}${t} = (${h}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${h}`}}class yF extends yP{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const xF=new class extends la{};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}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 wF{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const TF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(zo.indexOf(Bo.FLOAT),{menu:{entries:zo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:zo.indexOf(Bo.VEC3)}})}};class AF extends df{constructor(){super(...arguments),this.paramsConfig=TF,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((()=>[zo[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=zo[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=zo[this.pv.type],e=Go[t];let n=ko[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(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=zo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class MF extends vP{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()||Bo.VEC3;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const EF=\\\\\\\"SSSModel\\\\\\\";const SF=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.thickness=aa.FLOAT(.1),this.power=aa.FLOAT(2),this.scale=aa.FLOAT(16),this.distortion=aa.FLOAT(.1),this.ambient=aa.FLOAT(.4),this.attenuation=aa.FLOAT(.8)}};class CF extends df{constructor(){super(...arguments),this.paramsConfig=SF}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(EF,Bo.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(EF);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()} = ${hf.vector3(this.variableForInputParam(e))};`}}class NF extends yP{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((()=>[Bo.VEC4,Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var LF;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}(LF||(LF={}));const OF=[LF.AXIS,LF.ANGLE],PF={[LF.AXIS]:[0,0,1],[LF.ANGLE]:0};class RF extends xP{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>OF[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return PF[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class IF extends yP{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((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class FF extends yP{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((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}const DF=\\\\\\\"val\\\\\\\";const BF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=aa.FLOAT(0)}};class zF extends df{constructor(){super(...arguments),this.paramsConfig=BF}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 Ho(DF,Bo.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Bo.FLOAT,n=this._uniform_name(),i=this.glVarName(DF),s=new Af(this,Bo.SAMPLER_2D,n);t.addDefinitions(this,[s]);const r=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${r}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.RAMP,this.pv.name,bo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(DF)}}const kF=\\\\\\\"rand\\\\\\\";const UF=new class extends la{constructor(){super(...arguments),this.seed=aa.VECTOR2([1,1])}};class GF extends df{constructor(){super(...arguments),this.paramsConfig=UF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(kF,Bo.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=hf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(kF)} = rand(${n})`;t.addBodyLines(this,[i])}}const VF=new class extends la{constructor(){super(...arguments),this.rgb=aa.VECTOR3([1,1,1])}};class HF extends df{constructor(){super(...arguments),this.paramsConfig=VF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"hsv\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(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=hf.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 jF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(jF||(jF={}));const WF=[jF.AXIS,jF.QUAT],qF={[jF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[jF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},XF={[jF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[jF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},YF={[jF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[jF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},$F={[jF.AXIS]:[Bo.VEC3,Bo.VEC3,Bo.FLOAT],[jF.QUAT]:[Bo.VEC3,Bo.VEC4]},JF={vector:[0,0,1],axis:[0,1,0]};const ZF=new class extends la{constructor(){super(...arguments),this.signature=aa.INTEGER(jF.AXIS,{menu:{entries:WF.map(((t,e)=>({name:qF[t],value:e})))}})}};class QF extends df{constructor(){super(...arguments),this.paramsConfig=ZF}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=WF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=WF[this.pv.signature];return XF[e][t]}paramDefaultValue(t){return JF[t]}gl_method_name(){const t=WF[this.pv.signature];return YF[t]}_expected_input_types(){const t=WF[this.pv.signature];return $F[t]}_expected_output_types(){return[Bo.VEC3]}gl_function_definitions(){return[new Tf(this,wR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${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 KF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class tD extends yP{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=hf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==Vo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const t=KF.map((t=>this._simple_line(`${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 eD=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR3([0,0,0]),this.center=aa.VECTOR3([0,0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class nD extends df{constructor(){super(...arguments),this.paramsConfig=eD}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}const iD=new class extends la{};class sD extends df{constructor(){super(...arguments),this.paramsConfig=iD}static type(){return ns.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${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 rD=new class extends la{};class oD extends df{constructor(){super(...arguments),this.paramsConfig=rD}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?oD.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Bo.FLOAT,e=this.io.connections.inputConnections(),n=e?rr.clamp(e.length,2,16):2,i=[Bo.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(lD.OUTPUT_NAME),i=this._uniform_name(),s=new Af(this,Bo.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 wF,this._param_configs_controller.reset();const t=new $f(Er.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 cD;lD.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(cD||(cD={}));const hD=[cD.POSITION,cD.DIR_VEC];const uD=new class extends la{constructor(){super(...arguments),this.vec=aa.VECTOR3([0,0,0]),this.interpretation=aa.INTEGER(0,{menu:{entries:hD.map(((t,e)=>({name:t,value:e})))}})}};class dD extends df{constructor(){super(...arguments),this.paramsConfig=uD}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=hf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(hD[this.pv.interpretation]){case cD.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case cD.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var pD;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(pD||(pD={}));const _D=[pD.CONDITION,pD.IF_TRUE,pD.IF_FALSE];class mD extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return _D[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Bo.FLOAT;return[Bo.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=hf.bool(this.variableForInput(pD.CONDITION)),s=hf.any(this.variableForInput(pD.IF_TRUE)),r=hf.any(this.variableForInput(pD.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${s}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${r}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const fD=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const gD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:fD.map(((t,e)=>({name:t,value:e})))}})}};class vD extends df{constructor(){super(...arguments),this.paramsConfig=gD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[fD[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return vD.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Mf(this,this.gl_type(),e),i=this.glVarName(vD.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(fD.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}vD.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const yD={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class xD extends(tR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t,t]}_expected_output_types(){return[Bo.VEC4]}paramDefaultValue(t){return yD[t]}}const bD={start:[0,0,1],end:[1,0,0]};class wD extends(WP(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t]}_expected_output_types(){return[Bo.FLOAT]}paramDefaultValue(t){return bD[t]}}const TD={only:[`${OI.context()}/${OI.type()}`,`${vI.context()}/${vI.type()}`,`${wI.context()}/${wI.type()}`]};class AD extends sa{static context(){return ts.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${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 la{};const MD=[jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4];const ED=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:MD.map(((t,e)=>({name:t,value:e})))}})}};class SD extends AD{constructor(){super(...arguments),this.paramsConfig=ED,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((()=>[MD[this.pv.type]]))}get input_name(){return SD.INPUT_NAME}get output_name(){return SD.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(MD.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(SD.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(SD.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())}}SD.INPUT_NAME=\\\\\\\"export\\\\\\\",SD.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const CD=new class extends la{};class ND extends AD{constructor(){super(...arguments),this.paramsConfig=CD}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 LD=new class extends la{};class OD extends AD{constructor(){super(...arguments),this.paramsConfig=LD}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 PD{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 RD;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(RD||(RD={}));class ID{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 PD}}class FD extends ID{constructor(t,e,n){super(RD.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class DD extends Yf{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 Er.BOOLEAN:case Er.BUTTON:return{value:0};case Er.COLOR:return{value:new D.a(0,0,0)};case Er.FLOAT:case Er.FOLDER:case Er.INTEGER:case Er.OPERATOR_PATH:case Er.NODE_PATH:case Er.PARAM_PATH:return{value:0};case Er.RAMP:case Er.STRING:return{value:null};case Er.VECTOR2:return{value:new d.a(0,0)};case Er.VECTOR3:return{value:new p.a(0,0,0)};case Er.VECTOR4:return{value:new _.a(0,0,0,0)}}ls.unreachable(t)}}const BD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(Wo.indexOf(jo.FLOAT),{menu:{entries:Wo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:Wo.indexOf(jo.VEC3)}})}};class zD extends AD{constructor(){super(...arguments),this.paramsConfig=BD,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((()=>[Wo[this.pv.type]]))}setLines(t){const e=[],n=Wo[this.pv.type],i=this.uniform_name();e.push(new FD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=Wo[this.pv.type],e=Yo[t];let n=qo[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new DD(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new DD(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=Wo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class kD extends sa{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 UD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const GD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},VD=Object.keys(GD);function HD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=aa.BOOLEAN(0),this.front=aa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=aa.BOOLEAN(0),this.shadowDoubleSided=aa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=aa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=aa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthWrite=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthTest=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.premultipliedAlpha=aa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=aa.INTEGER(w.xb,{menu:{entries:VD.map((t=>({name:t,value:GD[t]})))}}),this.dithering=aa.BOOLEAN(0),this.polygonOffset=aa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}HD(la);class jD extends UD{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 WD extends(HD(la)){constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.lineWidth=aa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const qD=new WD;class XD extends kD{constructor(){super(...arguments),this.paramsConfig=qD,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new Ts.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 YD(t){return class extends t{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}YD(la);class $D extends UD{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 JD extends Xf{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&&$f.callback(i,s),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(li.playerMode())return this._material}}function ZD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=aa.BOOLEAN(0,{callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}})}}}ZD(la);class QD extends kD{constructor(){super(...arguments),this._children_controller_context=ts.GL,this.persisted_config=new JD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(ts.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function KD(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}KD(la);class tB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function eB(t){return class extends t{constructor(){super(...arguments),this.default=aa.FOLDER(null)}}}function nB(t){return class extends t{constructor(){super(...arguments),this.advanced=aa.FOLDER(null)}}}class iB extends(KD(HD(ZD(nB(YD(eB(la))))))){constructor(){super(...arguments),this.linewidth=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const sB=new iB;class rB extends QD{constructor(){super(...arguments),this.paramsConfig=sB,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_LINE}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function oB(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.useVertexColors=aa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}O.a;oB(la);class aB extends UD{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 lB(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}lB(la);class cB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function hB(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function uB(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 dB extends UD{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 pB(t){return class extends t{constructor(){super(...arguments),this.useMap=aa.BOOLEAN(0,hB(_B)),this.map=aa.NODE_PATH(vi.EMPTY,uB(_B,\\\\\\\"useMap\\\\\\\"))}}}O.a;pB(la);class _B extends dB{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 mB(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(fB)}),this.alphaMap=aa.NODE_PATH(vi.EMPTY,uB(fB,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;mB(la);class fB extends dB{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 gB(t){return class extends t{constructor(){super(...arguments),this.useAOMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(vB)}),this.aoMap=aa.NODE_PATH(vi.EMPTY,uB(vB,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;gB(la);class vB extends dB{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 yB;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(yB||(yB={}));const xB=[yB.MULT,yB.ADD,yB.MIX],bB={[yB.MULT]:w.nb,[yB.ADD]:w.c,[yB.MIX]:w.lb};function wB(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,hB(TB)),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(TB,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=aa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:xB.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}wB(la);class TB extends dB{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=bB[xB[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 AB(t){return class extends t{constructor(){super(...arguments),this.useLightMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(MB)}),this.lightMap=aa.NODE_PATH(vi.EMPTY,uB(MB,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=aa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;AB(la);class MB extends dB{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 EB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(EB||(EB={}));const SB=[EB.ROUND,EB.BUTT,EB.SQUARE];var CB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(CB||(CB={}));const NB=[CB.ROUND,CB.BEVEL,CB.MITER];function LB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=aa.INTEGER(0,{menu:{entries:SB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=aa.INTEGER(0,{menu:{entries:NB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;LB(la);class OB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=SB[n.wireframeLinecap],e.wireframeLinejoin=NB[n.wireframeLinejoin],e.needsUpdate=!0}}function PB(t){return class extends t{constructor(){super(...arguments),this.textures=aa.FOLDER(null)}}}const RB={directParams:!0};class IB extends(lB(LB(HD(nB(AB(wB(gB(mB(pB(PB(oB(eB(la))))))))))))){}const FB=new IB;class DB extends kD{constructor(){super(...arguments),this.paramsConfig=FB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,RB),aoMap:new vB(this,RB),envMap:new TB(this,RB),lightMap:new MB(this,RB),map:new _B(this,RB)},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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function BB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0)}}}BB(la);class zB extends UD{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 kB={uniforms:!0};class UB extends(KD(BB(HD(ZD(nB(wB(gB(mB(pB(PB(YD(eB(la))))))))))))){}const GB=new UB;class VB extends QD{constructor(){super(...arguments),this.paramsConfig=GB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,kB),aoMap:new vB(this,kB),envMap:new TB(this,kB),map:new _B(this,kB)},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();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function HB(t){return class extends t{constructor(){super(...arguments),this.emissive=aa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=aa.BOOLEAN(0,hB(jB)),this.emissiveMap=aa.NODE_PATH(vi.EMPTY,uB(jB,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=aa.FLOAT(1)}}}O.a;HB(la);class jB extends dB{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 WB={directParams:!0};class qB extends(lB(LB(HD(nB(AB(wB(HB(gB(mB(pB(PB(oB(eB(la)))))))))))))){}const XB=new qB;class YB extends kD{constructor(){super(...arguments),this.paramsConfig=XB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,WB),aoMap:new vB(this,WB),emissiveMap:new jB(this,WB),envMap:new TB(this,WB),lightMap:new MB(this,WB),map:new _B(this,WB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new ws.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function $B(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=aa.BOOLEAN(0,{callback:t=>{JB.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=aa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=aa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}$B(la);class JB extends UD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${hf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${hf.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=>JB.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 ZB={uniforms:!0};class QB extends($B(KD(BB(HD(ZD(nB(AB(wB(HB(gB(mB(pB(PB(YD(eB(la)))))))))))))))){}const KB=new QB;class tz extends QD{constructor(){super(...arguments),this.paramsConfig=KB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ZB),aoMap:new vB(this,ZB),emissiveMap:new jB(this,ZB),envMap:new TB(this,ZB),lightMap:new MB(this,ZB),map:new _B(this,ZB),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_LAMBERT}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function ez(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(nz)}),this.bumpMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(nz,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;ez(la);class nz extends dB{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 iz;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(iz||(iz={}));const sz=[iz.TANGENT,iz.OBJECT],rz={[iz.TANGENT]:w.Uc,[iz.OBJECT]:w.zb};function oz(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(az)}),this.normalMap=aa.NODE_PATH(vi.EMPTY,uB(az,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=aa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:sz.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=aa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;oz(la);class az extends dB{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=rz[sz[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 lz(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(cz)}),this.displacementMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;lz(la);class cz extends dB{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 hz(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=aa.BOOLEAN(0,hB(uz)),this.matcapMap=aa.NODE_PATH(vi.EMPTY,uB(uz,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;hz(la);class uz extends dB{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 dz={directParams:!0};class pz extends(lB(HD(nB(oz(lz(ez(mB(pB(hz(PB(oB(eB(la))))))))))))){}const _z=new pz;class mz extends kD{constructor(){super(...arguments),this.paramsConfig=_z,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,dz),bumpMap:new nz(this,dz),displacementMap:new cz(this,dz),map:new _B(this,dz),matcap:new uz(this,dz),normalMap:new az(this,dz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({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();aB.update(this),cB.update(this),this.setMaterial(this.material)}}const fz={directParams:!0};class gz extends(lB(HD(oz(lz(ez(PB(eB(la)))))))){}const vz=new gz;class yz extends kD{constructor(){super(...arguments),this.paramsConfig=vz,this.controllers={advancedCommon:new jD(this),bumpMap:new nz(this,fz),displacementMap:new cz(this,fz),normalMap:new az(this,fz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshNormal\\\\\\\"}createMaterial(){return new Hf({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();cB.update(this),this.setMaterial(this.material)}}function xz(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=aa.BOOLEAN(0,hB(bz)),this.specularMap=aa.NODE_PATH(vi.EMPTY,uB(bz,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;xz(la);class bz extends dB{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 wz={directParams:!0};class Tz extends(lB(LB(HD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){constructor(){super(...arguments),this.flatShading=aa.BOOLEAN(0)}}const Az=new Tz;class Mz extends kD{constructor(){super(...arguments),this.paramsConfig=Az,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,wz),aoMap:new vB(this,wz),bumpMap:new nz(this,wz),displacementMap:new cz(this,wz),emissiveMap:new jB(this,wz),envMap:new TB(this,wz),lightMap:new MB(this,wz),map:new _B(this,wz),normalMap:new az(this,wz),specularMap:new bz(this,wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.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();aB.update(this),cB.update(this),OB.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Ez={uniforms:!0};class Sz extends($B(KD(BB(HD(ZD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Cz=new Sz;class Nz extends QD{constructor(){super(...arguments),this.paramsConfig=Cz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Ez),aoMap:new vB(this,Ez),bumpMap:new nz(this,Ez),displacementMap:new cz(this,Ez),emissiveMap:new jB(this,Ez),envMap:new TB(this,Ez),lightMap:new MB(this,Ez),map:new _B(this,Ez),normalMap:new az(this,Ez),specularMap:new bz(this,Ez),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHONG}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Lz(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Oz)}),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(Oz,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Lz(la);class Oz extends dB{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 Pz(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.metalnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=aa.FLOAT(1),this.useRoughnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.roughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=aa.FLOAT(.5)}}}O.a;Pz(la);class Rz extends dB{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 Iz(t){return class extends t{constructor(){super(...arguments),this.clearcoat=aa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatNormalMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=aa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=aa.FLOAT(0),this.useClearCoatRoughnessMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatRoughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=aa.BOOLEAN(0),this.sheen=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=aa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=aa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=aa.BOOLEAN(0),this.transmissionMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.ior=aa.FLOAT(1.5,{range:[1,2.3333],rangeLocked:[!0,!0]}),this.thickness=aa.FLOAT(.01,{range:[0,10],rangeLocked:[!0,!1]}),this.useThicknessMap=aa.BOOLEAN(0),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1]}),this.attenuationColor=aa.COLOR([1,1,1])}}}Iz(la);const Fz=new Uf.a;class Dz extends dB{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;Fz.ior=t.ior;const e=Fz.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 Bz={directParams:!0};class zz extends(lB(LB(HD(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))))){}const kz=new zz;class Uz extends kD{constructor(){super(...arguments),this.paramsConfig=kz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Bz),aoMap:new vB(this,Bz),bumpMap:new nz(this,Bz),displacementMap:new cz(this,Bz),emissiveMap:new jB(this,Bz),envMap:new Oz(this,Bz),lightMap:new MB(this,Bz),map:new _B(this,Bz),metalnessRoughnessMap:new Rz(this,Bz),normalMap:new az(this,Bz),physical:new Dz(this,Bz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const Gz={uniforms:!0};class Vz extends(function(t){return class extends($B(KD(BB(HD(ZD(t)))))){}}(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la))))))))))))))))){}const Hz=new Vz;class jz extends QD{constructor(){super(...arguments),this.paramsConfig=Hz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Gz),aoMap:new vB(this,Gz),bumpMap:new nz(this,Gz),displacementMap:new cz(this,Gz),emissiveMap:new jB(this,Gz),envMap:new Oz(this,{uniforms:!0,directParams:!0}),lightMap:new MB(this,Gz),map:new _B(this,Gz),metalnessRoughnessMap:new Rz(this,{uniforms:!0,directParams:!0}),normalMap:new az(this,Gz),physical:new Dz(this,{uniforms:!0,directParams:!0}),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHYSICAL}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}createMaterial(){const t=super.createMaterial();return t.isMeshStandardMaterial=!0,t.isMeshPhysicalMaterial=!0,t}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Wz={directParams:!0};class qz extends(lB(LB(HD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){}const Xz=new qz;class Yz extends kD{constructor(){super(...arguments),this.paramsConfig=Xz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Wz),aoMap:new vB(this,Wz),bumpMap:new nz(this,Wz),displacementMap:new cz(this,Wz),emissiveMap:new jB(this,Wz),envMap:new Oz(this,Wz),lightMap:new MB(this,Wz),map:new _B(this,Wz),metalnessRoughnessMap:new Rz(this,Wz),normalMap:new az(this,Wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new bs.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const $z={uniforms:!0};class Jz extends($B(KD(BB(HD(ZD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Zz=new Jz;class Qz extends QD{constructor(){super(...arguments),this.paramsConfig=Zz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,$z),aoMap:new vB(this,$z),bumpMap:new nz(this,$z),displacementMap:new cz(this,$z),emissiveMap:new jB(this,$z),envMap:new Oz(this,$z),lightMap:new MB(this,$z),map:new _B(this,$z),metalnessRoughnessMap:new Rz(this,$z),normalMap:new az(this,$z),PCSS:new JB(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();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Kz=U.meshphong_frag.slice(0,U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),tk=U.meshphong_frag.slice(U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),ek={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\\\\\\\",Kz,\\\\\\\"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;\\\\\\\",\\\\\\\"}\\\\\\\",tk.replace(\\\\\\\"#include \\\\\\\",(nk=U.lights_fragment_begin,ik=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",sk=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),nk.split(ik).join(sk)))].join(\\\\\\\"\\\\n\\\\\\\")};var nk,ik,sk;function rk(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function ok(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const ak={uniforms:!0};class lk extends(lB(BB(HD(nB(mB(pB(PB(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=aa.COLOR([1,1,1],{...rk(\\\\\\\"diffuse\\\\\\\")}),this.shininess=aa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{nodeSelection:{context:ts.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=aa.COLOR([.5,.3,0],{...rk(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=aa.FLOAT(.1,{...ok(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=aa.FLOAT(.4,{...ok(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=aa.FLOAT(.8,{...ok(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=aa.FLOAT(2,{range:[0,10],...ok(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=aa.FLOAT(16,{range:[0,100],...ok(\\\\\\\"thicknessScale\\\\\\\")})}}}(eB(la)))))))))){}const ck=new lk;class hk extends kD{constructor(){super(...arguments),this.paramsConfig=ck,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ak),map:new _B(this,ak)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(ek.uniforms),e=new F({uniforms:t,vertexShader:ek.vertexShader,fragmentShader:ek.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();cB.update(this),zB.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 uk(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=aa.BOOLEAN(0,hB(dk)),this.gradientMap=aa.NODE_PATH(vi.EMPTY,uB(dk,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;uk(la);class dk extends dB{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 pk={directParams:!0};class _k extends(lB(LB(HD(nB(oz(AB(uk(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))){}const mk=new _k;class fk extends kD{constructor(){super(...arguments),this.paramsConfig=mk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,pk),aoMap:new vB(this,pk),bumpMap:new nz(this,pk),displacementMap:new cz(this,pk),emissiveMap:new jB(this,pk),gradientMap:new dk(this,pk),lightMap:new MB(this,pk),map:new _B(this,pk),normalMap:new az(this,pk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const gk={directParams:!0};class vk extends(KD(HD(nB(mB(pB(PB(oB(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1),this.sizeAttenuation=aa.BOOLEAN(1)}}}(eB(la)))))))))){}const yk=new vk;class xk extends kD{constructor(){super(...arguments),this.paramsConfig=yk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,gk),map:new _B(this,gk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new xs.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();aB.update(this),tB.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class bk extends(KD(HD(ZD(nB(YD(eB(la))))))){}const wk=new bk;class Tk extends QD{constructor(){super(...arguments),this.paramsConfig=wk,this.controllers={advancedCommon:new jD(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();$D.update(this),tB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class Ak extends(HD(oB(la))){}const Mk=new Ak;class Ek extends kD{constructor(){super(...arguments),this.paramsConfig=Mk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new zf({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();aB.update(this),this.setMaterial(this.material)}}class Sk extends B.a{constructor(){const t=Sk.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)}}Sk.prototype.isSky=!0,Sk.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 Ck=new class extends la{constructor(){super(...arguments),this.turbidity=aa.FLOAT(2,{range:[0,20]}),this.rayleigh=aa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=aa.FLOAT(.005),this.mieDirectional=aa.FLOAT(.8),this.inclination=aa.FLOAT(.5),this.azimuth=aa.FLOAT(.25),this.up=aa.VECTOR3([0,1,0])}};class Nk extends kD{constructor(){super(...arguments),this.paramsConfig=Ck}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new Sk).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 Lk=\\\\\\\"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}\\\\\\\",Ok=\\\\\\\"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 Pk={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 Rk(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.stepSize=aa.FLOAT(.01),this.density=aa.FLOAT(1),this.shadowDensity=aa.FLOAT(1),this.lightDir=aa.VECTOR3([-1,-1,-1])}}}Rk(la);class Ik{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)}}Ik._object_bbox=new Ay.a;class Fk extends(Rk(la)){}const Dk=new Fk;class Bk extends kD{constructor(){super(...arguments),this.paramsConfig=Dk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:Lk,fragmentShader:Ok,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(Pk)});return gr.add_user_data_render_hook(t,Ik.render_hook.bind(Ik)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class zk extends(ZD(Rk(la))){}const kk=new zk;class Uk extends QD{constructor(){super(...arguments),this.paramsConfig=kk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return jn.GL_VOLUME}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class Gk extends sa{static context(){return ts.MAT}cook(){this.cookController.endCook()}}class Vk extends Gk{}class Hk extends Vk{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 jk extends Vk{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 Wk extends Vk{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 qk extends Vk{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 Xk extends Gk{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 Yk extends Vk{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 $k=n(87);const Jk=\\\\\\\"parent object\\\\\\\",Zk=[Jk,Jk,Jk,Jk];var Qk;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(Qk||(Qk={}));class Kk extends sa{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return ts.OBJ}static displayedInputNames(){return Zk}_create_children_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new K.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class tU extends Kk{constructor(){super(...arguments),this.flags=new Di(this),this.renderOrder=Qk.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const eU=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1)}};class nU extends tU{constructor(){super(...arguments),this.paramsConfig=eU}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new $k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class iU extends sv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}iU.prototype.isRectAreaLight=!0;var sU,rU=n(61);class oU{static init(){const 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pU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class _U{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 uU,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(pU)||this.node.dirtyController.addPostDirtyHook(pU,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,cU[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={}){uU.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),uU.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(),uU.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 mU{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&&_U.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(){mU.on_input_updated(this.node)}}dU(la);class fU extends tU{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(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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lt.a({side:w.i,fog:!1})))}updateMatrixWorld(){if(this.scale.set(.5*this.light.width,.5*this.light.height,1),void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);const t=this.material.color,e=Math.max(t.r,t.g,t.b);e>1&&t.multiplyScalar(1/e),this.children[0].material.color.copy(this.material.color)}this.matrixWorld.extractRotation(this.light.matrixWorld).scale(this.scale).copyPosition(this.light.matrixWorld),this.children[0].matrixWorld.copy(this.matrixWorld)}dispose(){this.geometry.dispose(),this.material.dispose(),this.children[0].geometry.dispose(),this.children[0].material.dispose()}}xU=la;var xU;class bU{constructor(t,e){this.node=t,this._name=e,this._object=this.createObject(),this._material=new lt.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class wU extends bU{createObject(){return new yU(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class TU extends(function(t){return class extends t{constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1,{range:[0,10]}),this.width=aa.FLOAT(1,{range:[0,10]}),this.height=aa.FLOAT(1,{range:[0,10]}),this.showHelper=aa.BOOLEAN(0)}}}(dU(la))){}const AU=new TU;class MU extends fU{constructor(){super(...arguments),this.paramsConfig=AU,this._helperController=new gU(this,wU,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new iU(16777215,1,1,1);return t.matrixAutoUpdate=!1,oU.initialized||(oU.init(),oU.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 EU=n(72);const SU=new p.a,CU=new tf.a;class NU extends As.a{constructor(t){const e=new S.a,n=new Ts.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;CU.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),LU(\\\\\\\"c\\\\\\\",e,t,CU,0,0,-1),LU(\\\\\\\"t\\\\\\\",e,t,CU,0,0,1),LU(\\\\\\\"n1\\\\\\\",e,t,CU,-1,-1,-1),LU(\\\\\\\"n2\\\\\\\",e,t,CU,1,-1,-1),LU(\\\\\\\"n3\\\\\\\",e,t,CU,-1,1,-1),LU(\\\\\\\"n4\\\\\\\",e,t,CU,1,1,-1),LU(\\\\\\\"f1\\\\\\\",e,t,CU,-1,-1,1),LU(\\\\\\\"f2\\\\\\\",e,t,CU,1,-1,1),LU(\\\\\\\"f3\\\\\\\",e,t,CU,-1,1,1),LU(\\\\\\\"f4\\\\\\\",e,t,CU,1,1,1),LU(\\\\\\\"u1\\\\\\\",e,t,CU,.7,1.1,-1),LU(\\\\\\\"u2\\\\\\\",e,t,CU,-.7,1.1,-1),LU(\\\\\\\"u3\\\\\\\",e,t,CU,0,2,-1),LU(\\\\\\\"cf1\\\\\\\",e,t,CU,-1,0,1),LU(\\\\\\\"cf2\\\\\\\",e,t,CU,1,0,1),LU(\\\\\\\"cf3\\\\\\\",e,t,CU,0,-1,1),LU(\\\\\\\"cf4\\\\\\\",e,t,CU,0,1,1),LU(\\\\\\\"cn1\\\\\\\",e,t,CU,-1,0,-1),LU(\\\\\\\"cn2\\\\\\\",e,t,CU,1,0,-1),LU(\\\\\\\"cn3\\\\\\\",e,t,CU,0,-1,-1),LU(\\\\\\\"cn4\\\\\\\",e,t,CU,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function LU(t,e,n,i,s,r,o){SU.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 zU(t.vertices,t.indices,t.radius,t.details)}}class kU extends zU{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 kU(t.radius,t.detail)}}class UU extends bU{constructor(){super(...arguments),this._geometry=new kU(1),this._quat=new ah.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 QU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class KU extends QU{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 KU(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class tG{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;ZU.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 KU(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 eG extends(dU(la)){constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1),this.angle=aa.FLOAT(45,{range:[0,180]}),this.penumbra=aa.FLOAT(.1),this.decay=aa.FLOAT(.1,{range:[0,1]}),this.distance=aa.FLOAT(100,{range:[0,100]}),this.showHelper=aa.BOOLEAN(0),this.helperSize=aa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=aa.FOLDER(),this.castShadow=aa.BOOLEAN(1),this.shadowAutoUpdate=aa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=aa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=aa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=aa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=aa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=aa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=aa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=aa.FOLDER(),this.tvolumetric=aa.BOOLEAN(0),this.volAttenuation=aa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=aa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const nG=new eG;class iG extends fU{constructor(){super(...arguments),this.paramsConfig=nG,this._helperController=new gU(this,ZU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new tG(this)}static type(){return PU.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new JU.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 sG;const rG=function(){return void 0===sG&&(sG=new(window.AudioContext||window.webkitAudioContext)),sG},oG=new p.a,aG=new ah.a,lG=new p.a,cG=new p.a;class hG extends K.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=rG(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}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(oG,aG,lG),cG.set(0,0,-1).applyQuaternion(aG),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(oG.x,t),e.positionY.linearRampToValueAtTime(oG.y,t),e.positionZ.linearRampToValueAtTime(oG.z,t),e.forwardX.linearRampToValueAtTime(cG.x,t),e.forwardY.linearRampToValueAtTime(cG.y,t),e.forwardZ.linearRampToValueAtTime(cG.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(oG.x,oG.y,oG.z),e.setOrientation(cG.x,cG.y,cG.z,n.x,n.y,n.z)}}class uG extends(dU(la)){}const dG=new uG;class pG extends Kk{constructor(){super(...arguments),this.paramsConfig=dG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new hG;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class _G extends As.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 Ts.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 mG;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(mG||(mG={}));const fG=[mG.TOGETHER,mG.SEPARATELY];const gG=new class extends la{constructor(){super(...arguments),this.object0=aa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.object1=aa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.mode=aa.INTEGER(fG.indexOf(mG.TOGETHER),{menu:{entries:fG.map(((t,e)=>({name:t,value:e})))}}),this.blend=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class vG extends Kk{constructor(){super(...arguments),this.paramsConfig=gG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._t0=new p.a,this._q0=new ah.a,this._s0=new p.a,this._t1=new p.a,this._q1=new ah.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=fG[this.pv.mode];switch(n){case mG.TOGETHER:return this._blend_together(t,e);case mG.SEPARATELY:return this._blend_separately(t,e)}ls.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 yG={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 xG={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}\\\\\\\"},bG=1/256e3;class wG{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(yG),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(xG),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*bG,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*bG,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var TG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(TG||(TG={}));const AG=[TG.ON_RENDER,TG.MANUAL];class MG extends(dU(la)){constructor(){super(...arguments),this.shadow=aa.FOLDER(),this.dist=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=aa.VECTOR2([1,1]),this.shadowRes=aa.VECTOR2([256,256]),this.blur=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=aa.BOOLEAN(1),this.blur2=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=aa.FLOAT(1),this.opacity=aa.FLOAT(1),this.showHelper=aa.BOOLEAN(0),this.updateMode=aa.INTEGER(AG.indexOf(TG.ON_RENDER),{callback:t=>{CG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:AG.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:AG.indexOf(TG.MANUAL)}}),this.scene=aa.FOLDER(),this.include=aa.STRING(\\\\\\\"\\\\\\\"),this.exclude=aa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const EG=new MG,SG=new d.a(256,256);class CG extends Kk{constructor(){super(...arguments),this.paramsConfig=EG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._renderTarget=this._createRenderTarget(SG),this._coreRenderBlur=this._createCoreRenderBlur(SG),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new _U(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 wG(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 NU(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=AG[this.pv.updateMode];switch(t){case TG.ON_RENDER:return this._addRenderHook();case TG.MANUAL:return this._removeRenderHook()}ls.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 NG=\\\\\\\"display\\\\\\\";class LG{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(NG),i=this.node.params.boolean(NG),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 OG extends(dU(la)){constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.renderOrder=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const PG=new OG;class RG extends Kk{constructor(){super(...arguments),this.paramsConfig=PG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.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 IG extends(dU(la)){}const FG=new IG;class DG extends Kk{constructor(){super(...arguments),this.paramsConfig=FG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._helper=new _G(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 BG=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR3([0,0,0]),this.longitude=aa.FLOAT(0,{range:[0,360]}),this.latitude=aa.FLOAT(0,{range:[-180,180]}),this.depth=aa.FLOAT(1,{range:[0,10]})}},zG=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",kG=new p.a(0,1,0),UG=new p.a(-1,0,0);class GG extends Kk{constructor(){super(...arguments),this.paramsConfig=BG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(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 ah.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(zG)||this.dirtyController.addPostDirtyHook(zG,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(kG,Object(On.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(UG,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 VG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=JG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||El.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=os.escapeLineBreaks(t)}}class HG{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=JG.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=VG.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=JG.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=JG.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 WG 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=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class XG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class YG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class $G extends HG{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 JG{static dispatch_node(t){return t.polyNodeController?new $G(t):new HG(t)}static dispatch_param(t){return t instanceof io?new WG(t):t instanceof _o?new qG(t):t instanceof wo?new XG(t):t instanceof bo?new YG(t):new jG(t)}}class ZG{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 ZG;if(this._objects){const e=[];for(let t of this._objects)e.push(yr.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 yr(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=_r.pointsCount(t.geometry)),{type:Ls(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 _r(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 _r(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+=_r.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=os.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new ZG;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=>os.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){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 Ay.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 Hs.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=ZG.geometryFromObject(n);if(i){new _r(i).renameAttrib(t,e)}}));break;case Hs.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new yr(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=os.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(os.matchMask(t,i))n.push(t);else{t==qs.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=qs.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=yr.clone(t);e.add(n)})),e}}class QG extends Fl{static context(){return ts.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new ZG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Cs.MESH){const n=QG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return QG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Ns[e])(t,n=n||Hs.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){QG.createIndexIfNone(t)}static createIndexIfNone(t){dr.createIndexIfNone(t)}}var KG;!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\\\\\\\"}(KG||(KG={}));const tV=\\\\\\\"input geometry\\\\\\\",eV=[tV,tV,tV,tV];class nV extends sa{constructor(){super(...arguments),this.flags=new Ui(this)}static context(){return ts.SOP}static displayedInputNames(){return eV}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,KG.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],KG.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,KG.FROM_SET_OBJECTS)}setGeometry(t,e=Cs.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],KG.FROM_SET_GEOMETRY)}setGeometries(t,e=Cs.MESH){const n=[];let i;for(let s of t)i=this.createObject(s,e),n.push(i);this._setContainerObjects(n,KG.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new ZG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return QG.createObject(t,e,n)}createObject(t,e,n){return nV.createObject(t,e,n)}static createIndexIfNone(t){QG.createIndexIfNone(t)}_createIndexIfNone(t){nV.createIndexIfNone(t)}}const iV=new class extends la{};class sV extends nV{constructor(){super(...arguments),this.paramsConfig=iV}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 rV extends nV{constructor(){super(...arguments),this.childrenDisplayController=new aV(this),this.displayNodeController=new Lm(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 oV={dependsOnDisplayNode:!0};class aV{constructor(t,e=oV){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(sV.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 lV(t,e){const n=new class extends la{constructor(){super(...arguments),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends rV{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new uV(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const cV=lV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.SOP,inputs:[0,4]});class hV extends cV{}class uV{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 Xl({}),i=new kl(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=JG.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 lV(t,n);case ts.OBJ:return dV(t,n)}}}function dV(t,e){const n=new class extends la{constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends Kk{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new mU(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this.polyNodeController=new uV(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 pV=dV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.OBJ});class _V extends pV{}class mV 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 MV;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(MV||(MV={}));const EV=[MV.LINEAR,MV.INVERSE,MV.EXPONENTIAL];class SV extends(dU(la)){constructor(){super(...arguments),this.audio=aa.FOLDER(),this.listener=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=aa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Or.AUDIO]}}),this.volume=aa.FLOAT(1),this.loop=aa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=aa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=aa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=aa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=aa.INTEGER(EV.indexOf(MV.LINEAR),{menu:{entries:EV.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=aa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=aa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0),this.play=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_pause(t)}})}}const CV=new SV;class NV extends Kk{constructor(){super(...arguments),this.paramsConfig=CV,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.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()}))}_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(EV[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.update()))}_createHelper(t){const e=new bV(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 xV(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new AV(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 LV;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(LV||(LV={}));const OV=[LV.ON_RENDER,LV.MANUAL];const PV=new class extends la{constructor(){super(...arguments),this.object=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{RV.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=aa.INTEGER(0,{range:[0,100]}),this.updateMode=aa.INTEGER(OV.indexOf(LV.ON_RENDER),{callback:t=>{RV.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:OV.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{RV.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:OV.indexOf(LV.MANUAL)}})}};class RV extends Kk{constructor(){super(...arguments),this.paramsConfig=PV,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(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=OV[this.pv.updateMode];switch(t){case LV.ON_RENDER:return this._add_render_hook();case LV.MANUAL:return this._remove_render_hook()}ls.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()==RG.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 IV extends(Na(Ea(ya(fa(ua(la)))))){}const FV=new IV;class DV extends Kk{constructor(){super(...arguments),this.paramsConfig=FV,this.hierarchyController=new mU(this),this.SceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.SceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new gs;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 BV{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 zV=\\\\\\\"controls\\\\\\\";class kV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(zV)?this.node.params.get(zV):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(_s.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 BV(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=OH[this.node.pv.updateFromControlsMode];switch(e){case LH.ON_END:return this._set_controls_events_to_update_on_end(t);case LH.ALWAYS:return this._set_controls_events_to_update_always(t);case LH.NEVER:return this._reset(t)}ls.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 UV(t){return class extends t{constructor(){super(...arguments),this.layer=aa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class GV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const VV={callback:t=>{UH.PARAM_CALLBACK_reset_effects_composer(t)}};function HV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=aa.BOOLEAN(0),this.postProcessNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[es.POST]},...VV})}}}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 WV extends sa{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,XV,YV,$V;!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\\\\\\\"}(XV||(XV={})),($V=YV||(YV={}))[$V.Linear=w.U]=\\\\\\\"Linear\\\\\\\",$V[$V.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",$V[$V.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",$V[$V.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",$V[$V.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",$V[$V.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",$V[$V.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",$V[$V.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const JV=[XV.Linear,XV.sRGB,XV.Gamma,XV.RGBE,XV.LogLuv,XV.RGBM7,XV.RGBM16,XV.RGBD],ZV=[YV.Linear,YV.sRGB,YV.Gamma,YV.RGBE,YV.LogLuv,YV.RGBM7,YV.RGBM16,YV.RGBD],QV=YV.sRGB;var KV,tH,eH;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(KV||(KV={})),(eH=tH||(tH={}))[eH.No=w.vb]=\\\\\\\"No\\\\\\\",eH[eH.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",eH[eH.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",eH[eH.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",eH[eH.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const nH=[KV.No,KV.Linear,KV.Reinhard,KV.Cineon,KV.ACESFilmic],iH=[tH.No,tH.Linear,tH.Reinhard,tH.Cineon,tH.ACESFilmic],sH=tH.ACESFilmic,rH=nH.map(((t,e)=>({name:t,value:iH[e]})));var oH;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(oH||(oH={}));const aH=[oH.HIGH,oH.MEDIUM,oH.LOW];var lH;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(lH||(lH={}));const cH=[lH.HIGH,lH.LOW,lH.DEFAULT];var hH,uH,dH;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(hH||(hH={})),(dH=uH||(uH={}))[dH.Basic=w.k]=\\\\\\\"Basic\\\\\\\",dH[dH.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",dH[dH.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",dH[dH.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const pH=[hH.Basic,hH.PCF,hH.PCFSoft,hH.VSM],_H=[uH.Basic,uH.PCF,uH.PCFSoft,uH.VSM],mH=(w.k,w.Fb,w.Gb,w.gd,uH.PCFSoft),fH={alpha:!1,precision:oH.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:lH.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const gH=new class extends la{constructor(){super(...arguments),this.tprecision=aa.BOOLEAN(0),this.precision=aa.INTEGER(aH.indexOf(oH.HIGH),{visibleIf:{tprecision:1},menu:{entries:aH.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=aa.BOOLEAN(0),this.powerPreference=aa.INTEGER(cH.indexOf(lH.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:cH.map(((t,e)=>({value:e,name:t})))}}),this.alpha=aa.BOOLEAN(1),this.premultipliedAlpha=aa.BOOLEAN(1),this.antialias=aa.BOOLEAN(1),this.stencil=aa.BOOLEAN(1),this.depth=aa.BOOLEAN(1),this.logarithmicDepthBuffer=aa.BOOLEAN(0),this.toneMapping=aa.INTEGER(sH,{menu:{entries:rH}}),this.toneMappingExposure=aa.FLOAT(1,{range:[0,2]}),this.outputEncoding=aa.INTEGER(QV,{menu:{entries:JV.map(((t,e)=>({name:t,value:ZV[e]})))}}),this.physicallyCorrectLights=aa.BOOLEAN(1),this.sortObjects=aa.BOOLEAN(1),this.tpixelRatio=aa.BOOLEAN(0),this.pixelRatio=aa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=aa.BOOLEAN(1),this.shadowMapAutoUpdate=aa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=aa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=aa.INTEGER(mH,{visibleIf:{tshadowMap:1},menu:{entries:pH.map(((t,e)=>({name:t,value:_H[e]})))}})}};class vH extends WV{constructor(){super(...arguments),this.paramsConfig=gH,this._renderers_by_canvas_id={}}static type(){return qV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(fH);let s;for(s of i)n[s]=fH[s];if(this.pv.tprecision){const t=aH[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=cH[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 '${this.path()}'`),li.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[t.id]=r,r}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:xH.defaultPixelRatio();li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`set renderer pixelRatio from '${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 yH(t){return class extends t{constructor(){super(...arguments),this.render=aa.FOLDER(),this.setScene=aa.BOOLEAN(0),this.scene=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]}}),this.setRenderer=aa.BOOLEAN(0),this.renderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:ts.ROP,types:[vH.type()]}}),this.setCSSRenderer=aa.BOOLEAN(0),this.CSSRenderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:ts.ROP,types:[qV.CSS2D,qV.CSS3D]}})}}}class xH{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){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),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)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,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,DV.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=xH._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 Zf.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=mH,i.physicallyCorrectLights=!0,i.toneMapping=sH,i.toneMappingExposure=1,i.outputEncoding=QV,li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),li.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}delete_renderer(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 bH{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 wH{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 TH{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 AH=\\\\\\\"hovered\\\\\\\";class MH{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=MH._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new wH(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Va(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new TH(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(AH)}setContainerClassHovered(){this.domElement().classList.add(AH)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}MH._next_viewer_id=0;class EH extends MH{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._clock=new Om,this._delta=0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${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 bH(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;if(this._do_render){if(this._delta=this._clock.getDelta(),this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(this._scene.timeController.incrementTimeIfPlaying(this._delta),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta);this.render(this._delta),null===(t=this._controls_controller)||void 0===t||t.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.delete_renderer(this._canvas)}render(t){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let e of this._onBeforeRenderCallbacks)e(t);const e=this.camerasController.size,n=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,e,n),this._onAfterRenderCallbacks)for(let e of this._onAfterRenderCallbacks)e(t)}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const SH={type:\\\\\\\"change\\\\\\\"},CH=1,NH=100;var LH;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(LH||(LH={}));const OH=[LH.ON_END,LH.ALWAYS,LH.NEVER];function PH(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=aa.BUTTON(null,{callback:(t,e)=>{kH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function RH(t){return class extends t{constructor(){super(...arguments),this.camera=aa.FOLDER(),this.controls=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.EVENT}}),this.updateFromControlsMode=aa.INTEGER(OH.indexOf(LH.ON_END),{menu:{entries:OH.map(((t,e)=>({name:t,value:e})))}}),this.near=aa.FLOAT(CH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=aa.FLOAT(NH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0)}}}var IH;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}(IH||(IH={}));const FH=[IH.DEFAULT,IH.COVER,IH.CONTAIN];function DH(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=aa.INTEGER(FH.indexOf(IH.DEFAULT),{menu:{entries:FH.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=aa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:FH.indexOf(IH.COVER)},{fovAdjustMode:FH.indexOf(IH.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}PH(la);HV(yH(dU(UV(RH(PH(la))))));class BH extends Kk{constructor(){super(...arguments),this.renderOrder=Qk.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(SH),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(){uU.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class zH extends BH{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new kV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new GV(this)}get renderController(){return this._render_controller=this._render_controller||new xH(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(SH),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 EH(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 NU(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 kH extends BH{}class UH extends zH{PARAM_CALLBACK_update_effects_composer(t){}}const GH=-.5,VH=.5,HH=.5,jH=-.5;class WH extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1)}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const qH=new WH;class XH extends zH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return is.ORTHOGRAPHIC}createObject(){return new ot.a(2*GH,2*VH,2*HH,2*jH,CH,NH)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.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=GH*e*1,this._object.right=VH*e*1,this._object.top=HH*t*1,this._object.bottom=jH*t*1}}const YH=50;class $H extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.fov=aa.FLOAT(YH,{range:[0,100]})}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const JH=new $H;class ZH extends zH{constructor(){super(...arguments),this.paramsConfig=JH}static type(){return is.PERSPECTIVE}createObject(){return new tt.a(YH,1,CH,NH)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}}}class QH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=aa.FOLDER(),this.resolution=aa.INTEGER(256),this.excludedObjects=aa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_printResolve(t)}}),this.near=aa.FLOAT(1),this.far=aa.FLOAT(100),this.render=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_render(t)}}),this.renderTarget=aa.FOLDER(),this.tencoding=aa.BOOLEAN(0),this.encoding=aa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(dU(la))){}const KH=new QH;class tj extends Kk{constructor(){super(...arguments),this.paramsConfig=KH,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new _G(1)}static type(){return Ng.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 ej extends Kk{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class nj extends ej{}class ij extends nj{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 sj extends ij{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER}}class rj extends nj{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 oj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.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 aj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.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 lj extends ej{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 cj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.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 hj=[\\\\\\\"input pass\\\\\\\"];const uj={cook:!1,callback:function(t,e){dj.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class dj extends sa{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 hj}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 pj={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 _j={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 mj extends Im{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===Rm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Rm;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===pj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(pj.uniforms),vertexShader:pj.vertexShader,fragmentShader:pj.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===_j&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(_j.uniforms),vertexShader:_j.vertexShader,fragmentShader:_j.fragmentShader,blending:w.ub}),this.fsQuad=new Bm(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 fj=new class extends la{constructor(){super(...arguments),this.adaptive=aa.BOOLEAN(1,{...uj}),this.averageLuminance=aa.FLOAT(.7,{...uj}),this.midGrey=aa.FLOAT(.04,{...uj}),this.maxLuminance=aa.FLOAT(16,{range:[0,20],...uj}),this.adaptiveRange=aa.FLOAT(2,{range:[0,10],...uj})}};class gj extends dj{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new mj(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 vj={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 yj extends Im{constructor(t=.96){super(),void 0===vj&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=vj,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 Bm(this.shaderMaterial);const e=new lt.a;this.copyFsQuad=new Bm(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 xj=new class extends la{constructor(){super(...arguments),this.damp=aa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...uj})}};class bj extends dj{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new yj;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const wj={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 Tj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...uj})}};class Aj extends dj{constructor(){super(...arguments),this.paramsConfig=Tj}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new zm(wj);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const Mj={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 Ej=new class extends la{constructor(){super(...arguments),this.brightness=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.contrast=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Sj extends dj{constructor(){super(...arguments),this.paramsConfig=Ej}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new zm(Mj);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 Cj extends Im{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 Nj=new class extends la{};class Lj extends dj{constructor(){super(...arguments),this.paramsConfig=Nj}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new Cj;return this.updatePass(e),e}updatePass(t){}}const Oj=new class extends la{};class Pj extends dj{constructor(){super(...arguments),this.paramsConfig=Oj}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const Rj={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 Ij=new class extends la{constructor(){super(...arguments),this.pow=aa.VECTOR3([2,2,2],{...uj}),this.mult=aa.COLOR([1,1,1],{...uj}),this.add=aa.COLOR([0,0,0],{...uj})}};class Fj extends dj{constructor(){super(...arguments),this.paramsConfig=Ij}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new zm(Rj);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 Dj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Bj extends dj{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new zm(Rm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const zj={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}\\\\\\\"},kj={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 Uj{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=_r.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 Gj{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 gs,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new Uj(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=zj;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=kj;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=Hj.smoothstep(e.near,e.far,t.pv.focalDepth),i=Hj.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 Vj=new class extends la{constructor(){super(...arguments),this.focalDepth=aa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...uj}),this.fStep=aa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...uj}),this.maxBlur=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.vignetting=aa.BOOLEAN(0,{...uj}),this.depthBlur=aa.BOOLEAN(0,{...uj}),this.threshold=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...uj}),this.gain=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...uj}),this.bias=aa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...uj}),this.fringe=aa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...uj}),this.noise=aa.BOOLEAN(0,{...uj}),this.dithering=aa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...uj}),this.pentagon=aa.BOOLEAN(0,{...uj}),this.rings=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...uj}),this.samples=aa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...uj}),this.clearColor=aa.COLOR([1,1,1],{...uj})}};class Hj extends dj{constructor(){super(...arguments),this.paramsConfig=Vj}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 Gj(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 Wj=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR2([.5,.5],{...uj}),this.angle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...uj}),this.scale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj})}};class qj extends dj{constructor(){super(...arguments),this.paramsConfig=Wj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new zm(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 Xj={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 Yj extends Im{constructor(t,e,n,i){super(),void 0===Xj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const s=Xj;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 Bm(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 $j=new class extends la{constructor(){super(...arguments),this.noiseIntensity=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.scanlinesIntensity=aa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.scanlinesCount=aa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...uj}),this.grayscale=aa.BOOLEAN(1,{...uj})}};class Jj extends dj{constructor(){super(...arguments),this.paramsConfig=$j}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new Yj(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 Zj={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 Qj=new class extends la{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(1,uj)}};class Kj extends dj{constructor(){super(...arguments),this.paramsConfig=Qj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new zm(Zj);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 tW={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 eW=new class extends la{};class nW extends dj{constructor(){super(...arguments),this.paramsConfig=eW}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new zm(tW);return this.updatePass(e),e}updatePass(t){}}const iW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class sW extends dj{constructor(){super(...arguments),this.paramsConfig=iW}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new zm(yG);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 rW=new class extends la{constructor(){super(...arguments),this.map=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP},...uj}),this.darkness=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj}),this.offset=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class oW extends dj{constructor(){super(...arguments),this.paramsConfig=rW}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 zm(oW._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 aW={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},lW=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",cW=\\\\\\\"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 hW extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(aW),this.material=new F({uniforms:this.uniforms,vertexShader:lW,fragmentShader:cW,transparent:!0}),this.fsQuad=new Bm(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 uW=new class extends la{};class dW extends dj{constructor(){super(...arguments),this.paramsConfig=uW}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 Gm(e,i),o=new Gm(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 hW(r,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const pW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj}),this.inverse=aa.BOOLEAN(0,uj)}};class _W extends dj{constructor(){super(...arguments),this.paramsConfig=pW}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new km(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 mW=new class extends la{};class fW extends dj{constructor(){super(...arguments),this.paramsConfig=mW}static type(){return\\\\\\\"null\\\\\\\"}}class gW extends Im{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===Rm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Rm;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 Bm(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=gW.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=gW.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=gW.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.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})}}gW.BlurDirectionX=new d.a(1,0),gW.BlurDirectionY=new d.a(0,1);const vW=new class extends la{constructor(){super(...arguments),this.objectsMask=aa.STRING(\\\\\\\"*outlined*\\\\\\\",{...uj}),this.refreshObjects=aa.BUTTON(null,{...uj}),this.printObjects=aa.BUTTON(null,{cook:!1,callback:t=>{yW.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=aa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.edgeThickness=aa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...uj}),this.edgeGlow=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.pulsePeriod=aa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...uj}),this.visibleEdgeColor=aa.COLOR([1,1,1],{...uj}),this.hiddenEdgeColor=aa.COLOR([.2,.1,.4],{...uj})}};class yW extends dj{constructor(){super(...arguments),this.paramsConfig=vW,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new gW(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 xW={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 bW=new class extends la{constructor(){super(...arguments),this.pixelSize=aa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...uj})}};class wW extends dj{constructor(){super(...arguments),this.paramsConfig=bW}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new zm(xW);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 TW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj})}};class AW extends dj{constructor(){super(...arguments),this.paramsConfig=TW}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(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()==DV.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const MW={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 EW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.angle=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...uj})}};class SW extends dj{constructor(){super(...arguments),this.paramsConfig=EW}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new zm(MW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const CW={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 NW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...uj})}};class LW extends dj{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new zm(CW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const OW=new class extends la{};class PW extends dj{constructor(){super(...arguments),this.paramsConfig=OW}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 RW=I.clone(yG.uniforms);RW.delta={value:new d.a};const IW={uniforms:RW,vertexShader:yG.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 FW=new class extends la{constructor(){super(...arguments),this.delta=aa.VECTOR2([2,2],{...uj})}};class DW extends dj{constructor(){super(...arguments),this.paramsConfig=FW}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new zm(IW);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 BW={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 zW extends Im{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}\\\\\\\"})}}zW.BlurDirectionX=new d.a(1,0),zW.BlurDirectionY=new d.a(0,1);const kW=new class extends la{constructor(){super(...arguments),this.strength=aa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...uj}),this.radius=aa.FLOAT(1,{...uj}),this.threshold=aa.FLOAT(0,{...uj})}};class UW extends dj{constructor(){super(...arguments),this.paramsConfig=kW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new zW(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 GW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class VW extends dj{constructor(){super(...arguments),this.paramsConfig=GW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new zm(xG);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 HW={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 la{constructor(){super(...arguments),this.offset=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.darkness=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class WW extends dj{constructor(){super(...arguments),this.paramsConfig=jW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new zm(HW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class qW extends sa{static context(){return ts.POST}cook(){this.cookController.endCook()}}class XW extends qW{}class YW extends XW{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 $W extends XW{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 JW extends XW{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 ZW extends XW{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 QW extends qW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 KW extends XW{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 tq extends sa{static context(){return ts.ROP}cook(){this.cookController.endCook()}}class eq extends tq{}class nq extends eq{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 iq 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 iq&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return K.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class sq{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 iq){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=rr.fit(t,this._fog_near,this._fog_far,0,1),s=rr.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 aq extends eq{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 lq extends eq{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 cq extends eq{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 hq extends tq{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(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 uq extends eq{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 dq extends QG{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,Cs.LINE_SEGMENTS);e.push(l)}}dq.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const pq=dq.DEFAULT_PARAMS;const _q=new class extends la{constructor(){super(...arguments),this.createPoint=aa.BOOLEAN(pq.createPoint),this.pointsCount=aa.INTEGER(pq.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=aa.VECTOR3(pq.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=aa.BOOLEAN(pq.connectInputPoints),this.connectToLastPoint=aa.BOOLEAN(pq.connectToLastPoint)}};class mq extends nV{constructor(){super(...arguments),this.paramsConfig=_q}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 dq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const fq=new class extends la{};class gq extends nV{constructor(){super(...arguments),this.paramsConfig=fq}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 vq{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 yq=n(71),xq=n(66);class bq{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){ah.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;ah.a.multiplyQuaternionsFlat(t,r,t,e,t,n),ah.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 wq=n(64);class Tq 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 bq(xq.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{Mq.PARAM_CALLBACK_reset(t,e)}})}};class Mq extends nV{constructor(){super(...arguments),this.paramsConfig=Aq}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 Tq(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 Eq extends QG{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 Bq extends nV{constructor(){super(...arguments),this.paramsConfig=Dq}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Iq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>Oq[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new Iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class zq extends QG{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 zq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class Vq extends QG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Fs[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=zs[n.type];switch(t){case Is.VERTEX:return void await this.add_point_attribute(i,e,n);case Is.OBJECT:return void await this.add_object_attribute(i,e,n)}ls.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Bs.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._add_attribute(Fs[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new Vq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=zs[this.pv.type];switch(t){case Is.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Is.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ls.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Bs.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=qs.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.compute_expression_for_objects(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 Is.VERTEX:this.delete_vertex_attribute(e,t);case Is.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 _r(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new yr(n,t).deleteAttribute(e),t++}))}}}class $q{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=rr.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 Jq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var tX;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(tX||(tX={}));const eX=[tX.MIN_MAX_TO_01,tX.VECTOR_TO_LENGTH_1];class nX extends QG{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=os.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(eX[n.mode]){case tX.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case tX.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=aa.STRING(iX.name),this.changeName=aa.BOOLEAN(iX.changeName),this.newName=aa.STRING(iX.newName,{visibleIf:{changeName:1}})}};class rX extends nV{constructor(){super(...arguments),this.paramsConfig=sX}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(nX.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(eX.indexOf(t))}cook(t){this._operation=this._operation||new nX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oX;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(oX||(oX={}));class aX extends QG{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=os.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 Is.VERTEX:return this.find_values_from_points(t,e);case Is.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=Ds.filter((t=>t.value==this.pv.classFrom))[0].name,e=Ds.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new aX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const dX=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(),this.ramp=aa.RAMP(),this.changeName=aa.BOOLEAN(0),this.newName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class pX extends nV{constructor(){super(...arguments),this.paramsConfig=dX}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let s=new Array(e.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,t.hasAttrib(r)||t.addNumericVertexAttrib(r,n,0));let o=0;for(let t of s){e[o].setAttribValue(r,t),o++}}_get_remaped_values(t,e,n){switch(t){case Us.FLOAT:return this._get_normalized_float(e,n);case Us.VECTOR2:return this._get_normalized_vector2(e,n);case Us.VECTOR3:return this._get_normalized_vector3(e,n);case Us.VECTOR4:return this._get_normalized_vector4(e,n)}ls.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var fX=n(18);class gX{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new gX(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${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 Ay.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class vX{constructor(t){this._root=new gX(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new fX.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 yX{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 xX=new class extends la{constructor(){super(...arguments),this.srcGroup=aa.STRING(),this.destGroup=aa.STRING(),this.name=aa.STRING(),this.maxSamplesCount=aa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=aa.FLOAT(1),this.blendWidth=aa.FLOAT(0)}};class bX extends nV{constructor(){super(...arguments),this.paramsConfig=xX}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 vX(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 yX;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 wX=new class extends la{constructor(){super(...arguments),this.stepSize=aa.FLOAT(.1)}};class TX extends nV{constructor(){super(...arguments),this.paramsConfig=wX}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,Cs.POINTS)}}const AX=new class extends la{constructor(){super(...arguments),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.blend=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class MX extends nV{constructor(){super(...arguments),this.paramsConfig=AX}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}}EX.fromPolygons=function(t){let e=new EX;return e.polygons=t,e};class SX{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 SX(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(CX.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 CX=new SX,NX=new SX;class LX{constructor(t,e,n,i){this.pos=(new SX).copy(t),this.normal=(new SX).copy(e),this.uv=(new SX).copy(n),this.uv.z=0,i&&(this.color=(new SX).copy(i))}clone(){return new LX(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new LX(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 OX{constructor(t,e){this.normal=t,this.w=e}clone(){return new OX(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;eOX.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 PX(r,t.shared)),a.length>=3&&s.push(new PX(a,t.shared))}}}OX.EPSILON=1e-5,OX.fromPoints=function(t,e,n){let i=CX.copy(e).sub(t).cross(NX.copy(n).sub(t)).normalize();return new OX(i.clone(),i.dot(t))};class PX{constructor(t,e){this.vertices=t,this.shared=e,this.plane=OX.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new PX(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class RX{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new RX;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 PX(t.vertices.map((t=>new LX(t.pos,t.normal,t.uv))),t.shared))))},EX.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}}),BX=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var zX;EX.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=DX(3*t*3),o=DX(3*t*3),a=BX(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=DX(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=aa.BOOLEAN(GX.useBothMaterials)}};class HX extends nV{constructor(){super(...arguments),this.paramsConfig=VX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(UX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>kX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(kX.indexOf(t))}async cook(t){this._operation=this._operation||new UX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jX extends QG{constructor(){super(...arguments),this._core_transform=new uU}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 WX=jX.DEFAULT_PARAMS;const qX=new class extends la{constructor(){super(...arguments),this.size=aa.FLOAT(WX.size),this.divisions=aa.INTEGER(WX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(WX.center)}};class XX extends nV{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 YX{constructor(){}}function $X(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 JX(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 ZX(t,e){e.set(t)}function QX(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 KX(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 tY=Math.pow(2,-24);function eY(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 nY=32,iY=new Array(nY).fill().map((()=>({count:0,bounds:new Float32Array(6),rightCacheBounds:new Float32Array(6),candidate:0}))),sY=new Float32Array(6);function rY(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=JX(e),-1!==o&&(a=(e[o]+e[o+3])/2);else if(1===r)o=JX(t),-1!==o&&(a=function(t,e,n,i){let s=0;for(let r=e,o=e+n;r=nY&&(s=31);const r=iY[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=iY[31];ZX(d.bounds,d.rightCacheBounds);for(let t=30;t>=0;t--){const e=iY[t],n=iY[t+1];QX(e.bounds,n.rightCacheBounds,e.rightCacheBounds)}let p=0;for(let e=0;e<31;e++){const n=iY[e],i=n.count,c=n.bounds,h=iY[e+1].rightCacheBounds;0!==i&&(0===p?ZX(c,sY):QX(c,sY,sY)),p+=i;let u=0,d=0;0!==p&&(u=KX(sY)/r);const _=s-p;0!==_&&(d=KX(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 Hw(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)*tY,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 YX;e.boundingData=new Float32Array(6),eY(r,t.offset,t.count,e.boundingData,s),n(e,t.offset,t.count,s),d.push(e)}return d}const oY=65535;class aY{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}}aY.prototype.setFromBox=function(){const t=new gb;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 aY}();const lY=function(){const t=new gb,e=new gb,n=new gb;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}}(),cY=function(){const t=new sb,e=new gb,n=new gb;return function(i,s,r,o){lY(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))}}}(),hY=function(){const t=new gb,e=new gb,n=new IT,i=new YN;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 uY extends Lw{constructor(...t){super(...t),this.isSeparatingAxisTriangle=!0,this.satAxes=new Array(4).fill().map((()=>new gb)),this.satBounds=new Array(4).fill().map((()=>new aY)),this.points=[this.a,this.b,this.c],this.sphere=new kb,this.plane=new IT,this.needsUpdate=!1}intersectsSphere(t){return hY(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}}uY.prototype.closestPointToSegment=function(){const t=new gb,e=new gb,n=new YN;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]),cY(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}}(),uY.prototype.distanceToPoint=function(){const t=new gb;return function(e){return this.closestPointToPoint(e,t),e.distanceTo(t)}}(),uY.prototype.distanceToTriangle=function(){const t=new gb,e=new gb,n=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],i=new YN,s=new YN;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 gb)),this.satAxes=new Array(3).fill().map((()=>new gb)),this.satBounds=new Array(3).fill().map((()=>new aY)),this.alignedSatBounds=new Array(3).fill().map((()=>new aY)),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}}dY.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 YN)),n=new Array(12).fill().map((()=>new YN)),i=new gb,s=new gb;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 wY(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 TY(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=CY(t),h=NY(t,a)):(c=NY(t,a),h=CY(t));const u=BY(c,r,i,OY)?IY(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=BY(h,r,i,OY)?IY(h,e,n,i):null;return u&&d?u.distance<=d.distance?u:d:u||d||null}}const FY=function(){let t,e;const n=[],i=new AY((()=>new xb));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=GY,i=VY;for(;!MY(e,n);)e=2*(t=CY(t));return EY(t,i)}function u(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=NY(t,i));return EY(t,i)+SY(e,n)}let d=2*n,p=UY,_=GY,m=VY;if(MY(d,_)){const e=EY(n,m),i=SY(d,_);return $X(n,p,t),o(e,i,!1,c,l+n,t)}{const d=CY(n),f=NY(n,m);let g,v,y,x,b=d,w=f;if(a&&(y=t,x=e,$X(b,p,y),$X(w,p,x),g=a(y),v=a(x),vs.intersectsBox(t),intersectsTriangle:t=>{t.a.applyMatrix4(c),t.b.applyMatrix4(c),t.c.applyMatrix4(c),t.needsUpdate=!0;for(let n=3*g,i=3*(v+g);nnew uY));class s${static serialize(t,e={}){if(e.isBufferGeometry)return console.warn(\\\\\\\"MeshBVH.serialize: The arguments for the function have changed. 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See docs for new signature.');const t=new Set;n.forEach((e=>t.add(e))),e&&e.forEach((e=>t.add(e))),c$.call(this,t)}else c$.apply(this,t)},[\\\\\\\"intersectsGeometry\\\\\\\",\\\\\\\"shapecast\\\\\\\",\\\\\\\"intersectsBox\\\\\\\",\\\\\\\"intersectsSphere\\\\\\\"].forEach((t=>{const e=s$.prototype[t];s$.prototype[t]=function(...n){return(null===n[0]||n[0].isMesh)&&(n.shift(),console.warn(`MeshBVH: The function signature for \\\\\\\"${t}\\\\\\\" has changed and no longer takes Mesh. See docs for new signature.`)),e.apply(this,n)}}));const h$=new Xb,u$=new Yb,d$=vT.prototype.raycast;function p$(t,e){if(this.geometry.boundsTree){if(void 0===this.material)return;u$.copy(this.matrixWorld).invert(),h$.copy(t.ray).applyMatrix4(u$);const n=this.geometry.boundsTree;if(!0===t.firstHitOnly){const i=bY(n.raycastFirst(h$,this.material),this,t);i&&e.push(i)}else{const i=n.raycast(h$,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){$X(0,n,_$);const{min:t,max:e}=_$;for(let n=-1;n<=1;n+=2){const i=n<0?t.x:e.x;for(let n=-1;n<=1;n+=2){const s=n<0?t.y:e.y;for(let n=-1;n<=1;n+=2){const r=n<0?t.z:e.z;a[o+0]=i,a[o+1]=s,a[o+2]=r,o+=3}}}return r}}),n),c=this.displayEdges?new Uint8Array([0,4,1,5,2,6,3,7,0,2,1,3,4,6,5,7,0,1,2,3,4,5,6,7]):new Uint8Array([0,1,2,2,1,3,4,6,5,6,7,5,1,4,5,0,4,1,2,3,6,3,7,6,0,2,4,2,6,4,1,5,3,3,5,7]),l=a.length>65535?new Uint32Array(c.length*r):new Uint16Array(c.length*r);const h=c.length;for(let t=0;te;)this._roots.pop();for(let t=0;t=this._roots.length){const e=new m$(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 f$(this.mesh,this.depth)}dispose(){this.edgeMaterial.dispose(),this.meshMaterial.dispose();const t=this.children;for(let e=0,n=t.length;e{const e=t;if(e.isMesh){const t=e.geometry;for(const e in t.attributes)\\\\\\\"position\\\\\\\"!==e&&t.deleteAttribute(e);n.push(e)}}))}const i=this._makeCompact(n);if(i){i.matrixAutoUpdate=!1,i.raycast=p$;const t=new s$(i.geometry,{verbose:!1});return i.geometry.boundsTree=t,this.createCoreGroupFromObjects([i])}return this.createCoreGroupFromObjects([])}_makeCompact(t){var e,n;const i=[];let s;for(let e of t){s=s||e.material;const t=e.geometry;t.applyMatrix4(e.matrix),i.push(t)}try{const t=_r.mergeGeometries(i);if(t){return this.createObject(t,Cs.MESH,s)}null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(n=this.states)||void 0===n||n.error.set(t.message)}}}g$.DEFAULT_PARAMS={},g$.INPUT_CLONED_STATE=Ki.ALWAYS;const v$=new class extends la{};class y$ extends nV{constructor(){super(...arguments),this.paramsConfig=v$}static type(){return\\\\\\\"BVH\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create BVH 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super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(C$.geometry=this.geometry,C$.material=this.material,void 0!==C$.material)for(let s=0;st.far||e.push({distance:a,point:O$.clone(),uv:Ks.a.getUV(O$,B$,z$,k$,U$,G$,V$,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 j$(t,e,n,i,s,r){I$.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(F$.x=r*I$.x-s*I$.y,F$.y=s*I$.x+r*I$.y):F$.copy(I$),t.copy(e),t.x+=F$.x,t.y+=F$.y,t.applyMatrix4(D$)}H$.prototype.isSprite=!0;var W$=n(92),q$=n(81),X$=n(46);class Y${constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let 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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)}}Y$.prototype.isSphericalHarmonics3=!0;class $$ extends sv.a{constructor(t=new Y$,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}$$.prototype.isLightProbe=!0;var J$=n(63),Z$=n(43);class Q$ 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}}Q$.prototype.isInstancedBufferGeometry=!0;class K$ extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.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 lr.a(o,s.stride);return a.uuid=s.uuid,e[i]=a,a}const s=t.isInstancedBufferGeometry?new Q$:new S.a,r=t.data.index;if(void 0!==r){const t=Object(It.c)(r.type,r.array);s.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let r;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);r=new cr.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(It.c)(n.type,n.array);r=new(n.isInstancedBufferAttribute?A$: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 Vg.b(e);s=new J$.a(n),s.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new J$.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{SJ.PARAM_CALLBACK_reset(t,e)}})}};class SJ extends nV{constructor(){super(...arguments),this.paramsConfig=EJ}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 wJ,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 CJ=[is.ORTHOGRAPHIC,is.PERSPECTIVE],NJ={direction:new p.a(0,1,0)},LJ=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],OJ=new p.a(0,0,1);const PJ=new class extends la{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:CJ}}),this.direction=aa.VECTOR3(NJ.direction),this.offset=aa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=aa.BOOLEAN(!0),this.stepSize=aa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{RJ.PARAM_CALLBACK_update(t)}})}};class RJ extends nV{constructor(){super(...arguments),this.paramsConfig=PJ,this._plane=new Y.a,this._raycaster=new WL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new uU,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(!CJ.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 LJ){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,OJ,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 IJ extends QG{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 zJ=new p.a(0,0,1);class kJ extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=BJ.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e,Cs.LINE_SEGMENTS)}_create_disk(t){const e=new tJ(t.radius,t.segments);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e)}}kJ.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const UJ=kJ.DEFAULT_PARAMS;const GJ=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(UJ.radius),this.segments=aa.INTEGER(UJ.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=aa.BOOLEAN(UJ.open),this.arcAngle=aa.FLOAT(UJ.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=aa.VECTOR3(UJ.direction)}};class VJ extends nV{constructor(){super(...arguments),this.paramsConfig=GJ}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new kJ(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var HJ;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(HJ||(HJ={}));const jJ=[HJ.SEGMENTS_COUNT,HJ.SEGMENTS_LENGTH];var WJ;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(WJ||(WJ={}));const qJ=[WJ.ABC,WJ.ACB,WJ.AB,WJ.AC,WJ.BC];class XJ{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 HJ.SEGMENTS_COUNT:return this.params.segmentsCount+1;case HJ.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)}}ls.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case WJ.ABC:case WJ.ACB:case WJ.AB:case WJ.AC:return this.x.copy(this.an);case WJ.BC:return this.x.copy(this.bn)}ls.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case WJ.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case WJ.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case WJ.AB:return void(this.angle=this.angle_ab);case WJ.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case WJ.BC:return void(this.angle=this.angle_bc)}ls.unreachable(t)}}const YJ=new class extends la{constructor(){super(...arguments),this.arc=aa.BOOLEAN(1),this.pointsCountMode=aa.INTEGER(jJ.indexOf(HJ.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:jJ.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=aa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=aa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=aa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=aa.INTEGER(qJ.indexOf(WJ.ABC),{visibleIf:{arc:1,full:0},menu:{entries:qJ.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=aa.BOOLEAN(1),this.addIdnAttribute=aa.BOOLEAN(1),this.center=aa.BOOLEAN(0)}};class $J extends nV{constructor(){super(...arguments),this.paramsConfig=YJ,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 XJ({arc:this.pv.arc,center:this.pv.center,pointsCountMode:jJ[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:qJ[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,Cs.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Cs.POINTS)),this.setObjects(n)}}class JJ extends QG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}JJ.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const ZJ=new D.a(1,1,1),QJ=\\\\\\\"color\\\\\\\",KJ=JJ.DEFAULT_PARAMS;const tZ=new class extends la{constructor(){super(...arguments),this.fromAttribute=aa.BOOLEAN(KJ.fromAttribute),this.attribName=aa.STRING(KJ.attribName,{visibleIf:{fromAttribute:1}}),this.color=aa.COLOR(KJ.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=aa.BOOLEAN(KJ.asHsv,{visibleIf:{fromAttribute:0}})}};class eZ extends nV{constructor(){super(...arguments),this.paramsConfig=tZ,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(QJ).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,ZJ);const n=e.points(),i=e.attribSize(this.pv.attribName),s=e.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(QJ).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(rZ.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):rZ.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=rZ.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(rZ.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 Q$;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(lZ),u=new p.a(0,0,0),d=new ah.a,_=new p.a(1,1,1),f=new uZ(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(lZ,this._point_color):oZ).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):aZ).toArray(t,i)})),s.setAttribute(cZ.UV,new A$(t,2))}s.setAttribute(cZ.POSITION,new A$(o,3)),s.setAttribute(cZ.SCALE,new A$(l,3)),s.setAttribute(cZ.ORIENTATION,new A$(c,4)),s.setAttribute(cZ.COLOR,new A$(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(r*n);i.forEach(((e,i)=>{const s=e.attribValue(t);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(t,new A$(o,n))}));return new _r(s).markAsInstance(),s}}uZ._point_color=new p.a,uZ._point_uv=new d.a;class dZ extends sh{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\\\\\\\"}(hZ||(hZ={}));const pZ=[hZ.OBJECT,hZ.GEOMETRY],_Z=[{name:\\\\\\\"object\\\\\\\",value:hZ.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:hZ.GEOMETRY}];const mZ=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=aa.BOOLEAN(0),this.transformMode=aa.INTEGER(0,{menu:{entries:_Z}}),this.copyAttributes=aa.BOOLEAN(0),this.attributesToCopy=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=aa.BOOLEAN(0)}};class fZ extends nV{constructor(){super(...arguments),this.paramsConfig=mZ,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();let i=new uZ(e).matrices();const s=new p.a,r=new ah.a,o=new p.a;i[0].decompose(s,r,o),this._attribute_names_to_copy=os.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=yr.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 yr(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 dZ(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const gZ=\\\\\\\"id\\\\\\\",vZ=\\\\\\\"class\\\\\\\",yZ=\\\\\\\"html\\\\\\\";class xZ extends QG{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(gZ):e.className,s=e.useClassAttrib?t.attribValue(vZ):e.className,r=e.useHtmlAttrib?t.attribValue(yZ):e.html,o=xZ.create_css_object({id:n,className:s,html:r}),a=o.element;if(e.copyAttributes){const n=os.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 xZ.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 iq(e);return n.matrixAutoUpdate=!1,n}}xZ.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},xZ.INPUT_CLONED_STATE=Ki.FROM_NODE;const bZ=xZ.DEFAULT_PARAMS;const wZ=new class extends la{constructor(){super(...arguments),this.useIdAttrib=aa.BOOLEAN(bZ.useIdAttrib),this.id=aa.STRING(bZ.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=aa.BOOLEAN(bZ.useClassAttrib),this.className=aa.STRING(bZ.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=aa.BOOLEAN(bZ.useHtmlAttrib),this.html=aa.STRING(bZ.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=aa.BOOLEAN(bZ.copyAttributes),this.attributesToCopy=aa.STRING(bZ.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class TZ extends nV{constructor(){super(...arguments),this.paramsConfig=wZ}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new xZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class AZ{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)?Bs.STRING:Bs.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class MZ{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 _r(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=os.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=qs.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()===Bs.STRING)if(this._options.doConvert&&os.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=qs.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=os.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];os.matchesOneMask(s,e)||(this._attribute_datas_by_name[s]=AZ.from_value(r))}else os.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=AZ.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 EZ=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 SZ=new class extends la{constructor(){super(...arguments),this.data=aa.STRING(EZ)}};class CZ extends nV{constructor(){super(...arguments),this.paramsConfig=SZ}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 MZ;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Cs.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class NZ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new MZ(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 LZ=\\\\\\\"position\\\\\\\";class OZ{constructor(t){this.attribute_names=t,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(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(OZ.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>qs.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(OZ.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=aa.STRING(IZ,{fileBrowse:{type:[Or.JSON]}}),this.jsonDataKeysPrefix=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.skipEntries=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convert=aa.BOOLEAN(0,{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convertToNumeric=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON),convert:1}}),this.readAttribNamesFromFile=aa.BOOLEAN(1,{visibleIf:{dataType:RZ.indexOf(PZ.CSV)}}),this.attribNames=aa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.CSV),readAttribNamesFromFile:0}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{DZ.PARAM_CALLBACK_reload(t,e)}})}};class DZ extends nV{constructor(){super(...arguments),this.paramsConfig=FZ}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(){switch(RZ[this.pv.dataType]){case PZ.JSON:return this._load_json();case PZ.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new NZ(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Cs.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new OZ(t),n=await e.load(this._url());n?this.setGeometry(n,Cs.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class BZ 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 zZ(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 zZ(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 WZ{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 qZ;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(qZ||(qZ={}));const XZ=[qZ.EQUAL,qZ.LESS_THAN,qZ.EQUAL_OR_LESS_THAN,qZ.EQUAL_OR_GREATER_THAN,qZ.GREATER_THAN,qZ.DIFFERENT],YZ={[qZ.EQUAL]:(t,e)=>t==e,[qZ.LESS_THAN]:(t,e)=>tt<=e,[qZ.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[qZ.GREATER_THAN]:(t,e)=>t>e,[qZ.DIFFERENT]:(t,e)=>t!=e},$Z=XZ.map(((t,e)=>({name:t,value:e})));class JZ{constructor(t){this.node=t}evalForEntities(t){const e=zs[this.node.pv.attribType];switch(e){case Bs.NUMERIC:return void this._eval_for_numeric(t);case Bs.STRING:return void this._eval_for_string(t)}ls.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Gs[this.node.pv.attribSize-1];switch(e){case Us.FLOAT:return this._eval_for_points_numeric_float(t);case Us.VECTOR2:return this._eval_for_points_numeric_vector2(t);case Us.VECTOR3:return this._eval_for_points_numeric_vector3(t);case Us.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ls.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=XZ[this.node.pv.attribComparisonOperator],r=YZ[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 ZZ{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.compute_expression_for_entities(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 QZ{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 Ay.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 KZ{constructor(t){this.node=t}eval_for_objects(t){const e=Os[this.node.pv.objectType];for(let n of t){Ls(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class tQ{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 eQ=new p.a(0,1,0),nQ=new p.a(0,-1,0);class iQ{constructor(t){this.node=t,this._matDoubleSideTmpSetter=new tQ,this._point_position=new p.a,this._raycaster=new WL,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,eQ)&&this._isPositionInObject(this._point_position,i,nQ)&&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 sQ=new class extends la{constructor(){super(...arguments),this.class=aa.INTEGER(Fs.indexOf(Is.VERTEX),{menu:{entries:Ds}}),this.invert=aa.BOOLEAN(0),this.byObjectType=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}}),this.objectType=aa.INTEGER(Os.indexOf(Cs.MESH),{menu:{entries:Ps},visibleIf:{class:Fs.indexOf(Is.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=aa.BOOLEAN(0),this.expression=aa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=aa.BOOLEAN(0),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},visibleIf:{byAttrib:1}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=aa.INTEGER(1,{range:Vs,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC)}}),this.attribComparisonOperator=aa.INTEGER(XZ.indexOf(qZ.EQUAL),{menu:{entries:$Z},visibleIf:{byAttrib:!0,attribType:zs.indexOf(Bs.NUMERIC),attribSize:Us.FLOAT}}),this.attribValue1=aa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:1}}),this.attribValue2=aa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:2}}),this.attribValue3=aa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:3}}),this.attribValue4=aa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:4}}),this.attribString=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.STRING)},separatorAfter:!0}),this.byBbox=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.bboxSize=aa.VECTOR3([1,1,1],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0}}),this.bboxCenter=aa.VECTOR3([0,0,0],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.keepPoints=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}})}};class rQ extends nV{constructor(){super(...arguments),this.paramsConfig=sQ,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new WZ(this),this.byExpressionHelper=new ZZ(this),this.byAttributeHelper=new JZ(this),this.byObjectTypeHelper=new KZ(this),this.byBboxHelper=new QZ(this),this.byBoundingObjectHelper=new iQ(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 Is.VERTEX:await this._eval_for_points(e,n);break;case Is.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=_r.geometryFromPoints(a,Ls(t.constructor));e&&(t.geometry=e,s.push(t))}}}this.setObjects(s)}_point_object(t){const e=t.points(),n=_r.geometryFromPoints(e,Cs.POINTS);if(n)return this.createObject(n,Cs.POINTS)}}const oQ=new class extends la{constructor(){super(...arguments),this.start=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=aa.BOOLEAN(0),this.count=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class aQ extends nV{constructor(){super(...arguments),this.paramsConfig=oQ}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 lQ{constructor(){this.pluginCallbacks=[],this.register((function(t){return new DQ(t)})),this.register((function(t){return new BQ(t)})),this.register((function(t){return new zQ(t)})),this.register((function(t){return new kQ(t)})),this.register((function(t){return new UQ(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 FQ,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=RQ(t.result),s=new DataView(new ArrayBuffer(8));s.setUint32(0,i.byteLength,!0),s.setUint32(4,5130562,!0);const r=RQ(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 gb;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 gb;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===fQ?l.setFloat32(c,i,!0):e===gQ?l.setUint32(c,i,!0):e===mQ?l.setUint16(c,i,!0):e===_Q&&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===vQ&&(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=RQ(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=fQ;else if(t.array.constructor===Uint32Array)o=gQ;else if(t.array.constructor===Uint16Array)o=mQ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=_Q}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?yQ:vQ);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===Ex?\\\\\\\"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=IQ=IQ||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!==Ex&&e!==Mx&&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===Ex)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:ib.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=lQ.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 CE;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 BQ{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 zQ{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 kQ{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 UQ{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 GQ(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)}lQ.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{HQ.PARAM_CALLBACK_export(t)}})}};class HQ extends nV{constructor(){super(...arguments),this.paramsConfig=VQ}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 gs;for(let t of n)i.add(t);(new lQ).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",GQ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){GQ(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 jQ=new class extends la{constructor(){super(...arguments),this.makeFacesUnique=aa.BOOLEAN(0),this.addFaceCenterAttribute=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=aa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class WQ extends nV{constructor(){super(...arguments),this.paramsConfig=jQ}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==qQ.JSON&&this._options.format==qQ.AUTO)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new wJ(this.loadingManager).parse(n,(e=>{$Q.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{$Q.decrement_in_progress_loads_count(),e(t)}));else{const s=await this._loaderForFormat();if(s)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(e=>{this.on_load_success(e).then((e=>{$Q.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{li.warn(\\\\\\\"error loading\\\\\\\",n,t),$Q.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==qQ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case YQ.PDB:return this.on_load_succes_pdb(t);case YQ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case YQ.DRC:return this.on_load_succes_drc(i);default:return[new B.a(i)]}const s=t;if(null!=s.scene)switch(e){case YQ.GLTF:case YQ.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=t;if(r.geometryAtoms||r.geometryBonds)switch(e){case YQ.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(t){return[new B.a(t,$Q._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new vs.a(t.geometryAtoms,$Q._default_mat_point),new As.a(t.geometryBonds,$Q._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case qQ.AUTO:return this.moduleNamesFromExt(e);case qQ.DRC:return[Hn.DRACOLoader];case qQ.FBX:return[Hn.FBXLoader];case qQ.JSON:return[];case qQ.GLTF:return[Hn.GLTFLoader];case qQ.GLTF_WITH_DRACO:return[Hn.GLTFLoader,Hn.DRACOLoader];case qQ.OBJ:return[Hn.OBJLoader];case qQ.PDB:return[Hn.PDBLoader];case qQ.PLY:return[Hn.PLYLoader];case qQ.STL:return[Hn.STLLoader]}ls.unreachable(t)}static moduleNamesFromExt(t){switch(t){case YQ.DRC:return[Hn.DRACOLoader];case YQ.FBX:return[Hn.FBXLoader];case YQ.GLTF:return[Hn.GLTFLoader];case YQ.GLB:return[Hn.GLTFLoader,Hn.DRACOLoader];case YQ.OBJ:return[Hn.OBJLoader];case YQ.PDB:return[Hn.PDBLoader];case YQ.PLY:return[Hn.PLYLoader];case YQ.STL:return[Hn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case qQ.AUTO:return this._loaderForExt();case qQ.DRC:return this.loader_for_drc(this._node);case qQ.FBX:return this.loader_for_fbx();case qQ.JSON:return;case qQ.GLTF:return this.loader_for_gltf();case qQ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case qQ.OBJ:return this.loader_for_obj();case qQ.PDB:return this.loader_for_pdb();case qQ.PLY:return this.loader_for_ply();case qQ.STL:return this.loader_for_stl()}ls.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case YQ.DRC:return this.loader_for_drc(this._node);case YQ.FBX:return this.loader_for_fbx();case YQ.GLTF:return this.loader_for_gltf();case YQ.GLB:return this.loader_for_glb(this._node);case YQ.OBJ:return this.loader_for_obj();case YQ.PDB:return this.loader_for_pdb();case YQ.PLY:return this.loader_for_ply();case YQ.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 $Q.loader_for_drc(t)}static async loader_for_glb(t){const e=li.modulesRegister.module(Hn.GLTFLoader),n=li.modulesRegister.module(Hn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=li.libs.root(),s=li.libs.DRACOGLTFPath();if(i||s){const e=`${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 $Q.loader_for_glb(t)}loader_for_obj(){const t=li.modulesRegister.module(Hn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=li.modulesRegister.module(Hn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=li.modulesRegister.module(Hn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=li.modulesRegister.module(Hn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$Q._default_mat_mesh=new ws.a,$Q._default_mat_point=new xs.a,$Q._default_mat_line=new Ts.a,$Q.MAX_CONCURRENT_LOADS_COUNT=$Q._init_max_concurrent_loads_count(),$Q.CONCURRENT_LOADS_DELAY=$Q._init_concurrent_loads_delay(),$Q.in_progress_loads_count=0,$Q._queue=[];const JQ=`${Gg}/models/wolf.obj`;class ZQ extends QG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new $Q({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}ZQ.DEFAULT_PARAMS={url:JQ,format:qQ.AUTO};const QQ=ZQ.DEFAULT_PARAMS;const KQ=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(QQ.url,{fileBrowse:{type:[Or.GEOMETRY]}}),this.format=aa.STRING(QQ.format,{menuString:{entries:XQ.map((t=>({name:t,value:t})))}}),this.reload=aa.BUTTON(null,{callback:t=>{tK.PARAM_CALLBACK_reload(t)}})}};class tK extends nV{constructor(){super(...arguments),this.paramsConfig=KQ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return $Q.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\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new ZQ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const eK=new class extends la{constructor(){super(...arguments),this.dist=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class nK extends nV{constructor(){super(...arguments),this.paramsConfig=eK}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=_r.geometryFromPoints(r,Ls(e.constructor));return t&&(e.geometry=t),e}}}class iK{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 sK=new p.a(0,1,0);const rK=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2([1,1]),this.hexagonRadius=aa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=aa.VECTOR3([0,1,0]),this.pointsOnly=aa.BOOLEAN(0)}};class oK extends nV{constructor(){super(...arguments),this.paramsConfig=rK,this._core_transform=new uU}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new iK(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,sK,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Cs.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 lK=[aK.ADD_PARENT,aK.REMOVE_PARENT,aK.ADD_CHILD];class cK extends QG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=lK[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)}}ls.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=aa.INTEGER(uK.levels,{range:[0,5],visibleIf:[{mode:lK.indexOf(aK.ADD_PARENT)},{mode:lK.indexOf(aK.REMOVE_PARENT)}]}),this.objectMask=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}}),this.debugObjectMask=aa.BOOLEAN(0,{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}})}};class pK extends nV{constructor(){super(...arguments),this.paramsConfig=dK}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(cK.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=lK[this.pv.mode];return hK.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new cK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const _K=new class extends la{constructor(){super(...arguments),this.texture=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP}}),this.mult=aa.FLOAT(1)}};class mK extends nV{constructor(){super(...arguments),this.paramsConfig=_K}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:Pf.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 fK(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class gK 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 FK=new class extends la{constructor(){super(...arguments),this.length=aa.FLOAT(1,{range:[0,10]}),this.pointsCount=aa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=aa.VECTOR3([0,0,0]),this.direction=aa.VECTOR3([0,1,0])}};class DK extends nV{constructor(){super(...arguments),this.paramsConfig=FK}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,Cs.LINE_SEGMENTS)}}const BK=new class extends la{constructor(){super(...arguments),this.distance0=aa.FLOAT(1),this.distance1=aa.FLOAT(2),this.autoUpdate=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{zK.PARAM_CALLBACK_update(t)}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class zK extends nV{constructor(){super(...arguments),this.paramsConfig=BK,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 Ss;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&&!yr.isInGroup(n.group,t))return;const i=n.cloneMat?gr.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,gr.apply_render_hook(t,i),gr.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})}}}}kK.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new yi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},kK.INPUT_CLONED_STATE=Ki.FROM_NODE;const UK=kK.DEFAULT_PARAMS;const GK=new class extends la{constructor(){super(...arguments),this.group=aa.STRING(UK.group),this.assignMat=aa.BOOLEAN(UK.assignMat),this.material=aa.NODE_PATH(UK.material.path(),{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=aa.BOOLEAN(UK.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=aa.BOOLEAN(UK.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=aa.BOOLEAN(UK.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=aa.BOOLEAN(UK.swapCurrentTex),this.texSrc0=aa.STRING(UK.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=aa.STRING(UK.texDest0,{visibleIf:{swapCurrentTex:1}})}};class VK extends nV{constructor(){super(...arguments),this.paramsConfig=GK}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(kK.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 kK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class HK extends QG{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=Ls(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=_r.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}}HK.DEFAULT_PARAMS={compact:!1},HK.INPUT_CLONED_STATE=Ki.FROM_NODE;const jK=\\\\\\\"geometry to merge\\\\\\\",WK=HK.DEFAULT_PARAMS;const qK=new class extends la{constructor(){super(...arguments),this.compact=aa.BOOLEAN(WK.compact),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{XK.PARAM_CALLBACK_setInputsCount(t)}})}};class XK extends nV{constructor(){super(...arguments),this.paramsConfig=qK}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[jK,jK,jK,jK]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(HK.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 HK(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()}}class YK extends K.a{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}}YK.prototype.isImmediateRenderObject=!0;class $K extends YK{constructor(t,e,n,i){super(e);const s=this,r=new Float32Array(36),o=new Float32Array(36),a=new Float32Array(36);function l(t,e,n){return t+(e-t)*n}function c(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i+m*s.delta,r[e+1]=c,r[e+2]=h,o[e+0]=l(f[t+0],f[t+3],m),o[e+1]=l(f[t+1],f[t+4],m),o[e+2]=l(f[t+2],f[t+5],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function h(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c+m*s.delta,r[e+2]=h;const g=t+3*s.yd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function u(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c,r[e+2]=h+m*s.delta;const g=t+3*s.zd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function d(t){const e=3*t;0===s.normal_cache[e]&&(s.normal_cache[e+0]=s.field[t-1]-s.field[t+1],s.normal_cache[e+1]=s.field[t-s.yd]-s.field[t+s.yd],s.normal_cache[e+2]=s.field[t-s.zd]-s.field[t+s.zd])}function p(t,e,n,i,l,p){const m=i+1,f=i+s.yd,g=i+s.zd,v=m+s.yd,y=m+s.zd,x=i+s.yd+s.zd,b=m+s.yd+s.zd;let w=0;const T=s.field[i],A=s.field[m],M=s.field[f],E=s.field[v],S=s.field[g],C=s.field[y],N=s.field[x],L=s.field[b];T=s.maxCount-3&&(s.hasPositions=!0,s.hasNormals=!0,s.enableUvs&&(s.hasUvs=!0),s.enableColors&&(s.hasColors=!0),a(s))}function m(t,e,n){const i=new Float32Array(t.length+n);return i.set(t,0),i.set(e.slice(0,n),t.length),i}this.enableUvs=void 0!==n&&n,this.enableColors=void 0!==i&&i,this.init=function(t){this.resolution=t,this.isolation=80,this.size=t,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=4096,this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=new Float32Array(3*this.maxCount),this.normalArray=new Float32Array(3*this.maxCount),this.enableUvs&&(this.uvArray=new Float32Array(2*this.maxCount)),this.enableColors&&(this.colorArray=new Float32Array(3*this.maxCount))},this.begin=function(){this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasUvs=!1,this.hasColors=!1},this.end=function(t){if(0!==this.count){for(let t=3*this.count;tthis.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}},this.addPlaneX=function(t,e){const n=this.size,i=this.yd,s=this.zd,r=this.field;let o,a,l,c,h,u,d,p=n*Math.sqrt(t/e);for(p>n&&(p=n),o=0;o0)for(a=0;an&&(_=n),a=0;a<_;a++)if(u=a/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=a*i,o=0;on&&(_=n),l=0;l<_;l++)if(u=l/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=s*l,a=0;a=i))for(let e=-1;e<=1;e+=2){const r=e+o;if(!(r<0||r>=i))for(let e=-1;e<=1;e+=2){const o=e+a;if(o<0||o>=i)continue;const u=n[s*o+i*r+l];h++,c+=t*(u-c)/h}}}e[l]=c}},this.reset=function(){for(let t=0;tu?(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 s0;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(s0||(s0={}));const r0=[s0.ADD,s0.SET,s0.MULT,s0.SUBSTRACT,s0.DIVIDE];const o0=new class extends la{constructor(){super(...arguments),this.amplitude=aa.FLOAT(1),this.tamplitudeAttrib=aa.BOOLEAN(0),this.amplitudeAttrib=aa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=aa.VECTOR3([1,1,1]),this.offset=aa.VECTOR3([0,0,0]),this.octaves=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10]}),this.seed=aa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=aa.BOOLEAN(0),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=aa.BOOLEAN(0),this.restP=aa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=aa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=aa.INTEGER(r0.indexOf(s0.ADD),{menu:{entries:r0.map((t=>({name:t,value:r0.indexOf(t)})))}}),this.computeNormals=aa.BOOLEAN(1)}};class a0 extends nV{constructor(){super(...arguments),this.paramsConfig=o0,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.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(r0.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)!=Bs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=this._get_simplex(),r=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=r0[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,h=this.pv.tamplitudeAttrib;let u,f,g=new p.a;for(let t=0;t{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._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)}}m0.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},m0.INPUT_CLONED_STATE=Ki.FROM_NODE;const f0=m0.DEFAULT_PARAMS;const g0=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(f0.applyToChildren,{separatorAfter:!0}),this.tname=aa.BOOLEAN(f0.tname),this.name=aa.STRING(f0.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=aa.BOOLEAN(f0.trenderOrder),this.renderOrder=aa.INTEGER(f0.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=aa.BOOLEAN(f0.tfrustumCulled),this.frustumCulled=aa.BOOLEAN(f0.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=aa.BOOLEAN(f0.tmatrixAutoUpdate),this.matrixAutoUpdate=aa.BOOLEAN(f0.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=aa.BOOLEAN(f0.tvisible),this.visible=aa.BOOLEAN(f0.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=aa.BOOLEAN(f0.tcastShadow),this.castShadow=aa.BOOLEAN(f0.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=aa.BOOLEAN(f0.treceiveShadow),this.receiveShadow=aa.BOOLEAN(f0.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class v0 extends nV{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(m0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new m0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const y0=new class extends la{};class x0 extends nV{constructor(){super(...arguments),this.paramsConfig=y0,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}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 b0 extends cf{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 Sf,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 Sf.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 Af(t,Bo.SAMPLER_2D,s);i.addDefinitions(t,[r]);return`texture2D( ${s}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Bo.VEC2,b0.UV_ATTRIB),i=new Mf(t,Bo.VEC2,b0.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${b0.UV_VARYING} = ${b0.UV_ATTRIB}`],xf.VERTEX)}}b0.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",b0.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",b0.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class w0{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new b0(b0.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${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,gr.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._last_simulated_frame&&(this._last_simulated_frame=e-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),t>this._last_simulated_frame&&this._compute_simulation(t-this._last_simulated_frame))}_compute_simulation(t=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();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._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),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 A0(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var s=this._gpu_compute.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._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[pR.TIME]={value:this.node.scene().time()},t.uniforms[pR.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)$f.callback(o,t.uniforms[r])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[pR.TIME].value=this.node.scene().time(),t.material.uniforms[pR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));s++);const r=b0.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?A$:C.a;n.setAttribute(r,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.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}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.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 C0{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 C0(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 N0{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()==ss.ATTRIBUTE?[gf.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 N0;for(let n of t){const t=C0.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const L0=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class O0{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==bF.type())),n=t.filter((t=>t.type()!=bF.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=O0._sortNodes(t),e=O0._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case bF.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new C0(i.name(),Vo[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const e=new C0(i.attribute_name,Vo[r.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case AI.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(L0.includes(t)){const s=i.io.outputs.namedOutputConnectionPointsByName(t);if(s){const i=s.type(),r=new C0(t,Vo[i]);r.addGraphNodeId(e),n.push(r)}}}break}case gf.type():{const i=t,s=i.output_connection_point();if(s){const t=new C0(i.attribute_name,Vo[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 O0;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=N0.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 P0 extends Xf{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 O0.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 R0=new class extends la{constructor(){super(...arguments),this.startFrame=aa.FLOAT(El.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=aa.BOOLEAN(1),this.maxTexturesSize=aa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=aa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=aa.INTEGER(0,{menu:{entries:M0.map(((t,e)=>({value:e,name:t})))}}),this.reset=aa.BUTTON(null,{callback:(t,e)=>{I0.PARAM_CALLBACK_reset(t)}}),this.material=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1})}};class I0 extends nV{constructor(){super(...arguments),this.paramsConfig=R0,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new P0(this),this.globals_handler=new b0(b0.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new S0(this),this.renderController=new w0(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.reset_gpu_compute_and_set_dirty()}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.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),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.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?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.reset_gpu_compute(),this.gpuController.reset_particle_groups()}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.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.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 F0 extends QG{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 _r(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]}}F0.DEFAULT_PARAMS={amount:0};const D0=F0.DEFAULT_PARAMS;const B0=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(D0.amount,{range:[-1,1]})}};class z0 extends nV{constructor(){super(...arguments),this.paramsConfig=B0}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){this._operation=this._operation||new F0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const k0=new p.a(0,0,1),U0=new p.a(0,0,1),G0=new p.a(0,1,0);class V0 extends QG{constructor(){super(...arguments),this._core_transform=new uU,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,k0,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,U0,G0);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)}}V0.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)},V0.INPUT_CLONED_STATE=Ki.NEVER;const H0=V0.DEFAULT_PARAMS;const j0=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2(H0.size),this.useSegmentsCount=aa.BOOLEAN(H0.useSegmentsCount),this.stepSize=aa.FLOAT(H0.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2(H0.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=aa.VECTOR3(H0.direction),this.center=aa.VECTOR3(H0.center)}};class W0 extends nV{constructor(){super(...arguments),this.paramsConfig=j0}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(V0.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new V0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class q0 extends QG{static type(){return\\\\\\\"playerCapsule\\\\\\\"}cook(t,e){return this.createCoreGroupFromGeometry(Oy(e))}}q0.DEFAULT_PARAMS={radius:.5,height:1};const X0=q0.DEFAULT_PARAMS;const Y0=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(X0.radius,{range:[0,1],rangeLocked:[!0,!1]}),this.height=aa.FLOAT(X0.height,{range:[0,1],rangeLocked:[!0,!1]})}};class $0 extends nV{constructor(){super(...arguments),this.paramsConfig=Y0}static type(){return\\\\\\\"playerCapsule\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new q0(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const J0=\\\\\\\"position\\\\\\\";const Z0=new class extends la{constructor(){super(...arguments),this.updateX=aa.BOOLEAN(0),this.x=aa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=aa.BOOLEAN(0),this.y=aa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=aa.BOOLEAN(0),this.z=aa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=aa.BOOLEAN(1)}};class Q0 extends nV{constructor(){super(...arguments),this.paramsConfig=Z0,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=aa.VECTOR3(o1.center.toArray()),this.longitude=aa.FLOAT(o1.longitude,{range:[0,360]}),this.latitude=aa.FLOAT(o1.latitude,{range:[-180,180]}),this.depth=aa.FLOAT(o1.depth,{range:[0,10]})}};class l1 extends nV{constructor(){super(...arguments),this.paramsConfig=a1}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(r1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new r1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c1{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 _r(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 h1{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new _r(this.geometry0),e=new _r(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 u1=new p.a(0,0,0),d1=new p.a(1,1,1);const p1=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.segmentsRadial=aa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=aa.BOOLEAN(0)}};class _1 extends nV{constructor(){super(...arguments),this.paramsConfig=p1,this._core_transform=new uU,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 As.a&&this._create_tube(t);const n=_r.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Cs.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new _r(n).points(),s=null===(e=n.getIndex())||void 0===e?void 0:e.array,r=c1.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=BJ.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,s=this._core_transform.matrix(e,u1,d1,1,hU),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 h1(n,t,e).process(),n}}const m1=\\\\\\\"dist\\\\\\\";class f1 extends QG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new tQ,this._raycaster=function(){const t=new WL;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(m1)||t.addNumericVertexAttrib(m1,1,-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(m1,e)}n.transferFaceNormals&&s.face&&t.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}f1.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},f1.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const g1=f1.DEFAULT_PARAMS;const v1=new class extends la{constructor(){super(...arguments),this.useNormals=aa.BOOLEAN(g1.useNormals),this.direction=aa.VECTOR3(g1.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=aa.BOOLEAN(g1.transformPoints),this.transferFaceNormals=aa.BOOLEAN(g1.transferFaceNormals),this.addDistAttribute=aa.BOOLEAN(g1.addDistAttribute)}};class y1 extends nV{constructor(){super(...arguments),this.paramsConfig=v1}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(f1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new f1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const x1={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},b1=\\\\\\\"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}\\\\\\\",w1=\\\\\\\"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}\\\\\\\",T1={minFilter:w.V,magFilter:w.V,format:w.ic};class A1 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,T1),Object(On.i)(n)&&Object(On.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new wG(new d.a(n,i)),this.material=new F({uniforms:I.clone(x1),fragmentShader:w1,vertexShader:b1}),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 M1 extends QG{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 A1(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)}}M1.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},M1.INPUT_CLONED_STATE=Ki.NEVER;const E1=M1.DEFAULT_PARAMS;const S1=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(E1.active),this.clipBias=aa.FLOAT(E1.clipBias),this.color=aa.COLOR(E1.color.toArray()),this.opacity=aa.FLOAT(E1.opacity),this.pixelRatio=aa.INTEGER(E1.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=aa.BOOLEAN(E1.tblur),this.blur=aa.FLOAT(E1.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=aa.FLOAT(E1.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=aa.BOOLEAN(E1.tblur2,{visibleIf:{tblur:1}}),this.blur2=aa.FLOAT(E1.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=aa.FLOAT(E1.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class C1 extends nV{constructor(){super(...arguments),this.paramsConfig=S1}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(M1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new M1(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var N1=n(85);var L1;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(L1||(L1={}));const O1=[L1.POINTS_COUNT,L1.SEGMENT_LENGTH];var P1;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(P1||(P1={}));const R1=[P1.CENTRIPETAL,P1.CHORDAL,P1.CATMULLROM];const I1=new class extends la{constructor(){super(...arguments),this.method=aa.INTEGER(O1.indexOf(L1.POINTS_COUNT),{menu:{entries:O1.map(((t,e)=>({name:t,value:e})))}}),this.curveType=aa.INTEGER(R1.indexOf(P1.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:R1.map(((t,e)=>({name:t,value:e})))}}),this.tension=aa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=aa.INTEGER(100,{visibleIf:{method:O1.indexOf(L1.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=aa.FLOAT(1,{visibleIf:{method:O1.indexOf(L1.SEGMENT_LENGTH)}})}};class F1 extends nV{constructor(){super(...arguments),this.paramsConfig=I1}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=hr(o);return this.createObject(a,Cs.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=R1[this.pv.curveType],i=this.pv.tension,s=new N1.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=O1[this.pv.method];switch(e){case L1.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case L1.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)}ls.unreachable(e)}}class D1 extends QG{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())}}}}D1.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const B1=D1.DEFAULT_PARAMS;const z1=new class extends la{constructor(){super(...arguments),this.tposition=aa.BOOLEAN(B1.tposition),this.position=aa.STRING(B1.position,{visibleIf:{tposition:!0}}),this.restP=aa.STRING(B1.restP,{visibleIf:{tposition:!0}}),this.tnormal=aa.BOOLEAN(B1.tnormal),this.normal=aa.STRING(B1.normal,{visibleIf:{tnormal:!0}}),this.restN=aa.STRING(B1.restN,{visibleIf:{tnormal:!0}})}};class k1 extends nV{constructor(){super(...arguments),this.paramsConfig=z1}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new D1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class U1 extends QG{constructor(){super(...arguments),this._core_transform=new uU}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 Ly(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 Ly(s.x,s.y,s.z,n,e.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}U1.DEFAULT_PARAMS={size:new p.a(1,1,1),divisions:2,bevel:.1,center:new p.a(0,0,0)},U1.INPUT_CLONED_STATE=Ki.NEVER;const G1=U1.DEFAULT_PARAMS;const V1=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR3(G1.size),this.divisions=aa.INTEGER(G1.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=aa.FLOAT(G1.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(G1.center)}};class H1 extends nV{constructor(){super(...arguments),this.paramsConfig=V1}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(U1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new U1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class j1 extends QG{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 yX,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rr.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,Cs.POINTS);return this.createCoreGroupFromObjects([v])}}j1.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},j1.INPUT_CLONED_STATE=Ki.FROM_NODE;const W1=j1.DEFAULT_PARAMS;const q1=new class extends la{constructor(){super(...arguments),this.pointsCount=aa.INTEGER(W1.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=aa.INTEGER(W1.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=aa.BOOLEAN(W1.transferAttributes),this.attributesToTransfer=aa.STRING(W1.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=aa.BOOLEAN(W1.addIdAttribute),this.addIdnAttribute=aa.BOOLEAN(W1.addIdnAttribute)}};class X1 extends nV{constructor(){super(...arguments),this.paramsConfig=q1}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 j1(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Y1;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(Y1||(Y1={}));const $1=[Y1.MATRIX,Y1.AXIS];var J1;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(J1||(J1={}));const Z1=[J1.BBOX_CENTER,J1.BBOX_CENTER_OFFSET,J1.CUSTOM];class Q1 extends QG{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=$1[e.mode];switch(n){case Y1.MATRIX:return this._applyMatrixShear(t,e);case Y1.AXIS:return this._applyAxisShear(t,e)}ls.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 _r(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=Z1[e.centerMode];switch(n){case J1.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case J1.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case J1.CUSTOM:return this._getAxisModeCenterCustom(e)}ls.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 K1;Q1.DEFAULT_PARAMS={mode:$1.indexOf(Y1.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:Z1.indexOf(J1.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},Q1.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\\\\\\\"}(K1||(K1={}));const t2=Q1.DEFAULT_PARAMS;const e2=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(t2.mode,{menu:{entries:$1.map(((t,e)=>({name:t,value:e})))}}),this.xy=aa.FLOAT(t2.xy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.xz=aa.FLOAT(t2.xz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yx=aa.FLOAT(t2.yx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yz=aa.FLOAT(t2.yz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zx=aa.FLOAT(t2.zx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zy=aa.FLOAT(t2.zy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.centerMode=aa.INTEGER(t2.centerMode,{visibleIf:{mode:$1.indexOf(Y1.AXIS)},menu:{entries:Z1.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=aa.VECTOR3(t2.centerOffset.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.BBOX_CENTER_OFFSET)}}),this.center=aa.VECTOR3(t2.center.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.CUSTOM)}}),this.planeAxis=aa.VECTOR3(t2.planeAxis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axis=aa.VECTOR3(t2.axis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axisAmount=aa.FLOAT(t2.axisAmount,{range:[-1,1],visibleIf:{mode:$1.indexOf(Y1.AXIS)}})}};class n2 extends nV{constructor(){super(...arguments),this.paramsConfig=e2}static type(){return K1.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Q1.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set($1.indexOf(t))}cook(t){this._operation=this._operation||new Q1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const i2=new class extends la{};class s2 extends nV{constructor(){super(...arguments),this.paramsConfig=i2}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=c1.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 h1(n,t,e).process(),n}}var r2;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(r2||(r2={}));const o2=[r2.X,r2.Y,r2.Z];class a2 extends QG{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 yr(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=o2[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),r){case r2.X:o=this._pointPos.x;break;case r2.Y:o=this._pointPos.y;break;case r2.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=aa.BOOLEAN(l2.invert)}};class h2 extends nV{constructor(){super(...arguments),this.paramsConfig=c2}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 a2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const u2=new class extends la{constructor(){super(...arguments),this.startFrame=aa.INTEGER(El.START_FRAME)}};class d2 extends rV{constructor(){super(...arguments),this.paramsConfig=u2,this._last_simulated_frame=null,this.childrenDisplayController=new aV(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(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:f2[t]})))}}),this.radius=aa.FLOAT(y2.radius,{visibleIf:{type:f2.default}}),this.resolution=aa.VECTOR2(y2.resolution,{visibleIf:{type:f2.default}}),this.open=aa.BOOLEAN(y2.open,{visibleIf:{type:f2.default}}),this.phiStart=aa.FLOAT(y2.phiStart,{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.phiLength=aa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaStart=aa.FLOAT(y2.thetaStart,{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaLength=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.detail=aa.INTEGER(y2.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:f2.isocahedron}}),this.center=aa.VECTOR3(y2.center)}};class b2 extends nV{constructor(){super(...arguments),this.paramsConfig=x2}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(v2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new v2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const w2=new class extends la{constructor(){super(...arguments),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\")}};class T2 extends nV{constructor(){super(...arguments),this.paramsConfig=w2,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const s=t.object(),r=e.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=Us.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${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=Ls(s.constructor);i.forEach(((t,e)=>{const i=_r.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);yr.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const A2=new A.a,M2=new K.a,E2=new p.a;class S2 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 S2).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}}S2.prototype.isGeometry=!0;class C2{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 N2(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()])}L2.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{z2.PARAM_CALLBACK_reset(t)}})}};class z2 extends nV{constructor(){super(...arguments),this.paramsConfig=B2}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 k2=n(82);class U2 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new k2.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 k2.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)}}V2.DEFAULT_PARAMS={url:G2,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const H2=V2.DEFAULT_PARAMS;const j2=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(H2.url,{fileBrowse:{type:[Or.SVG]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{W2.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=aa.BOOLEAN(H2.drawFillShapes),this.fillShapesWireframe=aa.BOOLEAN(H2.fillShapesWireframe),this.drawStrokes=aa.BOOLEAN(H2.drawStrokes),this.strokesWireframe=aa.BOOLEAN(H2.strokesWireframe)}};class W2 extends nV{constructor(){super(...arguments),this.paramsConfig=j2}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 V2(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 q2=\\\\\\\"geometry to switch to\\\\\\\";const X2=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Y2 extends nV{constructor(){super(...arguments),this.paramsConfig=X2}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[q2,q2,q2,q2]}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 $2 extends gK{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 J2=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.detail=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=aa.BOOLEAN(0),this.center=aa.VECTOR3([0,0,0])}};class Z2 extends nV{constructor(){super(...arguments),this.paramsConfig=J2}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new $2(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,Cs.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class Q2 extends cJ{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 K2=n(48);class t9 extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.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 e9(t)}}class e9{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 s9;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(s9||(s9={}));const r9=[s9.MESH,s9.FLAT,s9.LINE,s9.STROKE],o9=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const a9=new class extends la{constructor(){super(...arguments),this.font=aa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Or.FONT]}}),this.text=aa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=aa.INTEGER(0,{menu:{entries:r9.map(((t,e)=>({name:t,value:e})))}}),this.size=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=aa.FLOAT(.1,{visibleIf:{type:r9.indexOf(s9.MESH)}}),this.segments=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:r9.indexOf(s9.MESH)}}),this.strokeWidth=aa.FLOAT(.02,{visibleIf:{type:r9.indexOf(s9.STROKE)}})}};class l9 extends nV{constructor(){super(...arguments),this.paramsConfig=a9,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(r9[this.pv.type]){case s9.MESH:return this._create_geometry_from_type_mesh(t);case s9.FLAT:return this._create_geometry_from_type_flat(t);case s9.LINE:return this._create_geometry_from_type_line(t);case s9.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 Q2(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(o9)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new _J(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}}c9.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},c9.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const h9=c9.DEFAULT_PARAMS;const u9=new class extends la{constructor(){super(...arguments),this.textureName=aa.STRING(h9.textureName)}};class d9 extends nV{constructor(){super(...arguments),this.paramsConfig=u9}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(c9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new c9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class p9 extends QG{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)}}p9.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:Xm,tmagFilter:!1,magFilter:qm},p9.INPUT_CLONED_STATE=Ki.FROM_NODE;const _9=p9.DEFAULT_PARAMS;const m9=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(_9.applyToChildren,{separatorAfter:!0}),this.tencoding=aa.BOOLEAN(_9.tencoding),this.encoding=aa.INTEGER(_9.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=aa.BOOLEAN(_9.tmapping),this.mapping=aa.INTEGER(_9.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=aa.BOOLEAN(_9.twrap),this.wrapS=aa.INTEGER(_9.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=aa.INTEGER(_9.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=aa.BOOLEAN(_9.tanisotropy),this.useRendererMaxAnisotropy=aa.BOOLEAN(_9.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=aa.INTEGER(_9.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(_9.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(_9.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class f9 extends nV{constructor(){super(...arguments),this.paramsConfig=m9}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(p9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new p9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const g9=new p.a(0,0,1);class v9 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=new fJ(n,i,s,r);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,g9,e.direction),this.createCoreGroupFromGeometry(o)}}v9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},v9.INPUT_CLONED_STATE=Ki.FROM_NODE;const y9=v9.DEFAULT_PARAMS;const x9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(y9.radius,{range:[0,1]}),this.radiusTube=aa.FLOAT(y9.radiusTube,{range:[0,1]}),this.segmentsRadial=aa.INTEGER(y9.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=aa.INTEGER(y9.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=aa.VECTOR3(y9.direction),this.center=aa.VECTOR3(y9.center)}};class b9 extends nV{constructor(){super(...arguments),this.paramsConfig=x9}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new v9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class w9 extends QG{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 gJ(n,i,s,r,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}w9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},w9.INPUT_CLONED_STATE=Ki.FROM_NODE;const T9=w9.DEFAULT_PARAMS;const A9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(T9.radius),this.radiusTube=aa.FLOAT(T9.radiusTube),this.segmentsRadial=aa.INTEGER(T9.segmentsRadial,{range:[1,128]}),this.segmentsTube=aa.INTEGER(T9.segmentsTube,{range:[1,32]}),this.p=aa.INTEGER(T9.p,{range:[1,10]}),this.q=aa.INTEGER(T9.q,{range:[1,10]}),this.center=aa.VECTOR3(T9.center)}};class M9 extends nV{constructor(){super(...arguments),this.paramsConfig=A9}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new w9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var E9;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(E9||(E9={}));const S9=[E9.SET_PARAMS,E9.UPDATE_MATRIX];class C9 extends QG{constructor(){super(...arguments),this._core_transform=new uU,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=aU[e.applyOn];switch(n){case sU.GEOMETRIES:return this._update_geometries(t,e);case sU.OBJECTS:return this._update_objects(t,e)}ls.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=ZG._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=S9[e.objectMode];switch(n){case E9.SET_PARAMS:return this._update_objects_params(t,e);case E9.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ls.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=cU[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,cU[t.rotationOrder])}}C9.DEFAULT_PARAMS={applyOn:aU.indexOf(sU.GEOMETRIES),objectMode:S9.indexOf(E9.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:cU.indexOf(lU.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)},C9.INPUT_CLONED_STATE=Ki.FROM_NODE;const N9=C9.DEFAULT_PARAMS;const L9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(N9.applyOn,{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=aa.INTEGER(N9.objectMode,{visibleIf:{applyOn:aU.indexOf(sU.OBJECTS)},menu:{entries:S9.map(((t,e)=>({name:t,value:e})))}}),this.group=aa.STRING(N9.group,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}}),this.rotationOrder=aa.INTEGER(N9.rotationOrder,{menu:{entries:cU.map(((t,e)=>({name:t,value:e})))}}),this.t=aa.VECTOR3(N9.t),this.r=aa.VECTOR3(N9.r),this.s=aa.VECTOR3(N9.s),this.scale=aa.FLOAT(N9.scale,{range:[0,10]}),this.pivot=aa.VECTOR3(N9.pivot,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}})}};class O9 extends nV{constructor(){super(...arguments),this.paramsConfig=L9}static type(){return K1.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(C9.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(aU.indexOf(t))}setObjectMode(t){this.p.objectMode.set(S9.indexOf(t))}cook(t){this._operation=this._operation||new C9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const P9=new class extends la{constructor(){super(...arguments),this.useSecondInput=aa.BOOLEAN(1),this.reference=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},visibleIf:{useSecondInput:0}})}};class R9 extends nV{constructor(){super(...arguments),this.paramsConfig=P9}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 D9(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const B9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(aU.indexOf(sU.GEOMETRIES),{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.count=aa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(0)}),this.r0=aa.VECTOR3([0,0,0],{...D9(0)}),this.rotationOrder1=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(1)}),this.r1=aa.VECTOR3([0,0,0],{...D9(1)}),this.rotationOrder2=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(2)}),this.r2=aa.VECTOR3([0,0,0],{...D9(2)}),this.rotationOrder3=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(3)}),this.r3=aa.VECTOR3([0,0,0],{...D9(3)}),this.rotationOrder4=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(4)}),this.r4=aa.VECTOR3([0,0,0],{...D9(4)}),this.rotationOrder5=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(5)}),this.r5=aa.VECTOR3([0,0,0],{...D9(5)})}};class z9 extends nV{constructor(){super(...arguments),this.paramsConfig=B9,this._core_transform=new uU,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],(()=>aU[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=aU[this.pv.applyOn];switch(n){case sU.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case sU.OBJECTS:return this._apply_matrix_to_objects(t,e)}ls.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=[js.POSITION,js.NORMAL,js.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&qs.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class V9 extends nV{constructor(){super(...arguments),this.paramsConfig=G9,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(U9.indexOf(t))}cook(t){const e=U9[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case k9.RESET_OBJECT:return this._reset_objects(e);case k9.CENTER_GEO:return this._center_geos(e,!1);case k9.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ls.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),uU.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 ho,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 zr.create(this.param,e,t,n)}}class f3 extends m3{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 g3 extends m3{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 v3=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],y3=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class x3 extends m3{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(v3.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?y3.indexOf(n)>=0?t[n]:-1:t}return-1}}class b3 extends m3{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 w3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new ho,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 T3 extends m3{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()==fZ.type()&&(a=o.stamp_value(s)),null==a&&(a=i),e(a)}else e(0)}))}}class A3 extends m3{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 M3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class E3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class S3 extends m3{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 ${t}`)}}class C3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${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 N3 extends m3{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 L3 extends m3{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(os.tailDigits(t))}else e(0)}else e(0)}))}}class O3 extends m3{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 P3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class R3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${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 I3 extends m3{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 F3 extends m3{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 D3 extends m3{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class B3 extends m3{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 z3 extends m3{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 k3 extends m3{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 U3{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=JG.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()==Er.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 G3{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new U3(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 V3;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(V3||(V3={}));class H3{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 j3{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 W3{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 q3{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 W3(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 X3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\";\\\\\\\",[V3.BODY]:\\\\\\\";\\\\\\\"},Y3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\"\\\\\\\",[V3.BODY]:\\\\\\\"\\\\t\\\\\\\"};class $3{static node_comment(t,e){let n=`// ${t.path()}`,i=Y3[e];if(e==V3.BODY){let e=this.node_distance_to_material(t);t.type()==ns.OUTPUT&&(e+=1),i=i.repeat(e)}return e==V3.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=Y3[n];if(n==V3.BODY&&(s=s.repeat(this.node_distance_to_material(t))),e=`${s}${e}`,i){const t=e[e.length-1],i=X3[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==V3.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 J3{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new wF,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 q3(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,yf.FUNCTION,V3.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,V3.DEFINE),this.addDefinitions(t,e,yf.VARYING,V3.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,V3.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,V3.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 vf(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=$3.node_comment(t.node,i);h.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=$3.line_wrap(t.node,e.line,i);h.pushOnArrayAtEntry(a,i,n)}const s=$3.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=$3.node_comment(t,n);if(h.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((e=>{e=$3.line_wrap(t,e,n),h.pushOnArrayAtEntry(r,n,e)})),n!=V3.BODY||!i){const t=$3.post_line_separator(n);h.pushOnArrayAtEntry(r,n,t)}}}}class Z3{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=xf.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 Q3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),K3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),t4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class e4 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 xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.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 bF.type():case AF.type():e.push(t);break;case gf.type():case Lf.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 AI.type():e.push(t);break;case gf.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 Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new J3(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.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 Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(e4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"mvPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldNormal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"gl_Position\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"cameraPosition\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return e4.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 H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.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 j3(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new j3(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return e4.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 Q3.get(t)}insert_body_after(t){return K3.get(t)}lines_to_remove(t){return t4.get(t)}_replace_template(t,e){const n=this.builder_lines(e,V3.FUNCTION_DECLARATION),i=this.builder_lines(e,V3.DEFINE),s=this.builder_lines(e,V3.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 n4,i4;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(n4||(n4={})),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\\\\\\\"}(i4||(i4={}));const s4=[i4.GL_FRAGCOORD,i4.GL_POINTCOORD];class r4 extends e4{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=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const s=this._shaders_by_name.get(xf.VERTEX),r=this._shaders_by_name.get(xf.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=hf.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 i4.TIME:return void this.handleTime(t);case i4.RESOLUTION:return void this.handle_resolution(t);case i4.MV_POSITION:return void this.handle_mvPosition(t);case i4.GL_POSITION:return void this.handle_gl_Position(t);case i4.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case i4.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 Af(t.globals_node,Bo.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 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Bo.VEC2,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.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==xf.VERTEX&&s4.includes(t)||i.push(t);return i}}const o4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const a4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.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 c4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),h4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const u4=new Map([]);u4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return o4.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})}}),u4.set(n4.DEPTH,class extends r4{templateShader(){const t=H.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return a4.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})}}),u4.set(n4.DEPTH_DOF,class extends r4{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 c4.get(t)}insert_body_after(t){return h4.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class d4 extends r4{custom_assembler_class_by_custom_name(){return u4}}class p4 extends d4{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 _4 extends d4{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 m4 extends d4{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 f4=\\\\\\\"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 g4 extends d4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",g4.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;\\\\\\\"),g4.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=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"metalness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"roughness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"emissive\\\\\\\",Bo.VEC3,[1,1,1])),g4.USE_SSS&&e.push(new Ho(\\\\\\\"SSSModel\\\\\\\",Bo.SSS_MODEL,f4)),this.isPhysical()&&(e.push(new Ho(\\\\\\\"transmission\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"thickness\\\\\\\",Bo.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 H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,t,[xf.VERTEX])]}create_variable_configs(){const t=e4.create_variable_configs();return t.push(new j3(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new j3(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new j3(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),g4.USE_SSS&&t.push(new j3(\\\\\\\"SSSModel\\\\\\\",{default:f4,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),this.isPhysical()&&(t.push(new j3(\\\\\\\"transmission\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_transmission = \\\\\\\"})),t.push(new j3(\\\\\\\"thickness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_thickness = \\\\\\\"}))),t}}g4.USE_SSS=!0;class v4 extends g4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshPhysicalBuilderMatNode\\\\\\\",v4.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 y4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),x4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const b4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),w4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const T4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),A4=new Map;A4.set(n4.DISTANCE,class extends r4{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 y4.get(t)}insert_body_after(t){return x4.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})}}),A4.set(n4.DEPTH_DOF,class extends r4{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 b4.get(t)}insert_body_after(t){return w4.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 M4 extends r4{custom_assembler_class_by_custom_name(){return A4}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=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"gl_PointSize\\\\\\\",Bo.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return e4.create_globals_node_output_connections().concat([new Ho(i4.GL_POINTCOORD,Bo.VEC2)])}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return e4.create_variable_configs().concat([new j3(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return T4.get(t)}}const E4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),S4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const C4=new Map([]);C4.set(n4.DEPTH_DOF,class extends r4{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 E4.get(t)}insert_body_after(t){return S4.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 N4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class L4 extends r4{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 console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",C4),C4}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(L4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return L4.create_globals_node_output_connections()}create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return N4.get(t)}}class O4 extends e4{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 bF.type():i.push(s);break;case gf.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 AI.type():i.push(s);break;case gf.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 Z3(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 O0,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 Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new O0}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=hf.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 Af(t,Bo.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 P4 extends e4{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(xf.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==xf.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\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}create_shader_configs(){return[new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"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 = ${hf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${hf.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 Af(t,Bo.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 R4=new Map([]);class I4 extends r4{custom_assembler_class_by_custom_name(){return R4}}const F4=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),D4=new Map([[xf.FRAGMENT,[]]]);class B4 extends I4{templateShader(){return{vertexShader:Lk,fragmentShader:Ok,uniforms:I.clone(Pk)}}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 gr.add_user_data_render_hook(e,Ik.render_hook.bind(Ik)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"density\\\\\\\",Bo.FLOAT,1)])}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"pos_normalized\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return B4.create_globals_node_output_connections()}insert_body_after(t){return F4.get(t)}lines_to_remove(t){return D4.get(t)}create_shader_configs(){return[new H3(xf.VERTEX,[],[]),new H3(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{}),new j3(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return B4.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 Af(t,Bo.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==xf.FRAGMENT&&n.push(`vec3 ${u} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class z4{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(z_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(hm,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Mm,Kl),t.registerNode(Em,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Eg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(zg,ic),t.registerNode(Kg,ic),t.registerNode(iv,ic),t.registerNode(lv,sc),t.registerNode(hv,sc),t.registerNode(_v,sc),t.registerNode(fv,sc),t.registerNode(yv,nc),t.registerNode(wv,ic),t.registerNode(Mv,nc),t.registerNode(Cv,rc),t.registerNode(Lv,rc),t.registerNode(Nv,rc),t.registerNode(Ov,rc),t.registerNode(Pv,rc),t.registerNode(Rv,rc)}}.run(t),class{static run(t){t.registerNode(Bv,cc),t.registerNode(Uv,lc),t.registerNode(Vv,lc),t.registerNode(Wv,lc),t.registerNode(ey,oc),t.registerNode(_y,oc),t.registerNode(py,oc),t.registerNode(fy,lc),t.registerNode(xl,ac),t.registerNode(Yy,oc),t.registerNode(al,ac),t.registerNode(Qy,lc),t.registerNode(tx,lc),t.registerNode(AL,oc),t.registerNode(Xa,ac),t.registerNode(CL,cc),t.registerNode(PL,lc),t.registerNode(GL,ac),t.registerNode(Qa,ac),t.registerNode(hO,lc),t.registerNode(nl,cc),t.registerNode(_O,cc),t.registerNode(MO,cc),t.registerNode(SO,lc),t.registerNode(LO,lc),t.registerNode(wl,ac),t.registerNode(PO,lc),t.registerNode(dl,ac),t.registerNode(FO,hc),t.registerNode(DO,hc),t.registerNode(BO,hc),t.registerNode(zO,hc),t.registerNode(kO,hc),t.registerNode(UO,hc)}}.run(t),class{static run(t){t.registerNode(MP,gc),t.registerNode(xR,vc),t.registerNode(EP,xc),t.registerNode(rR,gc),t.registerNode(MR,xc),t.registerNode(uR,fc),t.registerNode(SP,xc),t.registerNode(CP,xc),t.registerNode(gf,_c,{except:[`${ts.COP}/builder`]}),t.registerNode(ZO,dc),t.registerNode(NP,gc),t.registerNode(eR,gc),t.registerNode(NR,uc),t.registerNode(DR,fc),t.registerNode(BR,gc),t.registerNode(UR,_c),t.registerNode(LP,xc),t.registerNode(HR,pc),t.registerNode(jR,gc),t.registerNode(OP,dc),t.registerNode(XR,pc),t.registerNode(qP,pc),t.registerNode(oR,gc),t.registerNode(XP,pc),t.registerNode(eI,gc),t.registerNode(PP,gc),t.registerNode(RP,gc),t.registerNode(nR,pc),t.registerNode(sI,gc),t.registerNode(oI,gc),t.registerNode(lI,gc),t.registerNode(hI,gc),t.registerNode(HO,dc),t.registerNode(KO,dc),t.registerNode(eP,dc),t.registerNode(iP,dc),t.registerNode(IP,gc),t.registerNode(pI,uc),t.registerNode(wI,fc),t.registerNode(FP,gc),t.registerNode(AI,_c),t.registerNode(EI,uc),t.registerNode(CI,uc),t.registerNode(OI,fc),t.registerNode(RI,bc),t.registerNode($O,dc),t.registerNode(qO,dc),t.registerNode(DP,gc),t.registerNode(UI,pc),t.registerNode(GI,pc),t.registerNode(HI,uc),t.registerNode(BP,gc),t.registerNode(zP,gc),t.registerNode(YP,gc),t.registerNode(WI,gc),t.registerNode($P,gc),t.registerNode(JP,gc),t.registerNode(JI,gc),t.registerNode(XI,gc),t.registerNode(lR,gc),t.registerNode(KI,gc),t.registerNode(tF,gc),t.registerNode(yF,bc),t.registerNode(vF,pc),t.registerNode(kP,gc),t.registerNode(dR,fc),t.registerNode(bF,_c),t.registerNode(AF,_c),t.registerNode(ZP,gc),t.registerNode(NF,yc),t.registerNode(RF,yc),t.registerNode(IF,yc),t.registerNode(FF,yc),t.registerNode(zF,_c),t.registerNode(GF,_c),t.registerNode(UP,dc),t.registerNode(QP,pc),t.registerNode(MF,pc),t.registerNode(HF,uc),t.registerNode(QF,pc),t.registerNode(tD,gc),t.registerNode(GP,gc),t.registerNode(VP,xc),t.registerNode(iR,gc),t.registerNode(nD,pc),t.registerNode(HP,gc),t.registerNode(CF,mc),t.registerNode(KP,pc),t.registerNode(vI,fc),t.registerNode(sD,fc,TD),t.registerNode(mI,fc,TD),t.registerNode(aR,gc),t.registerNode(oD,fc),t.registerNode(jP,xc),t.registerNode(lD,uc),t.registerNode(dD,_c),t.registerNode(mD,fc),t.registerNode(Lf,_c),t.registerNode(vD,_c),t.registerNode(hP,dc),t.registerNode(mP,dc),t.registerNode(uP,dc),t.registerNode(_P,dc),t.registerNode(fP,dc),t.registerNode(dP,dc),t.registerNode(pP,dc),t.registerNode(xD,pc),t.registerNode(wD,pc)}}.run(t),class{static run(t){t.registerNode(SD,wc),t.registerNode(ND,wc),t.registerNode(OD,wc),t.registerNode(zD,wc)}}.run(t),class{static run(t){t.registerNode(XD,Ac),t.registerNode(rB,Ac),t.registerNode(DB,Mc),t.registerNode(VB,Tc),t.registerNode(YB,Mc),t.registerNode(tz,Tc),t.registerNode(mz,Mc),t.registerNode(yz,Mc),t.registerNode(Mz,Mc),t.registerNode(Nz,Tc),t.registerNode(Uz,Mc),t.registerNode(jz,Tc),t.registerNode(Yz,Mc),t.registerNode(Qz,Tc),t.registerNode(hk,Mc),t.registerNode(fk,Mc),t.registerNode(xk,Sc),t.registerNode(Tk,Tc),t.registerNode(Ek,Tc),t.registerNode(Nk,Mc),t.registerNode(Bk,Cc),t.registerNode(Uk,Cc),t.registerNode(Hk,Ec),t.registerNode(jk,Ec),t.registerNode(Wk,Ec),t.registerNode(qk,Ec),t.registerNode(Xk,Ec),t.registerNode(Yk,Ec)}}.run(t),class{static run(t){t.registerNode(nU,Rc),t.registerNode(MU,Rc),t.registerNode(DU,Rc),t.registerNode(HU,Rc),t.registerNode($U,Rc),t.registerNode(iG,Rc),t.registerNode(pG,Lc),t.registerNode(vG,Fc),t.registerNode(CG,Nc),t.registerNode(RG,Pc),t.registerNode(DG,Fc),t.registerNode(GG,Fc),t.registerNode(_V,Nc),t.registerNode(NV,Lc),t.registerNode(RV,Fc),t.registerNode(DV,Nc),t.registerNode(XH,Oc),t.registerNode(ZH,Oc),t.registerNode(tj,Oc),t.registerNode(sj,Ic),t.registerNode(rj,Ic),t.registerNode(oj,Ic),t.registerNode(aj,Ic),t.registerNode(lj,Ic),t.registerNode(cj,Ic)}}.run(t),class{static run(t){t.registerNode(gj,Qc),t.registerNode(bj,Qc),t.registerNode(Aj,Zc),t.registerNode(Sj,Zc),t.registerNode(Lj,Kc),t.registerNode(Pj,Kc),t.registerNode(Fj,Kc),t.registerNode(Bj,Kc),t.registerNode(qj,Qc),t.registerNode(Hj,Qc),t.registerNode(Jj,Qc),t.registerNode(Kj,Kc),t.registerNode(nW,Zc),t.registerNode(sW,Jc),t.registerNode(oW,Kc),t.registerNode(dW,Kc),t.registerNode(_W,Kc),t.registerNode(fW,Kc),t.registerNode(yW,Qc),t.registerNode(wW,Qc),t.registerNode(AW,Kc),t.registerNode(SW,Qc),t.registerNode(LW,Zc),t.registerNode(PW,Kc),t.registerNode(DW,Jc),t.registerNode(UW,Qc),t.registerNode(VW,Jc),t.registerNode(WW,Qc),t.registerNode(YW,th),t.registerNode($W,th),t.registerNode(JW,th),t.registerNode(ZW,th),t.registerNode(QW,th),t.registerNode(KW,th)}}.run(t),class{static run(t){t.registerNode(oq,Dc),t.registerNode(vH,zc),t.registerNode(aq,Bc),t.registerNode(nq,Bc),t.registerNode(lq,Bc),t.registerNode(cq,Bc),t.registerNode(hq,Bc),t.registerNode(uq,Bc)}}.run(t),class{static run(t){t.registerOperation(dq),t.registerOperation(Eq),t.registerOperation(Iq),t.registerOperation(zq),t.registerOperation(Vq),t.registerOperation(nX),t.registerOperation(Jq),t.registerOperation(aX),t.registerOperation(UX),t.registerOperation(jX),t.registerOperation(g$),t.registerOperation(x$),t.registerOperation(IJ),t.registerOperation(kJ),t.registerOperation(xZ),t.registerOperation(kZ),t.registerOperation(ZQ),t.registerOperation(cK),t.registerOperation(yK),t.registerOperation(TK),t.registerOperation(NK),t.registerOperation(HK),t.registerOperation(KK),t.registerOperation(kK),t.registerOperation(h0),t.registerOperation(m0),t.registerOperation(F0),t.registerOperation(V0),t.registerOperation(q0),t.registerOperation(K0),t.registerOperation(r1),t.registerOperation(f1),t.registerOperation(M1),t.registerOperation(D1),t.registerOperation(U1),t.registerOperation(j1),t.registerOperation(Q1),t.registerOperation(a2),t.registerOperation(v2),t.registerOperation(O2),t.registerOperation(V2),t.registerOperation(c9),t.registerOperation(p9),t.registerOperation(v9),t.registerOperation(w9),t.registerOperation(C9),t.registerOperation(X9),t.registerOperation(t3),t.registerOperation(s3),t.registerNode(mq,Hc),t.registerNode(gq,Uc),t.registerNode(Mq,Uc),t.registerNode(Nq,Gc),t.registerNode(Bq,Gc),t.registerNode(Gq,Gc),t.registerNode(qq,Gc),t.registerNode(Yq,Gc),t.registerNode(Kq,Gc),t.registerNode(rX,Gc),t.registerNode(uX,Gc),t.registerNode(pX,Gc),t.registerNode(mX,Gc),t.registerNode(bX,Gc),t.registerNode(TX,qc),t.registerNode(MX,qc),t.registerNode(HX,qc),t.registerNode(XX,Yc),t.registerNode(y$,kc),t.registerNode(T$,kc),t.registerNode(SJ,Wc),t.registerNode(RJ,Yc),t.registerNode(DJ,Yc),t.registerNode(VJ,Yc),t.registerNode($J,Yc),t.registerNode(eZ,qc),t.registerNode(sZ,Yc),t.registerNode(fZ,qc),t.registerNode(TZ,Yc),t.registerNode(CZ,Hc),t.registerNode(DZ,Hc),t.registerNode(VZ,Wc),t.registerNode(jZ,Wc),t.registerNode(rQ,qc),t.registerNode(aQ,qc),t.registerNode(HQ,kc),t.registerNode(WQ,qc),t.registerNode(tK,Hc),t.registerNode(nK,qc),t.registerNode(oK,Yc),t.registerNode(mK,qc),t.registerNode(pK,Wc),t.registerNode(wK,Yc),t.registerNode(EK,$c),t.registerNode(CK,$c),t.registerNode(PK,qc),t.registerNode(IK,qc),t.registerNode(DK,Yc),t.registerNode(zK,kc),t.registerNode(VK,$c),t.registerNode(XK,Wc),t.registerNode(n0,Yc),t.registerNode(a0,Wc),t.registerNode(c0,qc),t.registerNode(d0,Wc),t.registerNode(_0,Hc),t.registerNode(v0,qc),t.registerNode(x0,kc,{userAllowed:!1}),t.registerNode(I0,Vc),t.registerNode(z0,qc),t.registerNode(W0,Yc),t.registerNode($0,qc),t.registerNode(l1,qc),t.registerNode(Q0,qc),t.registerNode(n1,jc),t.registerNode(hV,kc),t.registerNode(_1,qc),t.registerNode(y1,qc),t.registerNode(C1,$c),t.registerNode(F1,qc),t.registerNode(k1,Gc),t.registerNode(H1,Hc),t.registerNode(X1,qc),t.registerNode(s2,qc),t.registerNode(n2,qc),t.registerNode(d2,kc),t.registerNode(_2,kc),t.registerNode(h2,qc),t.registerNode(b2,Yc),t.registerNode(T2,qc),t.registerNode(I2,qc),t.registerNode(D2,Wc),t.registerNode(z2,Wc),t.registerNode(sV,Wc),t.registerNode(W2,Hc),t.registerNode(Y2,Wc),t.registerNode(Z2,Yc),t.registerNode(l9,Yc),t.registerNode(d9,qc),t.registerNode(f9,qc),t.registerNode(b9,Yc),t.registerNode(M9,Yc),t.registerNode(O9,qc),t.registerNode(R9,qc),t.registerNode(z9,qc),t.registerNode(V9,qc),t.registerNode(W9,Yc),t.registerNode($9,qc),t.registerNode(K9,qc),t.registerNode(i3,qc),t.registerNode(o3,qc),t.registerNode(c3,Xc),t.registerNode(h3,Xc),t.registerNode(u3,Xc),t.registerNode(d3,Xc),t.registerNode(p3,Xc),t.registerNode(_3,Xc)}}.run(t)}}.run(li),class{static run(t){t.registerCamera(XH),t.registerCamera(ZH)}}.run(li),class{static run(t){t.expressionsRegister.register(f3,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(g3,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(x3,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(b3,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(w3,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(T3,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(A3,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(M3,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(E3,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(S3,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(C3,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(N3,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(L3,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(O3,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(P3,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(R3,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(I3,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(F3,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(D3,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(B3,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(z3,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(k3,\\\\\\\"windowSize\\\\\\\")}}.run(li),class{static run(t){t.assemblersRegister.register(jn.GL_MESH_BASIC,G3,p4),t.assemblersRegister.register(jn.GL_MESH_LAMBERT,G3,_4),t.assemblersRegister.register(jn.GL_MESH_PHONG,G3,m4),t.assemblersRegister.register(jn.GL_MESH_STANDARD,G3,g4),t.assemblersRegister.register(jn.GL_MESH_PHYSICAL,G3,v4),t.assemblersRegister.register(jn.GL_PARTICLES,G3,O4),t.assemblersRegister.register(jn.GL_POINTS,G3,M4),t.assemblersRegister.register(jn.GL_LINE,G3,L4),t.assemblersRegister.register(jn.GL_TEXTURE,G3,P4),t.assemblersRegister.register(jn.GL_VOLUME,G3,B4)}}.run(li))}}z4._started=!1,z4.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":"2813173"}},"type":2,"external":true,"timestamp":1723912759362},{"data":{"url":"blob:https://ipfs.arkivo.art/4ecc1940-1dec-454c-af21-25743c5e159d","host":"","path":"https://ipfs.arkivo.art/4ecc1940-1dec-454c-af21-25743c5e159d","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":1723912759374},{"data":{"url":"blob:https://ipfs.arkivo.art/4ecc1940-1dec-454c-af21-25743c5e159d","body":"\"// ../../Documents/PJS/src/polygonjs/PolyConfig.js\\nfunction configurePolygonjs(poly) {\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n configureScene\\n};\\n\"","status":200,"headers":{"content-type":"application/javascript","content-length":"175"}},"type":2,"external":true,"timestamp":1723912764898}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized 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