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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,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=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],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+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],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+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],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*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]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],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),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*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,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,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-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*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 r=s.set(i[0],i[1],i[2]).length();const a=s.set(i[4],i[5],i[6]).length(),l=s.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/r,u=1/a,h=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=u,o.elements[5]*=u,o.elements[6]*=u,o.elements[8]*=h,o.elements[9]*=h,o.elements[10]*=h,e.setFromRotationMatrix(o),n.x=r,n.y=a,n.z=l,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,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}}r.prototype.isMatrix4=!0;const s=new i.a,o=new r,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,u=new i.a,h=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(3);const r={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},s={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 u{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,r=2*n-i;this.r=a(r,i,t+1/3),this.g=a(r,i,t),this.b=a(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.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=r[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const 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,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){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 h.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 r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));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 r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-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,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}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],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+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,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*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,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*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=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,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(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}r.prototype.isQuaternion=!0},function(t,e,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,r=this.w,s=t.elements;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.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)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),r.length>0&&(n.textures=r),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 s(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,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,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,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=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 r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*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],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,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,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+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,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+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),r=n(1),s=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=s.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=r.xb,this.side=r.H,this.vertexColors=!1,this.opacity=1,this.format=r.Ib,this.transparent=!1,this.blendSrc=r.Nc,this.blendDst=r.Db,this.blendEquation=r.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=r.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=r.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=r.R,this.stencilZFail=r.R,this.stencilZPass=r.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const 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===r.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!==r.xb&&(n.blending=this.blending),this.side!==r.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==r.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),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}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 r}));var i=n(29);class r{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),r=n(2),s=n(18),o=n(39),a=n(5),l=n(10),c=n(40),u=n(1),h=n(27),d=n(7);const p=new a.a,_=new o.a,m=new s.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,E=new r.a,M=new r.a,S=new r.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new h.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,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(r),!1===t.ray.intersectsSphere(m))return;if(p.copy(r).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,s,f,g,v,C);if(O){h&&(E.fromBufferAttribute(h,p),M.fromBufferAttribute(h,_),S.fromBufferAttribute(h,m),O.uv=c.a.getUV(C,f,g,v,E,M,S,new r.a)),d&&(E.fromBufferAttribute(d,p),M.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,E,M,S,new r.a));const t={a:p,b:_,c:m,normal:new 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s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,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.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}}o.prototype.isMeshBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(8),r=n(0),s=n(5),o=n(3);const a=new s.a,l=new i.a;class c{constructor(t=0,e=0,n=0,i=c.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,r=i[0],s=i[4],a=i[8],l=i[1],c=i[5],u=i[9],h=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(o.d)(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-u,p),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-h,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-h,p),this._z=Math.atan2(-s,c)):(this._y=0,this._z=Math.atan2(l,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,r)):(this._x=0,this._z=Math.atan2(-s,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-u,c),this._y=Math.atan2(-h,r)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-u,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return a.makeRotationFromQuaternion(t),this.setFromRotationMatrix(a,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return l.setFromEuler(this),this.setFromQuaternion(l,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new r.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}c.prototype.isEuler=!0,c.DefaultOrder=\\\\\\\"XYZ\\\\\\\",c.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let r,s=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===s&&void 0!==i.onStart&&i.onStart(t,o,a),s=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||a.getNormalMatrix(t),i=this.coplanarPoint(s).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}l.prototype.isPlane=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(41),r=n(0),s=n(4);const o=new r.a,a=new r.a;class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;t0)if(p=l*u-c,_=l*c-u,f=r*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*u)+h}else _=r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _=-r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _<=-f?(p=Math.max(0,-(-l*r+c)),_=p>0?-r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h):_<=f?(p=0,_=Math.min(Math.max(-r,-u),r),m=_*(_+2*u)+h):(p=Math.max(0,-(l*r+c)),_=p>0?r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h);else _=l>0?-r:r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(s),m}intersectSphere(t,e){r.subVectors(t.center,this.origin);const n=r.dot(this.direction),i=r.dot(r)-n*n,s=t.radius*t.radius;if(i>s)return null;const o=Math.sqrt(s-i),a=n-o,l=n+o;return a<0&&l<0?null:a<0?this.at(l,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,r)}intersectTriangle(t,e,n,i,r){l.subVectors(e,t),c.subVectors(n,t),u.crossVectors(l,c);let s,o=this.direction.dot(u);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}a.subVectors(this.origin,t);const h=s*this.direction.dot(c.crossVectors(a,c));if(h<0)return null;const d=s*this.direction.dot(l.cross(a));if(d<0)return null;if(h+d>o)return null;const p=-s*a.dot(u);return p<0?null:this.at(p/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const r=new i.a,s=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,u=new i.a,h=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),r.subVectors(t,e),i.cross(r);const s=i.lengthSq();return s>0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){r.subVectors(i,e),s.subVectors(n,e),o.subVectors(t,e);const l=r.dot(r),c=r.dot(s),u=r.dot(o),h=s.dot(s),d=s.dot(o),p=l*h-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(h*u-c*d)*_,f=(l*d-c*u)*_;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,r,s,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(r,a.x),l.addScaledVector(s,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return r.subVectors(n,e),s.subVectors(t,e),r.cross(s).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 r.subVectors(this.c,this.b),s.subVectors(this.a,this.b),.5*r.cross(s).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,r){return _.getUV(t,this.a,this.b,this.c,e,n,i,r)}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,r=this.c;let s,o;l.subVectors(i,n),c.subVectors(r,n),h.subVectors(t,n);const a=l.dot(h),_=c.dot(h);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 s=a/(a-m),e.copy(n).addScaledVector(l,s);p.subVectors(t,r);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(r);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 u.subVectors(r,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(u,o);const w=1/(b+x+g);return s=x*w,o=g*w,e.copy(n).addScaledVector(l,s).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),r=n(39),s=n(5),o=n(10),a=n(0),l=n(24),c=n(7),u=n(4);const h=new a.a,d=new a.a,p=new s.a,_=new r.a,m=new i.a;class f extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;tl)continue;d.applyMatrix4(this.matrixWorld);const a=t.ray.origin.distanceTo(d);at.far||e.push({distance:a,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;nl)continue;d.applyMatrix4(this.matrixWorld);const 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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}}a.prototype.isMeshPhysicalMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));const i=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",r=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),s=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",o=\\\\\\\"[^\\\\\\\"+i.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",a=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",s),l=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",o),c=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",s),u=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",s),h=new RegExp(\\\\\\\"^\\\\\\\"+a+l+c+u+\\\\\\\"$\\\\\\\"),d=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class p{constructor(t,e,n){this.path=e,this.parsedPath=n||p.parseTrackName(e),this.node=p.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 p.Composite(t,e,n):new p(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(r,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=h.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!==d.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;ii.length-2?i.length-1:a+1],d=i[a>i.length-3?i.length-1:a+2];return n.set(Object(r.a)(l,c.x,u.x,h.x,d.x),Object(r.a)(l,c.y,u.y,h.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;ethis.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 r.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)}}s.prototype.isBox2=!0;var o=n(7),a=n(21),l=n(4),c=n(13),u=n(11),h=n(47),d=n(46),p=n(48),_=n(53),m=n(0);class f extends c.a{constructor(t){super(t),this.defaultDPI=90,this.defaultUnit=\\\\\\\"px\\\\\\\"}load(t,e,n,i){const r=this,s=new a.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(n))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this;function n(t,e,n,i,s,o,a,l){if(0==e||0==n)return void t.lineTo(l.x,l.y);i=i*Math.PI/180,e=Math.abs(e),n=Math.abs(n);const c=(a.x-l.x)/2,u=(a.y-l.y)/2,h=Math.cos(i)*c+Math.sin(i)*u,d=-Math.sin(i)*c+Math.cos(i)*u;let p=e*e,_=n*n;const m=h*h,f=d*d,g=m/p+f/_;if(g>1){const t=Math.sqrt(g);p=(e*=t)*e,_=(n*=t)*n}const v=p*f+_*m,y=(p*_-v)/v;let x=Math.sqrt(Math.max(0,y));s===o&&(x=-x);const b=x*e*d/n,w=-x*n*h/e,T=Math.cos(i)*b-Math.sin(i)*w+(a.x+l.x)/2,A=Math.sin(i)*b+Math.cos(i)*w+(a.y+l.y)/2,E=r(1,0,(h-b)/e,(d-w)/n),M=r((h-b)/e,(d-w)/n,(-h-b)/e,(-d-w)/n)%(2*Math.PI);t.currentPath.absellipse(T,A,e,n,E,E+M,0===o,i)}function r(t,e,n,i){const r=t*n+e*i,s=Math.sqrt(t*t+e*e)*Math.sqrt(n*n+i*i);let o=Math.acos(Math.max(-1,Math.min(1,r/s)));return t*i-e*n<0&&(o=-o),o}function s(t,e){e=Object.assign({},e);let n={};if(t.hasAttribute(\\\\\\\"class\\\\\\\")){const e=t.getAttribute(\\\\\\\"class\\\\\\\").split(/\\\\s/).filter(Boolean).map((t=>t.trim()));for(let t=0;t=0;t--){const r=i[t].trim();if(\\\\\\\"\\\\\\\"===r)continue;const s=r.indexOf(\\\\\\\"(\\\\\\\"),o=r.length;if(s>0&&s=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.translate(t,i)}break;case\\\\\\\"rotate\\\\\\\":if(e.length>=1){let t=0,i=0,r=0;t=-e[0]*Math.PI/180,e.length>=3&&(i=e[1],r=e[2]),b.identity().translate(-i,-r),w.identity().rotate(t),T.multiplyMatrices(w,b),b.identity().translate(i,r),n.multiplyMatrices(b,T)}break;case\\\\\\\"scale\\\\\\\":if(e.length>=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.scale(t,i)}break;case\\\\\\\"skewX\\\\\\\":1===e.length&&n.set(1,Math.tan(e[0]*Math.PI/180),0,0,1,0,0,0,1);break;case\\\\\\\"skewY\\\\\\\":1===e.length&&n.set(1,0,0,Math.tan(e[0]*Math.PI/180),1,0,0,0,1);break;case\\\\\\\"matrix\\\\\\\":6===e.length&&n.set(e[0],e[2],e[4],e[1],e[3],e[5],0,0,1)}}e.premultiply(n)}}return e}(t);y.length>0&&e.premultiply(y[y.length-1]);return M.copy(e),y.push(e),e}(e);let c=!0,m=null;switch(e.nodeName){case\\\\\\\"svg\\\\\\\":break;case\\\\\\\"style\\\\\\\":!function(t){if(!t.sheet||!t.sheet.cssRules||!t.sheet.cssRules.length)return;for(let e=0;et.trim()));for(let t=0;t0&&(r.copy(l),e.currentPath.currentPoint.copy(r),c=!0);break;default:console.warn(i)}u=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new p.a;l.moveTo(e+i,n),l.lineTo(e+s-i,n),(0!==i||0!==r)&&l.bezierCurveTo(e+s-i*a,n,e+s,n+r*a,e+s,n+r);l.lineTo(e+s,n+o-r),(0!==i||0!==r)&&l.bezierCurveTo(e+s,n+o-r*a,e+s-i*a,n+o,e+s-i,n+o);l.lineTo(e+i,n+o),(0!==i||0!==r)&&l.bezierCurveTo(e+i*a,n+o,e,n+o-r*a,e,n+o-r);l.lineTo(e,n+r),(0!==i||0!==r)&&l.bezierCurveTo(e,n+r*a,e+i*a,n,e+i,n);return l}(e);break;case\\\\\\\"polygon\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!0,i}(e);break;case\\\\\\\"polyline\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),r=new h.a;r.absarc(e,n,i,0,2*Math.PI);const s=new p.a;return s.subPaths.push(r),s}(e);break;case\\\\\\\"ellipse\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),s=new h.a;s.absellipse(e,n,i,r,0,2*Math.PI);const o=new p.a;return o.subPaths.push(s),o}(e);break;case\\\\\\\"line\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),s=new p.a;return s.moveTo(e,n),s.lineTo(i,r),s.currentPath.autoClose=!1,s}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":r=s(e,r);const l=e.href.baseVal.substring(1),u=e.viewportElement.getElementById(l);u?t(u,r):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==r.fill&&\\\\\\\"none\\\\\\\"!==r.fill&&m.color.setStyle(r.fill),function(t,e){function n(t){E.set(t.x,t.y,1).applyMatrix3(e),t.set(E.x,E.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),r=t.subPaths;for(let t=0,s=r.length;t0?M.copy(y[y.length-1]):M.identity())}(S.documentElement,{fill:\\\\\\\"#000\\\\\\\",fillOpacity:1,strokeOpacity:1,strokeWidth:1,strokeLineJoin:\\\\\\\"miter\\\\\\\",strokeLineCap:\\\\\\\"butt\\\\\\\",strokeMiterLimit:4});return{paths:g,xml:S.documentElement}}static createShapes(t){const e=999999999,n=0,r=1,o=2,a=3,l=4,c=5,u=6,p={loc:n,t:0};function m(t,e,i,r){const s=t.x,a=e.x,l=i.x,c=r.x,u=t.y,h=e.y,d=i.y,_=r.y,m=(c-l)*(u-d)-(_-d)*(s-l),g=(_-d)*(a-s)-(c-l)*(h-u),v=m/g,y=((a-s)*(u-d)-(h-u)*(s-l))/g;if(0===g&&0!==m||v<=0||v>=1||y<0||y>1)return null;if(0===m&&0===g){for(let l=0;l<2;l++){if(f(0===l?i:r,t,e),p.loc==n){const t=0===l?i:r;return{x:t.x,y:t.y,t:p.t}}if(p.loc==o){return{x:+(s+p.t*(a-s)).toPrecision(10),y:+(u+p.t*(h-u)).toPrecision(10),t:p.t}}}return null}for(let s=0;s<2;s++)if(f(0===s?i:r,t,e),p.loc==n){const t=0===s?i:r;return{x:t.x,y:t.y,t:p.t}}return{x:+(s+v*(a-s)).toPrecision(10),y:+(u+v*(h-u)).toPrecision(10),t:v}}function f(t,e,i){const s=i.x-e.x,h=i.y-e.y,d=t.x-e.x,_=t.y-e.y,m=s*_-d*h;if(t.x===e.x&&t.y===e.y)return p.loc=n,void(p.t=0);if(t.x===i.x&&t.y===i.y)return p.loc=r,void(p.t=1);if(m<-Number.EPSILON)return void(p.loc=a);if(m>Number.EPSILON)return void(p.loc=l);if(s*d<0||h*_<0)return void(p.loc=c);if(Math.sqrt(s*s+h*h){if(e.boundingBox.containsPoint(r)){(function(t,e){const n=[],r=[];for(let s=1;st.t<=s.t+Number.EPSILON&&t.t>=s.t-Number.EPSILON))&&(n.push(s),r.push(new i.a(s.x,s.y)))}}return r})(t,e.points).forEach((t=>{s.push({identifier:e.identifier,isCW:e.isCW,point:t})}))}})),s.sort(((t,e)=>t.point.x-e.point.x)),s}let v=0,y=e,x=-999999999,b=t.subPaths.map((t=>{const n=t.getPoints();let r=-999999999,o=e,a=-999999999,l=e;for(let t=0;tr&&(r=e.y),e.ya&&(a=e.x),e.x=l&&(y=l-1),{points:n,isCW:_.a.isClockWise(n),identifier:v++,boundingBox:new s(new i.a(l,o),new i.a(a,r))}}));b=b.filter((t=>t.points.length>1));const w=b.map((e=>function(t,e,n,r,s){null!=s&&\\\\\\\"\\\\\\\"!==s||(s=\\\\\\\"nonzero\\\\\\\");const o=new i.a;t.boundingBox.getCenter(o);const a=g([new i.a(n,o.y),new i.a(r,o.y)],t.boundingBox,e);a.sort(((t,e)=>t.point.x-e.point.x));const l=[],c=[];a.forEach((e=>{e.identifier===t.identifier?l.push(e):c.push(e)}));const u=l[0].point.x,h=[];let d=0;for(;d0&&h[h.length-1]===c[d].identifier?h.pop():h.push(c[d].identifier),d++;if(h.push(t.identifier),\\\\\\\"evenodd\\\\\\\"===s){const e=h.length%2==0,n=h[h.length-2];return{identifier:t.identifier,isHole:e,for:n}}if(\\\\\\\"nonzero\\\\\\\"===s){let n=!0,i=null,r=null;for(let t=0;t{if(!w[t.identifier].isHole){const e=new d.a(t.points);w.filter((e=>e.isHole&&e.for===t.identifier)).forEach((t=>{const n=b[t.identifier];e.holes.push(new h.a(n.points))})),T.push(e)}})),T}static getStrokeStyle(t,e,n,i,r){return{strokeColor:e=void 0!==e?e:\\\\\\\"#000\\\\\\\",strokeWidth:t=void 0!==t?t:1,strokeLineJoin:n=void 0!==n?n:\\\\\\\"miter\\\\\\\",strokeLineCap:i=void 0!==i?i:\\\\\\\"butt\\\\\\\",strokeMiterLimit:r=void 0!==r?r:4}}static pointsToStroke(t,e,n,i){const r=[],s=[],a=[];if(0===f.pointsToStrokeWithBuffers(t,e,n,i,r,s,a))return null;const c=new o.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new l.c(r,3)),c.setAttribute(\\\\\\\"normal\\\\\\\",new l.c(s,3)),c.setAttribute(\\\\\\\"uv\\\\\\\",new l.c(a,2)),c}static pointsToStrokeWithBuffers(t,e,n,r,s,o,a,l){const c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a,_=new i.a,m=new i.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,E=new i.a;n=void 0!==n?n:12,r=void 0!==r?r:.001,l=void 0!==l?l:0;const M=(t=function(t){let e=!1;for(let n=1,i=t.length-1;n=r&&n.push(t[e]);return n.push(t[t.length-1]),n}(t)).length;if(M<2)return 0;const S=t[0].equals(t[M-1]);let C,N,L=t[0];const O=e.strokeWidth/2,R=1/(M-1);let P,I,F,D,k=0,B=!1,z=0,U=3*l,G=2*l;V(t[0],t[1],c).multiplyScalar(O),f.copy(t[0]).sub(c),g.copy(t[0]).add(c),v.copy(f),y.copy(g);for(let 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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(),r=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,r)),!e&&(this._createConnection(i,r),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${r}`),!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 r=this[e](t.graphNodeId());for(;r.length>0;){const t=[];for(let n of r)for(let i of this[e](n))t.push(i);for(let t of r)n.set(t,!0);for(let e of t)r.push(e);r=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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h.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return h.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{r[s]=t,s++})),m.isString(r[0])?r.sort():r.sort(((t,e)=>t-e));const o=new Array(t.length);s=0;for(let t of r){const e=n.get(t);if(e)for(let t of e)o[s]=t,s++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),r=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),u.push(a/f),u.push(1-s/g),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}}F.prototype.isShaderMaterial=!0;var D=n(6),k=n(14),B=\\\\\\\"\\\\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\\\\\\\";const z={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:B,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:\\\\\\\"\\\\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\\\\\\\",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 U=n(11);const G={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new U.a},uv2Transform:{value:new U.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 U.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 U.a}}},V={basic:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.fog]),vertexShader:z.meshbasic_vert,fragmentShader:z.meshbasic_frag},lambert:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.fog,G.lights,{emissive:{value:new D.a(0)}}]),vertexShader:z.meshlambert_vert,fragmentShader:z.meshlambert_frag},phong:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.fog,G.lights,{emissive:{value:new D.a(0)},specular:{value:new 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wt{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 ot({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:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),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${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#define 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r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Ct(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=St(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<_t.length;t++)_t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(vt),t.scissorTest=!1,Mt(t,0,0,t.width,t.height)}_fromTexture(t){vt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:w.ob,minFilter:w.ob,generateMipmaps:!1,type:w.Zc,format:w.hc,encoding:Tt(t)?t.encoding:w.gc,depthBuffer:!1},n=Et(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Et(e),n}_compileMaterial(t){const e=new k.a(_t[0],t);this._renderer.compile(e,pt)}_sceneToCubeUV(t,e,n,i){const r=new K.a(90,1,e,n),s=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,l=a.autoClear,c=a.outputEncoding,u=a.toneMapping;a.getClearColor(gt),a.toneMapping=w.vb,a.outputEncoding=w.U,a.autoClear=!1;const h=new at.a({name:\\\\\\\"PMREM.Background\\\\\\\",side:w.i,depthWrite:!1,depthTest:!1}),d=new k.a(new N,h);let p=!1;const _=t.background;_?_.isColor&&(h.color.copy(_),t.background=null,p=!0):(h.color.copy(gt),p=!0);for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(o[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,o[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,o[e])),Mt(i,n*lt,e>2?lt:0,lt,lt),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,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=dt[w.U]:t.value=dt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Ct()):null==this._equirectShader&&(this._equirectShader=St());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new k.a(_t[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),Mt(e,0,0,3*lt,2*lt),n.setRenderTarget(e),n.render(r,pt)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eht&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,pt)}}function Tt(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function At(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=ct[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Z(3*lt,3*lt,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function Mt(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function St(){const t=new d.a(1,1);return new ot({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\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 Ct(){return new ot({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),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${Lt()}\\\\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 Nt(){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 Lt(){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 Ot(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===w.D||s===w.E,a=s===w.o||s===w.p;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[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 r=n.morphAttributes;for(const n in r){const i=r[n];for(let n=0,r=i.length;ne.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const p=new Float32Array(u*h*4*i),_=new kt(p,u,h,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const r=e*n;let s=$t[r];if(void 0===s&&(s=new Float32Array(r),$t[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function ee(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function sn(t){return t.replace(rn,on)}function on(t,e){const n=z[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return sn(n)}const an=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,ln=/#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 cn(t){return t.replace(ln,hn).replace(an,un)}function un(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\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[dn(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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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 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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 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y=v+f+o,x=v+g+a,b=Xe(r,r.VERTEX_SHADER,y),T=Xe(r,r.FRAGMENT_SHADER,x);if(r.attachShader(m,b),r.attachShader(m,T),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(b).trim(),n=r.getShaderInfoLog(T).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,r.LINK_STATUS)){i=!1;const e=Je(r,b,\\\\\\\"vertex\\\\\\\"),n=Je(r,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,r.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info 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0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.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:s.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?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||fn),r.length>1&&r.sort(e||gn),s.length>1&&s.sort(e||gn)}}}function yn(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new vn(t),e.set(n,[r])):i>=e.get(n).length?(r=new vn(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function xn(){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 bn=0;function wn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function Tn(t,e){const n=new xn,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}}}(),r={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++)r.probe.push(new p.a);const s=new p.a,o=new A.a,a=new A.a;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(wn);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_FLOAT_1,r.rectAreaLTC2=G.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_HALF_1,r.rectAreaLTC2=G.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=bn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new An(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class Mn 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}}Mn.prototype.isMeshDepthMaterial=!0;class Sn 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}}Sn.prototype.isMeshDistanceMaterial=!0;function Cn(t,e,n){let i=new T.a;const r=new d.a,s=new d.a,o=new _.a,a=new Mn({depthPacking:w.Hb}),l=new Sn,c={},u=n.maxTextureSize,h={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 k.a(f,p),v=this;function y(n,i){const r=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,r,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,r,m,g,null)}function x(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.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 r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,u===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:h[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===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function b(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let 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0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.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,r,s,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 r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):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 zn(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(Pt.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=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\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=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,R=e.width,P=e.height,I=1,F=null,D=null;const k=new _.a(0,0,R,P),B=new _.a(0,0,R,P);let z=!1;const U=[],G=new T.a;let V=!1,X=!1,$=null;const J=new A.a,Q=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,ut,ht,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Et=n;function Mt(t,n){for(let i=0;i0&&function(t,e,n){if(null===$){const t=!0===o&&!0===nt.isWebGL2;$=new(t?Vt:Z)(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($),g.clear();const r=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=r,at.updateMultisampleRenderTarget($),at.updateRenderTargetMipmap($),g.setRenderTarget(i)}(r,e,n),i&&it.viewport(N.copy(i)),r.length>0&&Xt(r,e,n),s.length>0&&Xt(s,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?f[f.length-1]:null,m.pop(),h=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 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Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Et.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Et.RGB&&(o=Et.RGB8),o===Et.RGBA&&(o=Et.RGBA8)),at.setTexture2D(e,0),Et.copyTexImage2D(Et.TEXTURE_2D,n,o,t.x,t.y,r,s,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,n.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?Et.compressedTexSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Et.generateMipmap(Et.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=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 u;if(i.isDataTexture3D)at.setTexture3D(i,0),u=Et.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),u=Et.TEXTURE_2D_ARRAY}Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,i.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,i.unpackAlignment);const h=Et.getParameter(Et.UNPACK_ROW_LENGTH),d=Et.getParameter(Et.UNPACK_IMAGE_HEIGHT),p=Et.getParameter(Et.UNPACK_SKIP_PIXELS),_=Et.getParameter(Et.UNPACK_SKIP_ROWS),m=Et.getParameter(Et.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Et.pixelStorei(Et.UNPACK_ROW_LENGTH,f.width),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,f.height),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,t.min.x),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,t.min.y),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Et.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,f.data)):Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f),Et.pixelStorei(Et.UNPACK_ROW_LENGTH,h),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,d),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,p),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,_),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,m),0===r&&i.generateMipmaps&&Et.generateMipmap(u),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 Un={};var Gn,Vn,Hn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Gn||(Gn={}));class jn{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(Gn.WEBGL2))}createWebGLRenderer(t){const e=new zn(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?Gn.WEBGL2:Gn.WEBGL;let i=t.getContext(n,Un);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Gn.EXPERIMENTAL_WEBGL2:Gn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Un)),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 Vt(t,e,n):new Z(t,e,n)}}class Wn{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 qn{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(),r=t.type().toLowerCase();let s=this._node_register.get(i);s||(s=new Map,this._node_register.set(i,s));if(s.get(r))console.error(`node ${i}/${r} already registered`);else{if(s.set(r,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(r,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(r,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,r;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===(r=this._node_register_options.get(t))||void 0===r||r.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 r=n.type().toLowerCase(),s=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(r);if(s){const n=s.only,i=s.except,r=`${t}/${e}`;return n?n.includes(r):!i||!i.includes(r)}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 Xn{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 Yn 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\\\\\\\"}(Vn||(Vn={}));class $n 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\\\\\\\"}(Hn||(Hn={}));class Jn 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 Zn{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 Qn{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 Kn=n(86);class ti{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,ai.playerMode()),ai.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(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.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(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.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 ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(Kn.a)(t)}}class ei{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],r=this._map[n];return r?i?`${r}?${i}`:r:void 0}}var ni=n(93),ii=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:r,unzippedData:s}=e,o=Object.keys(r);for(let t of o){const e=s[`assets/${r[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:ai.blobs.createBlobUrl(n)};ai.blobs.registerBlobUrl(i)}ai.setPlayerMode(!0),ai.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]=ai;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,s)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[ii.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let r=document.getElementById(t.scriptElementId);const s=[];return s.push(`import {configurePolygonjs, configureScene} from '${i}';`),s.push(`configurePolygonjs(window.${t.Poly});`),s.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),s.push(`delete window.${t.loadSceneArgs};`),r||(r=document.createElement(\\\\\\\"script\\\\\\\"),r.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),r.text=s.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(r)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const r=new n(e),s=await r.scene();i&&i(s);const o=s.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);s.play(),t.scene=s,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ni.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(Kn.a)(i)}}class si{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class oi{constructor(){this.renderersController=new jn,this.nodesRegister=new qn(this),this.operationsRegister=new Xn(this),this.expressionsRegister=new Yn,this.modulesRegister=new $n,this.assemblersRegister=new Jn,this.pluginsRegister=new Zn(this),this.camerasRegister=new Qn(this),this.blobs=new ti,this.assetUrls=new ei,this.selfContainedScenesLoader=new ri,this.performance=new si,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new oi;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new Wn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const ai=oi._instance_();class li{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=ai.performance.performanceManager(),i=n.now();e();const r=n.now()-i;console.log(`${t}: ${r}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=ai.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 r=t[e];n.push(r),null==i[r]&&(i[r]=[]),i[r].push(e)}n.sort(((t,e)=>t-e));const r=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const s=[];for(let t of r){const n=i[t];for(let i of n){const n=e[i],r={duration:t,name:i,count:n,duration_per_iteration:t/n};s.push(r)}}return console.table(s),s}}class ci{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 hi{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 di{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 pi;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(pi||(pi={}));const _i=[pi.LOADED,pi.PLAY,pi.PAUSE,pi.TICK];class mi extends di{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}}class fi{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(pi.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 gi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class vi{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=xi.findNode(t,this._path)}clone(){const t=new vi(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 yi{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=xi.findParam(t,this._path)}clone(){const t=new yi(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 ${this.path()}`)}}class xi{static split_parent_child(t){const e=t.split(xi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(xi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR).filter((t=>t.length>0)),r=i[0];let s=null;if(e[0]!==xi.SEPARATOR){switch(r){case xi.PARENT:null==n||n.add_path_element(r),s=t.parent();break;case xi.CURRENT:null==n||n.add_path_element(r),s=t;break;default:s=t.node(r),s&&(null==n||n.add_node(r,s))}if(null!=s&&i.length>1){const t=i.slice(1).join(xi.SEPARATOR);s=this.findNode(s,t,n)}return s}{const i=e.substr(1);s=this.findNode(t.root(),i,n)}return s}static findParam(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(xi.SEPARATOR),r=this.findNode(t,e,n);if(null!=r){const t=i[i.length-1],e=r.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let r=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(xi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)s[l]||a.push(t),l++;return`${r}${a.join(xi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),r=Math.min(n.length,i.length);let s=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=xi.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 r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}}class Ti{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=ai.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 Ai{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ti(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 Mi{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 Si{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 Ci extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Ni extends Si{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Li extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Oi{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 Ni(this.node)}hasDisplay(){return!0}}}function Pi(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ci(this.node)}hasBypass(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.optimize=new Li(this.node)}hasOptimize(){return!0}}}class Fi extends(Ri(Oi)){}class Di extends(Pi(Oi)){}class ki extends(Pi(Ri(Oi))){}class Bi extends(Ii(Pi(Oi))){}class zi extends(Ii(Pi(Ri(Oi)))){}class Ui{constructor(t){this.node=t}}class Gi extends Ui{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 Vi extends Ui{set(t){this._message!=t&&(t&&ai.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 Hi{constructor(t){this.node=t,this.timeDependent=new Gi(this.node),this.error=new Vi(this.node)}}class ji{constructor(t){this.node=t,this._graph_node=new Ai(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 Wi{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(ji.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=xi.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==xi.CURRENT||t==xi.CURRENT_WITH_SLASH)return this.node;if(t==xi.PARENT||t==xi.PARENT_WITH_SLASH)return this.node.parent();const e=xi.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 xi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const qi=/[, ]/,Xi=/\\\\d+$/,Yi=/^0+/,$i=/,| /,Ji=/^-?\\\\d+\\\\.?\\\\d*$/;var Zi,Qi,Ki,tr,er,nr,ir,rr;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Zi||(Zi={}));class sr{static isBoolean(t){return t==Zi.TRUE||t==Zi.FALSE}static toBoolean(t){return t==Zi.TRUE}static isNumber(t){return Ji.test(t)}static tailDigits(t){const e=t.match(Xi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Xi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const r=n.match(Yi);r&&(i=r[0]);const s=parseInt(n);0==s&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${s+1}`}return`${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)sr.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(qi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let r=0;return n.forEach((t=>{i[r]=t,r++})),i}static indices(t){const e=t.split($i);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 or{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\\\\\\\"}(Qi||(Qi={}));class ar{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class lr{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==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.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 Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.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 vi)return n.set_path(e);if(n instanceof yi)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 cr(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 r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.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 r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.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 dr{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\\\\\\\"}(Ki||(Ki={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(tr||(tr={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(er||(er={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(nr||(nr={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ir||(ir={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rr||(rr={}));const pr=[rr.DEVICE_ORIENTATION,rr.MAP,rr.ORBIT,rr.FIRST_PERSON,rr.MOBILE_JOYSTICK];class _r{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 mr=n(69);class fr extends Q.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}}fr.prototype.isScene=!0;var gr=n(49),vr=n(52),yr=n(42),xr=n(55),br=n(61),wr=n(24),Tr=n(35);const Ar=new p.a,Er=new p.a;class Mr extends Q.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){Ar.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ar);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ar.setFromMatrixPosition(t.matrixWorld),Er.setFromMatrixPosition(this.matrixWorld);const n=Ar.distanceTo(Er)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=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,r){return(t-e)/(n-e)*(r-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*is}static radians_to_degrees(t){return t/is}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),r=this.deg2rad(e.lat-t.lat),s=this.deg2rad(e.lng-t.lng),o=Math.sin(r/2)*Math.sin(r/2)+Math.cos(n)*Math.cos(i)*Math.sin(s/2)*Math.sin(s/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)))}}rs.Easing=ns,rs.fract=t=>t-Math.floor(t),rs._vec={x:0,y:136574},rs._triangle_mid=new p.a,rs._triangle_mid_to_corner=new p.a;class ss{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 Zr(this._core_geometry,e[n+0]),new Zr(this._core_geometry,e[n+1]),new Zr(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 Qr.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=[rs.randFloat(t),rs.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(),r=this._geometry.attributes[t].itemSize,s=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(r){case 1:a=0;for(let t of s)a+=t*i[l],l++;o=a;break;default:for(let t of s){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 r=[e.a,e.b,e.c],s=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=r.map((t=>new p.a(s[3*t+0],s[3*t+1],s[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=r.map((t=>l[t]));break;case 2:c=r.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=r.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const u=r.map(((t,e)=>n.distanceTo(o[e]))),h=f.sum([u[0]*u[1],u[0]*u[2],u[1]*u[2]]),_=[u[1]*u[2]/h,u[0]*u[2]/h,u[0]*u[1]/h];let m;switch(a){case 1:m=f.sum(r.map(((t,e)=>_[e]*c[e])));break;default:var g=r.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class os{from_points(t){t=this._filter_points(t);const e=new S.a,n=new ps(e),i=t[0];if(null!=i){const r=i.geometry(),s=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),s={};r.forEach(((t,e)=>s[t]=e)),n.userDataAttribs()[i]=r;const o=[];for(let e of t){const t=s[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,r=new Array(t.length*n);switch(n){case 1:for(let e=0;enew ps(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},r=[];for(let i of e){const e=i.points();for(let i of e){r.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const s=Object.keys(n);for(let e of r){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=s}const r=cs(t),s=new ps(r);return Object.keys(i).forEach((t=>{const e=i[t];s.setIndexedAttributeValues(t,e)})),r&&delete r.userData.mergedUserData,r}}class ps{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 ps.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=Wr.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=Wr.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=Wr.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Dr.STRING:Dr.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return ps.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=sr.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)sr.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=Wr.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 r=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew ss(this,t)))}}var _s;ps._mesh_builder=new class extends os{_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=[],r=i.length;let s,o,a;for(let t=0;t{};class fs{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,r,s,o,a)=>{n(e,i,r,s,o,a,t)})}t.onBeforeRender=ms}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,r=n.customMaterials[i];r&&(t[i]=r,r.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.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 r=t,s=i.customMaterials[r];s&&(s.uniforms[e]=s.uniforms[e]||n)}}}const gs=\\\\\\\"name\\\\\\\";class vs extends qr{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 ps(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=sr.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(\\\\\\\"=\\\\\\\"),r=i[0];if(\\\\\\\"@\\\\\\\"==r[0]){const t=r.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,r=t.userData;r.attributes=r.attributes||{},r.attributes[e]=i}addAttribute(t,e){vs.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=Wr.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 ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==gs)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==gs?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return vs.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return vs.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(gs)}humanType(){return Vr.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)?Dr.STRING:Dr.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:Wr.attribSizeFromValue(e)}clone(){return vs.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return vs.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const r=n.get(e),s=e;if(s.geometry){const t=r.geometry;s.geometry=ps.clone(t);const e=s.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(s.material){s.material=r.material,fs.applyCustomMaterials(e,s.material);const t=s.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(r.userData));const o=r;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=r,u=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=u.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 ps(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 xs{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=ys[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 ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const bs=ai.performance.performanceManager();class ws{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=bs.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=bs.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=bs.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=bs.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=bs.now())}recordCookEnd(){this.active()&&(this._data.cookTime=bs.now()-this._cook_start,this._cooksCount+=1)}}class Ts{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new ws(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),ai.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()?ai.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()):(ai.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 r;for(let s=0;st.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 Es,Ms;!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\\\\\\\"}(Es||(Es={})),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\\\\\\\"}(Ms||(Ms={}));const Ss=\\\\\\\"dependentOnFoundNode\\\\\\\",Cs=\\\\\\\"visibleIf\\\\\\\";var Ns,Ls;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Ns||(Ns={})),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\\\\\\\"}(Ls||(Ls={}));class Os{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!(Ss 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 Ai(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(Ms.VISIBLE_UPDATED)}}}class Rs{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 Ps{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 Is{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Fs{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&ai.warn(this.param.path(),this._message),this.param.emitController.emit(Ms.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Ds{constructor(t){this.param=t,this.timeDependent=new Is(this.param),this.error=new Fs(this.param)}}class ks extends Ai{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 r=m.isNumber(e),s=new ks(t,e,i);if(n)s.set_resolved_graph_node(n);else if(!r){const t=e;s.set_unresolved_path(t)}return s}}const Bs=[];class zs extends Ai{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Os(this),this._emit_controller=new Rs(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Os(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rs(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ps(this)}get states(){return this._states=this._states||new Ds(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof ks&&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 Es.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(Ms.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 ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=xi.relativePath(t,this.node);return e.length>0?`${e}${xi.SEPARATOR}${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 Bs}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Us=n(96),Gs=n.n(Us);Gs.a.addUnaryOp(\\\\\\\"@\\\\\\\");Gs.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Vs{constructor(){}parse_expression(t){try{this.reset(),this.node=Gs()(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=Vs.string_value_elements(t),n=[];for(let t=0;t{e.push(js.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(js.assign_item_size_line(t)),e.push(js.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=js.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 js.var_attribute_size(t)}var_array(t){return js.var_array(t)}}const Ws={math_random:\\\\\\\"random\\\\\\\"},qs=Object.keys(ns),Xs={};[\\\\\\\"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=>{Xs[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),Object.keys(Ws).forEach((t=>{const e=Ws[t];Xs[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Xs[t]=`Core.Math.${t}`})),qs.forEach((t=>{Xs[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Xs[t]=`Core.String.${t}`}));const Ys={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},$s={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{$s[t]=`Math.${t}`}));const Js={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Zs extends Hs{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new js,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 = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rs,String:sr};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=Ys[n];if(i)return i(e);const r=`${e.join(\\\\\\\", \\\\\\\")}`,s=Xs[n];if(s)return`${s}(${r})`;const o=ai.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],s=`return ${e[0]}`;let o,a=[];try{o=new Function(s),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${r}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;ai.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)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,r=t.object,s=t.property;e=r.name,n=s.name;break}}if(e){if(e=Wr.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Js[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=$s[e];if(n)return n;const i=`traverse_Identifier_${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=ai.expressionsRegister,r=i.getMethod(t);if(!r){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void ai.warn(e)}const s=new r(this.param);if(this.method_index+=1,this.methods[this.method_index]=s,s.require_dependency()){const t=s.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Qs extends Hs{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${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 Ks{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 to{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Vs,this.function_generator=new Zs(this.param),this.dependencies_controller=new Ks(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 Vs,this.reset(),this.param.type()==Es.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 Qs(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 eo{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 to(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: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class no extends zs{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${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(Ms.VALUE_UPDATED),this.removeDirtyState()}}class io extends no{static type(){return Es.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)&&sr.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(sr.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=io.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends zs{constructor(){super(...arguments),this._components_contructor=io}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\\\\\\\"}(so||(so={}));so.NONE,so.GAMMA_TO_LINEAR,so.LINEAR_TO_GAMMA,so.SRGB_TO_LINEAR,so.LINEAR_TO_SRGB;class oo{static set_hsv(t,e,n,i){t=Object(Ln.f)(t,1),e=Object(Ln.d)(e,0,1),n=Object(Ln.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const ao=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class lo extends no{static type(){return Es.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)&&sr.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(sr.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=lo.convert(t);return e?this.options.ensureInRange(e):e}}class co{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(xi.SEPARATOR);const n=e[0];return n&&(xi.NON_LETTER_PREFIXES.includes(n)||(e=`${xi.SEPARATOR}${e}`)),e}}class uo extends zs{constructor(){super(...arguments),this.decomposed_path=new co}}var ho;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(ho||(ho={}));class po 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 Es.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(Ms.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,r=this.options.paramSelectionOptions()?ho.PARAM:ho.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(r==ho.PARAM?n=xi.findParam(this.node,t,this.decomposed_path):e=xi.findNode(this.node,t,this.decomposed_path));const s=r==ho.PARAM?this._found_param:this._found_node,o=r==ho.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==s?void 0:s.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),r==ho.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 _o,mo=n(33),fo=n(70);class go{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 go;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 go(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(_o||(_o={}));class vo{constructor(t=_o.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(Ln.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(go.from_json(n));return new vo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new vo;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(!go.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(go.from_json(n)),e+=1}}}const yo=1024;class xo extends zs{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new mo.a(this._texture_data,yo,1,w.ic)}static type(){return Es.RAMP}defaultValueSerialized(){return this._default_value instanceof vo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof vo?t.clone():vo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof vo?this._raw_input.toJSON():vo.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 vo?e instanceof vo?t.is_equal(e):t.is_equal_json(e):e instanceof vo?e.is_equal_json(t):vo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof vo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof vo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=vo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Ms.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/yo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new fo.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 r=0;for(let t of e)n[r]=t.position,i[r]=t.value,r++;return xo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}xo.DEFAULT_VALUE=new vo(_o.LINEAR,[new go(0,0),new go(1,1)]),xo.DEFAULT_VALUE_JSON=xo.DEFAULT_VALUE.toJSON();class bo extends zs{static type(){return Es.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 eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.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: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Vs.string_value_elements(t)}}const wo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Es.BOOLEAN]:class extends no{static type(){return Es.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(sr.isBoolean(t))return sr.toBoolean(t);if(sr.isNumber(t)){return parseFloat(t)>=1}}return null}},[Es.BUTTON]:class extends zs{static type(){return Es.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()}},[Es.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 Es.COLOR}componentNames(){return ao}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!=so.NONE){switch(t){case so.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case so.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case so.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case so.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ar.unreachable(t)}this._value_serialized_dirty=!0}},[Es.FLOAT]:io,[Es.FOLDER]:class extends zs{static type(){return Es.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}},[Es.INTEGER]:lo,[Es.OPERATOR_PATH]:po,[Es.PARAM_PATH]:class extends uo{static type(){return Es.PARAM_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}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(Ms.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=xi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),r=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==r?void 0:r.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()}},[Es.NODE_PATH]:class extends uo{static type(){return Es.NODE_PATH}initialize_param(){this._value=new vi}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(Ms.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=xi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),r=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==r?void 0:r.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()}},[Es.RAMP]:xo,[Es.STRING]:bo,[Es.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Es.VECTOR2}componentNames(){return wo}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}},[Es.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Es.VECTOR3}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 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}},[Es.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Es.VECTOR4}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 _.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 Mo{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 Es.STRING:return this._handle_string_param(t);case Es.OPERATOR_PATH:return this._handle_operator_path_param(t);case Es.NODE_PATH:return this._handle_node_path_param(t);case Es.PARAM_PATH:return this._handle_param_path_param(t);case Es.FLOAT:case Es.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 So{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 Mo}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 Ai(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 r;this.node.params_init_value_overrides&&(r=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,r)}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):ai.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Es.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Es.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Es.FLOAT)}get_operator_path(t){return this.param_with_type(t,Es.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Es.BOOLEAN)}float(t){return this.value_with_type(t,Es.FLOAT)}integer(t){return this.value_with_type(t,Es.INTEGER)}string(t){return this.value_with_type(t,Es.STRING)}vector2(t){return this.value_with_type(t,Es.VECTOR2)}vector3(t){return this.value_with_type(t,Es.VECTOR3)}color(t){return this.value_with_type(t,Es.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(ai.warn(`tried to access param '${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={},r){const s=i.spare||!1;!1!==this._param_create_mode||s||ai.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&ai.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&&(s?a.type()!=t&&this._deleteParam(a.name()):ai.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==r)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=r.raw_input)l.set(r.raw_input);else if(null!=r.simple_data)l.set(r.simple_data);else if(null!=r.complex_data){const t=r.complex_data.raw_input;t?l.set(t):l.set(n);const e=r.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${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 ai.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)?ai.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 Co{constructor(){}}class No{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=No._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)}}No._next_id=0;class Lo{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==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.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 Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.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.set_min_inputs_count(0),this.set_max_inputs_count(t.length),this.init_graph_node_inputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}init_graph_node_inputs(){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 s=this._graph_node_inputs[i];if(null==s){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&&r==l||(null!=o&&this._depends_on_inputs&&s.removeGraphInput(o),null!=e?s.addGraphInput(e)?(this._depends_on_inputs||s.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new No(e,this.node,r,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(),s.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 Lo(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 r=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&r.push(n)}return r}return[]}}class Po{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 Io{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 Fo{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 Do;!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\\\\\\\"}(Do||(Do={}));const ko=[Do.BOOL,Do.INT,Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4],Bo={[Do.BOOL]:Es.BOOLEAN,[Do.INT]:Es.INTEGER,[Do.FLOAT]:Es.FLOAT,[Do.VEC2]:Es.VECTOR2,[Do.VEC3]:Es.VECTOR3,[Do.VEC4]:Es.VECTOR4,[Do.SAMPLER_2D]:Es.RAMP,[Do.SSS_MODEL]:Es.STRING},zo={[Es.BOOLEAN]:Do.BOOL,[Es.COLOR]:Do.VEC3,[Es.INTEGER]:Do.INT,[Es.FLOAT]:Do.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Do.VEC2,[Es.VECTOR3]:Do.VEC3,[Es.VECTOR4]:Do.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Uo={[Do.BOOL]:!1,[Do.INT]:0,[Do.FLOAT]:0,[Do.VEC2]:[0,0],[Do.VEC3]:[0,0,0],[Do.VEC4]:[0,0,0,0],[Do.SAMPLER_2D]:xo.DEFAULT_VALUE_JSON,[Do.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Go={[Do.BOOL]:1,[Do.INT]:1,[Do.FLOAT]:1,[Do.VEC2]:2,[Do.VEC3]:3,[Do.VEC4]:4,[Do.SAMPLER_2D]:1,[Do.SSS_MODEL]:1};class Vo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Uo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Bo[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 Ho;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(Ho||(Ho={}));const jo=[Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4],Wo={[Ho.BOOL]:Es.BOOLEAN,[Ho.INT]:Es.INTEGER,[Ho.FLOAT]:Es.FLOAT,[Ho.VEC2]:Es.VECTOR2,[Ho.VEC3]:Es.VECTOR3,[Ho.VEC4]:Es.VECTOR4},qo={[Es.BOOLEAN]:Ho.BOOL,[Es.COLOR]:Ho.VEC3,[Es.INTEGER]:Ho.INT,[Es.FLOAT]:Ho.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Ho.VEC2,[Es.VECTOR3]:Ho.VEC3,[Es.VECTOR4]:Ho.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Xo={[Ho.BOOL]:!1,[Ho.INT]:0,[Ho.FLOAT]:0,[Ho.VEC2]:[0,0],[Ho.VEC3]:[0,0,0],[Ho.VEC4]:[0,0,0,0]};Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4;class Yo extends Fo{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Xo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Wo[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 $o;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}($o||($o={}));class Jo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Es.FLOAT}are_types_matched(t,e){return e==$o.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 Zo={[Ki.ANIM]:void 0,[Ki.COP]:void 0,[Ki.EVENT]:$o.BASE,[Ki.GL]:Do.FLOAT,[Ki.JS]:Ho.FLOAT,[Ki.MANAGER]:void 0,[Ki.MAT]:void 0,[Ki.OBJ]:void 0,[Ki.POST]:void 0,[Ki.ROP]:void 0,[Ki.SOP]:void 0};function Qo(t,e,n){switch(t){case Ki.EVENT:return new Jo(e,n);case Ki.GL:return new Vo(e,n);case Ki.JS:return new Yo(e,n);default:return}}class Ko{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 Ki.EVENT:return;case Ki.GL:return zo;case Ki.JS:return qo;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=Qo(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 r=t.init_value;const s=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(r=null!=o?o:null!=s?s:t.init_value,m.isArray(t.init_value))if(m.isNumber(r)){const e=new Array(t.init_value.length);e.fill(r),r=e}else m.isArray(r)&&r.length==t.init_value.length&&null!=s&&(r=t.init_value);null!=r&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(r),raw_input:b.clone(r),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 ta{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 Ko(this.node,this._context)}default_connection_type(){return Zo[this._context]}create_connection_point(t,e){return Qo(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(er.INPUT),n=this.node.nodesByType(er.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 ea{constructor(t){this.node=t,this._connections=new Po(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Oo(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 ta(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 Io(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 na{constructor(){}}class ia extends Ai{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new xs(this),this.pv=new Co,this.p=new na,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 Wi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new hr(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 Mi(this)}get states(){return this._states=this._states||new Hi(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new dr(this)}get serializer(){return this._serializer=this._serializer||new As(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ts(this)}get io(){return this._io=this._io||new ea(this)}get nameController(){return this._name_controller=this._name_controller||new ji(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new So(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 r;return null===(r=this._params_controller)||void 0===r?void 0:r.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 ia{static context(){return Ki.MANAGER}}class sa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class oa{static BUTTON(t,e){return new sa(Es.BUTTON,t,e)}static BOOLEAN(t,e){return new sa(Es.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new sa(Es.COLOR,t,e)}static FLOAT(t,e){return new sa(Es.FLOAT,t,e)}static FOLDER(t=null,e){return new sa(Es.FOLDER,t,e)}static INTEGER(t,e){return new sa(Es.INTEGER,t,e)}static RAMP(t=xo.DEFAULT_VALUE,e){return new sa(Es.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new sa(Es.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new sa(Es.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new sa(Es.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new sa(Es.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new sa(Es.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new sa(Es.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new sa(Es.PARAM_PATH,t,e)}}class aa{}class la{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),r=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(sr.matchMask(r,t))return i;const s=this.findObjectByMaskInObject(t,i,r);if(s)return s}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let r of e.children){const e=this._removeTrailingOrHeadingSlash(r.name),s=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;sr.matchMask(s,t)&&n.push(r),this.objectsByMaskInObject(t,r,n,s)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ca={computeOnDirty:!1,callback:t=>{ha.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=oa.BOOLEAN(1,ca)}}}ua(aa);class ha{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 da;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(da||(da={}));const pa=[da.NONE,da.COLOR,da.TEXTURE],_a={computeOnDirty:!1,callback:t=>{fa.update(t)}};function ma(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=oa.INTEGER(pa.indexOf(da.NONE),{menu:{entries:pa.map(((t,e)=>({name:t,value:e})))},..._a}),this.bgColor=oa.COLOR([0,0,0],{visibleIf:{backgroundMode:pa.indexOf(da.COLOR)},..._a}),this.bgTexture=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:pa.indexOf(da.TEXTURE)},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,..._a})}}}ma(aa);class fa{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==pa.indexOf(da.NONE))t.background=null;else if(e.backgroundMode==pa.indexOf(da.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(Ki.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 ga={computeOnDirty:!1,callback:t=>{ya.update(t)}};function va(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=oa.BOOLEAN(0,ga),this.environment=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,...ga})}}}va(aa);class ya{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(Ki.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 xa{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new xa(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}xa.prototype.isFog=!0;class ba{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new ba(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}ba.prototype.isFogExp2=!0;const wa={computeOnDirty:!1,callback:t=>{Ma.update(t)}};var Ta;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Ta||(Ta={}));const Aa=[Ta.LINEAR,Ta.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0,wa),this.fogType=oa.INTEGER(Aa.indexOf(Ta.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Aa.map(((t,e)=>({name:t,value:e})))},...wa}),this.fogColor=oa.COLOR([1,1,1],{visibleIf:{useFog:1},...wa}),this.fogNear=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogFar=oa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogDensity=oa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.EXPONENTIAL)},...wa})}}}Ea(aa);class Ma{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Aa.indexOf(Ta.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 xa(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new ba(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Sa={computeOnDirty:!1,callback:t=>{Na.update(t)}};function Ca(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=oa.BOOLEAN(0,Sa),this.overrideMaterial=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,...Sa})}}}Ca(aa);class Na{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(Ki.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 La extends(Ca(va(Ea(ma(ua(aa)))))){}const Oa=new La;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Oa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.sceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this),this._children_controller_context=Ki.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 fr;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 r=`_____${n.renderOrder}__${n.path()}`;e.push(r),t.set(r,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 Pa{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();sr.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 Ia{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const r=new As(i);e[i.graphNodeId()]=r.toJSON(t);const s=i.params.all;for(let t of s)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Fa;!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\\\\\\\"}(Fa||(Fa={}));const Da=[Fa.auxclick,Fa.click,Fa.contextmenu,Fa.dblclick,Fa.mousedown,Fa.mouseenter,Fa.mouseleave,Fa.mousemove,Fa.mouseover,Fa.mouseout,Fa.mouseup,Fa.pointerlockchange,Fa.pointerlockerror,Fa.select,Fa.wheel];class ka extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}}class Ba extends ia{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return Ki.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,u.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 za;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(za||(za={}));const Ua=[za.CANVAS,za.DOCUMENT];class Ga{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 r=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,r),i.set(n.type,{listener:r,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==za.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 Va={visibleIf:{active:1},callback:t=>{ja.PARAM_CALLBACK_updateRegister(t)}};class Ha extends Ba{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:Ua[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class ja extends Ha{acceptedEventTypes(){return[]}}const Wa=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=oa.BOOLEAN(0,Va),this.click=oa.BOOLEAN(0,Va),this.contextmenu=oa.BOOLEAN(0,Va),this.dblclick=oa.BOOLEAN(0,Va),this.mousedown=oa.BOOLEAN(1,Va),this.mouseenter=oa.BOOLEAN(0,Va),this.mouseleave=oa.BOOLEAN(0,Va),this.mousemove=oa.BOOLEAN(1,Va),this.mouseover=oa.BOOLEAN(0,Va),this.mouseout=oa.BOOLEAN(0,Va),this.mouseup=oa.BOOLEAN(1,Va),this.pointerlockchange=oa.BOOLEAN(0,Va),this.pointerlockerror=oa.BOOLEAN(0,Va),this.select=oa.BOOLEAN(0,Va),this.wheel=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class qa extends Ha{constructor(){super(...arguments),this.paramsConfig=Wa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Da.map((t=>new Jo(t,$o.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 Xa;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Xa||(Xa={}));const Ya=[Xa.pointerdown,Xa.pointermove,Xa.pointerup];class $a extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}}const Ja=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{Za.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=oa.BOOLEAN(1,Va),this.pointermove=oa.BOOLEAN(0,Va),this.pointerup=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class Za extends Ha{constructor(){super(...arguments),this.paramsConfig=Ja}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ya.map((t=>new Jo(t,$o.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 Qa,Ka;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Qa||(Qa={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(Ka||(Ka={}));const tl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{el.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.sceneLoaded=oa.BOOLEAN(1,Va),this.play=oa.BOOLEAN(1,Va),this.pause=oa.BOOLEAN(1,Va),this.tick=oa.BOOLEAN(1,{separatorAfter:!0,...Va}),this.treachedTime=oa.BOOLEAN(0,{callback:t=>{el.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=oa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=oa.INTEGER(1,{range:[0,100]}),this.setFrame=oa.BUTTON(null,{callback:t=>{el.PARAM_CALLBACK_setFrame(t)}})}};class el extends Ha{constructor(){super(...arguments),this.paramsConfig=tl}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Qa.SET_FRAME,$o.BASE,this.onSetFrame.bind(this))]);const t=_i.map((t=>new Jo(t,$o.BASE)));t.push(new Jo(Ka.TIME_REACHED,$o.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}on_frame_update(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(Ka.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Ai(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this.on_frame_update.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 nl;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(nl||(nl={}));const il=[nl.keydown,nl.keypress,nl.keyup];class rl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}}const sl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{ol.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=oa.BOOLEAN(1,Va),this.keypress=oa.BOOLEAN(0,Va),this.keyup=oa.BOOLEAN(0,Va),this.keyCodes=oa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Va),this.ctrlKey=oa.BOOLEAN(0,Va),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class ol extends Ha{constructor(){super(...arguments),this.paramsConfig=sl}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(il.map((t=>new Jo(t,$o.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var al;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(al||(al={}));const ll=[al.resize];class cl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}}const ul=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{hl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.resize=oa.BOOLEAN(1,Va)}};class hl extends Ha{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(ll.map((t=>new Jo(t,$o.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 dl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(dl||(dl={}));const pl=[dl.dragover];class _l extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}}var ml;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(ml||(ml={}));const fl=[ml.touchstart,ml.touchmove,ml.touchend];class gl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}}const vl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{yl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=oa.BOOLEAN(1,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class yl extends Ha{constructor(){super(...arguments),this.paramsConfig=vl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(pl.map((t=>new Jo(t,$o.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 xl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{bl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=oa.BOOLEAN(1,Va),this.touchmove=oa.BOOLEAN(0,Va),this.touchend=oa.BOOLEAN(0,Va)}};class bl extends Ha{constructor(){super(...arguments),this.paramsConfig=xl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(fl.map((t=>new Jo(t,$o.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 wl{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 ol.type():return this.keyboardEventsController;case qa.type():return this.mouseEventsController;case yl.type():return this.dragEventsController;case Za.type():return this.pointerEventsController;case el.type():return this.sceneEventsController;case bl.type():return this.touchEventsController;case hl.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(ka)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(_l)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller($a)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(mi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(cl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(gl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Tl{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),u.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)u.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){u.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)u.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([Es.OPERATOR_PATH,Es.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,r=[];for(let t of n)this._check_param(t,i,r);for(let t of r)i.set(t.node.graphNodeId(),t.node);const s=[];return i.forEach((t=>{s.push(t)})),s}_check_param(t,e,n){if(t instanceof po){const i=t.found_node(),r=t.found_param();return i&&e.set(i.graphNodeId(),i),void(r&&n.push(r))}}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 Al;!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\\\\\\\"}(Al||(Al={}));const El=ai.performance.performanceManager();class Ml{constructor(t){this.scene=t,this._frame=0,this._time=0,this._prev_performance_now=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._graph_node=new Ai(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(pi.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(pi.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(pi.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,Al.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Al.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();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,Al.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()}}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(Ml.START_FRAME,!0)}incrementTimeIfPlaying(){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime())}incrementTime(){if(this._realtimeState){const t=El.now(),e=(t-this._prev_performance_now)/1e3,n=this._time+e;this._prev_performance_now=t,this.setTime(n)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return Ml.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,ai.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 Ai(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 Rl{constructor(){this._cameras_controller=new s(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new wi(this),this._expressions_controller=new ui,this._nodes_controller=new Pa(this),this._objects_controller=new la(this),this._references_controller=new Tl(this),this._time_controller=new Ml(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=sr.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 ci(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new wl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new hi(this)}get loadingController(){return this._loading_controller=this._loading_controller||new fi(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 li}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 Ia(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 Pl{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 r of t)i=e[r],i&&(n[r]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let r;for(let s of i)if(r=e[s],null!=r){const e=r.options,i=r.overriden_options;if(e||i){const o=r;e&&e.spare==t?null!=o.raw_input&&(n[s]={complex_data:o}):i&&(n[s]={complex_data:o})}else{const t=r;(i||null!=t)&&(n[s]={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,r;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:s}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of s)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let s of this._optimized_root_node_names){const o=e[s],a=this._node.createNode(Il);if(a){a.setName(s),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(r=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===r||r.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,r;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const s=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of s)(null===(r=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===r?void 0:r.active())||o++;return this.is_optimized_root_node_generic({outputs_count:s.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let r of n)if(r!=e){const n=t[r].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(r)}}return i}_create_operation_container(t,e,n,i){const r=Pl.non_spare_params_data_value(n.params),s=Dl.operation_type(n),o=this._node.create_operation_container(s,i,r);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 kl{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:r}=Dl.child_names_by_optimized_state(e),s=[];for(let n of r){const i=e[n],r=i.type.toLowerCase(),o=Pl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(r,o);t&&(t.setName(n),s.push(t))}catch(e){console.error(`error importing node: cannot create with type ${r}`,e);const i=sr.camelCase(r);try{const t=this._node.createNode(i,o);t&&(t.setName(n),s.push(t))}catch(e){const a=`${r}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),s.push(t))}catch(e){const n=`failed to create node with type '${r}', '${i}' or '${a}'`;t.report.addWarning(n),ai.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==Ki.SOP){const t=Object.keys(e);let r;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(r=i)}if(r){const t=s.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(r)){const t=`node '${`${this._node.path()}/${r}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of s){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else ai.warn(`possible import error for node ${n.name()}`)}for(let t of s){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const Bl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class zl{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;null!=e&&this._node.io.inputs.setCount(1,e),this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(ai.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const r=e.comment;r&&n.setComment(r),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new kl(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,r,s,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===(r=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(l));const c=a.optimize;null!=c&&(null===(o=null===(s=this._node.flags)||void 0===s?void 0:s.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,r=n.toAdd&&n.toAdd.length>0;if(i||r){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 Bl)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 Pl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Pl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Pl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends zl{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 zl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof bo?new Vl(t):t instanceof xo?new Hl(t):new Pl(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 Rl;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 r=e.realtimeState;null!=r&&t.timeController.setRealtimeState(r),t.setFrame(e.frame||Ml.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),r=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;r.push(i)}const s=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();s.push(`${n}/ui.json?t=${t}`)}for(let t of r)s.push(t);let o=0;const a=s.length,l=s.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),u=[];for(let t of c)u.push(await t.json());const h={root:u[0],properties:u[1]};let d=2;t.editorMode&&(h.ui=u[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=u[t+d];p[e]=n}return this.assemble(h,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{s.matrixAutoUpdate=!0},c.onComplete=()=>{s.matrixAutoUpdate=t,s.matrixAutoUpdate||s.updateMatrix()}}if(o instanceof au.a&&this._target_value instanceof au.a){const t={value:0},e=o,n=(new au.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 r=[];return m.isNumber(n)?(i=t,r.push(e)):(i=n,this._target_value instanceof d.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,au.a),{target_property:n,to_target:i,property_names:r}}{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):ai.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Es.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Es.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Es.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Es.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Es.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated 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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,r)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};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,r)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};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,r)}withOp(t,e,n){switch(n){case N_.SET:return 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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 aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0,{range:[-1,1]}),this.overridePositions=oa.BOOLEAN(0),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends iu{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new R_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ar.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends 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type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new R_,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 aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"position\\\\\\\")}};class im extends iu{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 R_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var rm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(rm||(rm={}));const sm=[rm.CUSTOM,rm.FROM_SCENE_GRAPH,rm.FROM_NODE],om=sm.indexOf(rm.CUSTOM),am=sm.indexOf(rm.FROM_SCENE_GRAPH),lm=sm.indexOf(rm.FROM_NODE);const cm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(om,{menu:{entries:sm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=oa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=oa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{um.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=oa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=oa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=oa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=oa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=oa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=oa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class um extends iu{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 R_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(sm.indexOf(t))}async _prepare_timeline_builder(t){const e=sm[this.pv.mode];switch(e){case rm.CUSTOM:return this._prepare_timebuilder_custom(t);case rm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case rm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ar.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 au.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 r=i.value;r&&(m.isNumber(r)||m.isVector(r))&&t.setPropertyValue(r)}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 hm=new class extends aa{constructor(){super(...arguments),this.unlimited=oa.BOOLEAN(0),this.count=oa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=oa.FLOAT(0),this.yoyo=oa.BOOLEAN(0)}};class dm extends iu{constructor(){super(...arguments),this.paramsConfig=hm}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 R_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends iu{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 aa{constructor(){super(...arguments),this.type=oa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=oa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends iu{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}ar.unreachable(t)}))}))}))}cook(t){const e=t[0]||new R_,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})}ar.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)))}ar.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new R_,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 ia{static context(){return Ki.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.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=Ki.EVENT}static type(){return tr.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=Ki.MAT}static type(){return tr.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 Ai(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=Rm(),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=Rm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Rm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Pm={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 st.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 km{constructor(t){this._mesh=new k.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 Bm 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 km(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 zm 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(),r=t.state;let s,o;r.buffers.color.setMask(!1),r.buffers.depth.setMask(!1),r.buffers.color.setLocked(!0),r.buffers.depth.setLocked(!0),this.inverse?(s=0,o=1):(s=1,o=0),r.buffers.stencil.setTest(!0),r.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),r.buffers.stencil.setFunc(i.ALWAYS,s,4294967295),r.buffers.stencil.setClear(o),r.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),r.buffers.color.setLocked(!1),r.buffers.depth.setLocked(!1),r.buffers.stencil.setLocked(!1),r.buffers.stencil.setFunc(i.EQUAL,1,4294967295),r.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),r.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 Z(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===Pm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===Bm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new Bm(Pm),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 aa{constructor(){super(...arguments),this.prependRenderPass=oa.BOOLEAN(1),this.useRenderTarget=oa.BOOLEAN(1),this.tmagFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=oa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=oa.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=ai.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=Ki.POST}static type(){return tr.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=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(43);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var rf=new Uint16Array(32),sf=0;sf<32;sf++)rf[sf]=28898;const of=new mo.a(rf,32,1,w.gb,w.M);class af extends ia{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new Bi(this)}static context(){return Ki.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 uf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${sr.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),m.isNumber(i)&&(i=sr.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return sr.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:sr.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return sr.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:sr.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const hf=/\\\\/+/g;class df extends ia{static context(){return Ki.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()==Ki.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(hf,\\\\\\\"_\\\\\\\")}_${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 uf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return uf.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 aa{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const ff=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=oa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=oa.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 ir.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(),r=t.get(i);r?r.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 ${r.data_type} from node '${r.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,Do.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 Ef 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} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Mf={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,r;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(!s)return;const o=t.glVarName(e),a=s.type(),l=new Ef(t,a,o);n.addDefinitions(t,[l]);const c=null===(r=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===r?void 0:r.assembler;if(!c)return;const u=c.shader_config(n.current_shader_name);if(!u)return;const h=u.dependencies(),d=[],p=`${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 h)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==h.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Mf[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 r,s;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 Ef(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const h=new Map;h.set(xf.FRAGMENT,[]),u.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(s=null===(r=t.material_node)||void 0===r?void 0:r.assemblerController)||void 0===s?void 0:s.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)u.pushOnArrayAtEntry(c,t,l),u.pushOnArrayAtEntry(h,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),h.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=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const Nf=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.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 Ef(this,e,n),r=`${n} = ${uf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[r],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 Rf{static overlay(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.max(t.width,e.width),r.height=Math.max(t.height,e.height);let s=r.getContext(\\\\\\\"2d\\\\\\\");s.drawImage(t,0,0,t.width,t.height),s.drawImage(e,0,0,e.width,e.height);const o=r.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 r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=t,r.height=e;let s=r.getContext(\\\\\\\"2d\\\\\\\");s.beginPath(),s.rect(0,0,t,e),s.fillStyle=\\\\\\\"white\\\\\\\",s.fill();const o=r.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 r=Math.min(t.width,t.height),s=t.width/t.height;i.width=r,i.height=r;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=s>1,l=a?(t.width-r)/2:(t.height-r)/2;a?o.drawImage(t,l,0,r,r,0,0,r,r):o.drawImage(t,0,l,r,r,0,0,r,r);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),u=new Image;u.onload=()=>{e(u)},u.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 Pf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Pf||(Pf={}));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=Rf.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,r=this._data_texture.image.data,s=4*n;for(let t=0;t0:r.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(r.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":r.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":r.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":r.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":r.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":r.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":r.uniforms[e].value=(new U.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":r.uniforms[e].value=(new A.a).fromArray(i.value);break;default:r.uniforms[e].value=i.value}}if(void 0!==t.defines&&(r.defines=t.defines),void 0!==t.vertexShader&&(r.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(r.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)r.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(r.flatShading=1===t.shading),void 0!==t.size&&(r.size=t.size),void 0!==t.sizeAttenuation&&(r.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(r.map=n(t.map)),void 0!==t.matcap&&(r.matcap=n(t.matcap)),void 0!==t.alphaMap&&(r.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(r.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(r.bumpScale=t.bumpScale),void 0!==t.normalMap&&(r.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(r.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),r.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(r.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(r.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(r.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(r.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(r.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(r.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(r.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(r.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(r.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(r.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(r.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(r.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(r.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(r.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(r.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(r.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(r.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(r.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(r.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(r.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(r.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(r.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(r.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(r.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(r.thicknessMap=n(t.thicknessMap)),r}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 U.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(Ki.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 aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2(Qf),this.useCameraRenderer=oa.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 k.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 fr,this._texture_camera=new tf.a,this._children_controller_context=Ki.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return Hn.GL_TEXTURE}_create_assembler_controller(){const t=ai.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(),r=t.assembler.uniforms();i&&r&&(this._fragment_shader=i,this._uniforms=r),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(),r=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(r,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(r))}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(Pf.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,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return ai.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}],rg=[{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}],sg=[{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\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),hg=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=oa.BOOLEAN(0,{...lg}),this.encoding=oa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=oa.BOOLEAN(0,{...cg}),this.mapping=oa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=oa.BOOLEAN(0,{...ug}),this.wrapS=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...ug}),this.wrapT=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...ug}),this.tminFilter=oa.BOOLEAN(0,{...hg}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...hg}),this.tmagFilter=oa.BOOLEAN(0,{...hg}),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...hg}),this.tanisotropy=oa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=oa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=oa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=oa.BOOLEAN(0,{...pg}),this.flipY=oa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=oa.BOOLEAN(0,{..._g}),this.offset=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=oa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=oa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=oa.BOOLEAN(0,{...yg}),this.tformat=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=oa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=oa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(aa)){}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: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const r=Object.keys(i)[0];i[r]==e[n]&&t.push(`${n}: ${r}`)}}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: ${e.flipY}`),t}}class Tg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=oa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=oa.BUTTON(null,{cook:!1,callback:t=>{Mg.PARAM_CALLBACK_update(t)}})}}}(aa))){}const Eg=new Ag;class Mg extends af{constructor(){super(...arguments),this.paramsConfig=Eg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${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 aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=oa.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(),r=255*i[0],s=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=Pg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(Ki.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;Rg[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 kg=new class extends aa{constructor(){super(...arguments),this.useCameraRenderer=oa.BOOLEAN(1),this.mode=oa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class Bg extends af{constructor(){super(...arguments),this.paramsConfig=kg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.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 wt(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(Pf.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 zg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==s?s:w.V,this.magFilter=void 0!==r?r: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)}}zg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(29);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=ai.assetUrls.remapedUrl(t);if(e)return e;const n=ai.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}${t}`)}this._node&&await ai.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return ai.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,r=n.fullUrl,s=t.node;e.push(ai.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:r,node:s}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}var Wg;jg.loadingManager=Hg,function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(Wg||(Wg={}));Wg.JPEG,Wg.JPG,Wg.PNG,Wg.EXR,Wg.BASIS,Wg.HDR;class qg extends jg{constructor(t,e,n,i){super(t,i,n),this._param=e,this._node=n,this._scene=i}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=xi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),r=await this._urlToLoad();if(qg.VIDEO_EXTENSIONS.includes(i)){const t=await this._load_as_video(r);e(t)}else this.loader_for_ext(i,t).then((async t=>{t?(qg.increment_in_progress_loads_count(),await qg.wait_for_max_concurrent_loads_queue_freed(),t.load(r,(t=>{qg.decrement_in_progress_loads_count();const n=qg._onTextureLoadedCallback;n&&n(r,t),e(t)}),void 0,(t=>{qg.decrement_in_progress_loads_count(),ai.warn(\\\\\\\"error\\\\\\\",t),n()}))):n()}))}))}static module_names(t){switch(t){case Wg.EXR:return[Vn.EXRLoader];case Wg.HDR:return[Vn.RGBELoader];case Wg.BASIS:return[Vn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case Wg.EXR:return await this._exr_loader(e);case Wg.HDR:return await this._hdr_loader(e);case Wg.BASIS:return await qg._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await ai.modulesRegister.module(Vn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await ai.modulesRegister.module(Vn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await ai.modulesRegister.module(Vn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.BASISPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const s=await ai.renderersController.waitForRenderer();return s?n.detectSupport(s):ai.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}_load_as_video(t){return new Promise(((e,n)=>{const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const t=new zg(i);e(t)};const r=document.createElement(\\\\\\\"source\\\\\\\"),s=jg.extension(t);let o=qg.VIDEO_SOURCE_TYPE_BY_EXT[s];o=o||qg._default_video_source_type(t),r.setAttribute(\\\\\\\"type\\\\\\\",o),r.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(r);let a=t;a=\\\\\\\"mp4\\\\\\\"==s?qg.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):qg.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=qg.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||qg._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static _default_video_source_type(t){return`video/${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 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)}))}}qg.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,qg.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,qg.VIDEO_EXTENSIONS=[\\\\\\\"mp4\\\\\\\",\\\\\\\"webm\\\\\\\",\\\\\\\"ogv\\\\\\\"],qg.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\\\\\\\"'},qg.MAX_CONCURRENT_LOADS_COUNT=qg._init_max_concurrent_loads_count(),qg.CONCURRENT_LOADS_DELAY=qg._init_concurrent_loads_delay(),qg.in_progress_loads_count=0,qg._queue=[];var Xg=n(115);class Yg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{Jg.PARAM_CALLBACK_reload(t)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{Jg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=oa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Jg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(aa))){}const $g=new Yg;class Jg extends af{constructor(){super(...arguments),this.paramsConfig=$g,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 qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.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){const e=await fetch(this.pv.url),n=await e.arrayBuffer(),i=await Object(Xg.parseGIF)(n);this._parsedFrames=await Object(Xg.decompressFrames)(i,!0);const r=this._parsedFrames[0];if(this._frameDelay=r.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),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()}}const Zg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\"];class Qg{static isStaticImageUrl(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\"),n=e[e.length-1];return!Zg.includes(n)}}class Kg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{ev.PARAM_CALLBACK_reload(t,e)}})}}}(aa))){}const tv=new Kg;class ev extends af{constructor(){super(...arguments),this.paramsConfig=tv,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 qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.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(Qg.isStaticImageUrl(this.pv.url)){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()}else this.states.error.set(\\\\\\\"input is not an image\\\\\\\")}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 qg(t,n,this,this.scene());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 nv=n(32);const iv=.005;class rv{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new fr,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:iv,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 au.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Z(this.res,this.res,{type:n}),this.progressiveLightMap2=new Z(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 r=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,r)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(r-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 s={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=s.previousShadowMap,i.uniforms.pixelOffset=s.pixelOffset,n.uniforms.previousShadowMap=s.previousShadowMap,n.uniforms.pixelOffset=s.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:iv}},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:iv}};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 sv=new class extends aa{constructor(){super(...arguments),this.update=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=oa.BOOLEAN(1),this.lightMapRes=oa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=oa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=oa.FLOAT(iv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=oa.BOOLEAN(1),this.blurAmount=oa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=oa.FLOAT(1,{range:[0,10]}),this.objectsMask=oa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=oa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class ov extends af{constructor(){super(...arguments),this.paramsConfig=sv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await ai.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new rv(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(Pf.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 nv.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 nv.a||!n.has(e)&&e instanceof k.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 av=new aa;class lv extends af{constructor(){super(...arguments),this.paramsConfig=av}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const cv=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE];class uv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:cv}}),this.resolution=oa.VECTOR2([1024,1024]),this.useCameraRenderer=oa.BOOLEAN(1),this.render=oa.BUTTON(null,{callback:t=>{dv.PARAM_CALLBACK_render(t)}})}}}(aa))){}const hv=new uv;class dv extends af{constructor(){super(...arguments),this.paramsConfig=hv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(Ki.OBJ),this._camera_node&&cv.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(Pf.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,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const pv=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.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 mv extends(xg(aa)){}const fv=new mv;class gv extends af{constructor(){super(...arguments),this.paramsConfig=fv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class vv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_VIDEO]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{xv.PARAM_CALLBACK_reload(t,e)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=oa.FLOAT(0,{cook:!1}),this.setVideoTime=oa.BUTTON(null,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_time(t)}})}}}(aa))){}const yv=new vv;class xv extends af{constructor(){super(...arguments),this.paramsConfig=yv,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 qg.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(Qi.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(Qg.isStaticImageUrl(this.pv.url))this.states.error.set(\\\\\\\"input 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()}}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 qg(t,n,this,this.scene());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 bv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=oa.VECTOR2([1024,1024])}}}(aa))){}const wv=new bv;class Tv extends af{constructor(){super(...arguments),this.paramsConfig=wv,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(Qi.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 zg(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 Av extends ia{static context(){return Ki.COP}cook(){this.cookController.endCook()}}class Ev extends Av{}class Mv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Av{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Rv,Pv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Rv||(Rv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Pv||(Pv={}));const Iv=new class extends aa{constructor(){super(...arguments),this.animation=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.ANIM},dependentOnFoundNode:!1}),this.play=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_pause(t)}})}};class Fv extends Ba{constructor(){super(...arguments),this.paramsConfig=Iv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Rv.START,$o.BASE,this._play.bind(this)),new Jo(Rv.STOP,$o.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Pv.START,$o.BASE),new Jo(Pv.COMPLETE,$o.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(Ki.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(Pv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Pv.COMPLETE,t)}}const Dv=\\\\\\\"event\\\\\\\";const kv=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(1),this.inputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Bv extends Ba{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((()=>Dv)),this.io.connection_points.set_expected_output_types_function((()=>[$o.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill($o.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Dv,t)}}const zv=new class extends aa{constructor(){super(...arguments),this.blocking=oa.BOOLEAN(1)}};class Uv extends Ba{constructor(){super(...arguments),this.paramsConfig=zv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Uv.OUTPUT,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Uv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Gv;Uv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Gv||(Gv={}));const Vv=new class extends aa{constructor(){super(...arguments),this.dispatch=oa.BUTTON(null,{callback:t=>{Hv.PARAM_CALLBACK_execute(t)}})}};class Hv extends Ba{constructor(){super(...arguments),this.paramsConfig=Vv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(Gv.OUT,$o.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Gv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class jv extends Ba{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 r=await this.create_controls_instance(t,i),s=this._controls_by_viewer.get(e);s&&s.dispose(),this._controls_by_viewer.set(e,r);const o=ai.performance.performanceManager().now();return r.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setup_controls(r),r}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Wv=n(28);const qv=new p.a(0,0,1),Xv=new Wv.a,Yv=new au.a,$v=new au.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Jv={type:\\\\\\\"change\\\\\\\"};class Zv extends $.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new au.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},r=function(){e.screenOrientation=window.orientation||0};this.connect=function(){r(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?Ln.e(t.alpha)+e.alphaOffset:0,r=t.beta?Ln.e(t.beta):0,s=t.gamma?Ln.e(t.gamma):0,o=e.screenOrientation?Ln.e(e.screenOrientation):0;!function(t,e,n,i,r){Xv.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler(Xv),t.multiply($v),t.multiply(Yv.setFromAxisAngle(qv,-r))}(e.object.quaternion,i,r,s,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Jv))}},this.dispose=function(){e.disconnect()},this.connect()}}const Qv=new class extends aa{constructor(){super(...arguments),this.enabled=oa.BOOLEAN(1)}};class Kv extends jv{constructor(){super(...arguments),this.paramsConfig=Qv,this._controls_by_element_id=new Map}static type(){return rr.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async create_controls_instance(t,e){const n=new Zv(t);return this._controls_by_element_id.set(e.id,n),n}setup_controls(t){t.enabled=this.pv.enabled}update_required(){return!0}dispose_controls_for_html_element_id(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ty{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(Ln.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const ey={type:\\\\\\\"change\\\\\\\"},ny={type:\\\\\\\"start\\\\\\\"},iy={type:\\\\\\\"end\\\\\\\"};class ry extends $.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(ey),n.update(),r=i.NONE},this.update=function(){const e=new p.a,h=(new au.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=h.clone().invert(),_=new p.a,m=new au.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(h),o.setFromVector3(e),n.autoRotate&&r===i.NONE&&M(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,!!(u||_.distanceToSquared(n.object.position)>s||8*(1-m.dot(n.object.quaternion))>s)&&(n.dispatchEvent(ey),_.copy(n.object.position),m.copy(n.object.quaternion),u=!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 r=i.NONE;const s=1e-6,o=new ty,a=new ty;let l=1;const c=new p.a;let u=!1;const h=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 E(){return Math.pow(.95,n.zoomSpeed)}function M(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 r=n.domElement;if(n.object.isPerspectiveCamera){const s=n.object.position;t.copy(s).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/r.clientHeight,n.object.matrix),N(2*i*o/r.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/r.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/r.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(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(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(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){h.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)h.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);h.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 k(){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 B(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(_,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_)}function z(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,r=t.pageY-e.y,s=Math.sqrt(i*i+r*r);x.set(0,s),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(),r=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&k(),n.enablePan&&D(),r=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&k(),n.enableRotate&&F(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(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),r=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),r=i.PAN}else{if(!1===n.enableRotate)return;P(t),r=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;P(t),r=i.ROTATE}else{if(!1===n.enablePan)return;I(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;B(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(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&&z(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&&B(t)}(t),n.update();break;default:r=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,h).multiplyScalar(n.rotateSpeed);var e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.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(E()):b.y<0&&R(E()),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(iy),r=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||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(ny),function(t){t.deltaY<0?R(E()):t.deltaY>0&&O(E()),n.update()}(t),n.dispatchEvent(iy))}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:M(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:M(-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{hy.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=oa.BOOLEAN(0),this.keysMode=oa.INTEGER(cy.indexOf(ly.PAN),{visibleIf:{enableKeys:1},menu:{entries:cy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=oa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.PAN)}}),this.keysRotateSpeedVertical=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}}),this.keysRotateSpeedHorizontal=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}})}};class hy extends jv{constructor(){super(...arguments),this.paramsConfig=uy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rr.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(oy,$o.BASE),new Jo(ay,$o.BASE),new Jo(\\\\\\\"end\\\\\\\",$o.BASE)])}async create_controls_instance(t,e){const n=new ry(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(oy,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(ay,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setup_controls(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=cy[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)}update_required(){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())}))}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class dy extends hy{static type(){return rr.MAP}async create_controls_instance(t,e){const n=new sy(t,e);return this._bind_listeners_to_controls_instance(n),n}}const py=new class extends aa{constructor(){super(...arguments),this.delay=oa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class _y extends Ba{constructor(){super(...arguments),this.paramsConfig=py}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new 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t!==e?(n-t)/(e-t):0},lerp:hx,damp:function(t,e,n,i){return hx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(ux(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&&(ix=t%2147483647),ix=16807*ix%2147483647,(ix-1)/2147483646},degToRad:function(t){return t*rx},radToDeg:function(t){return t*sx},isPowerOfTwo:dx,ceilPowerOfTwo:px,floorPowerOfTwo:_x,setQuaternionFromProperEuler:function(t,e,n,i,r){const s=Math.cos,o=Math.sin,a=s(n/2),l=o(n/2),c=s((e+i)/2),u=o((e+i)/2),h=s((e-i)/2),d=o((e-i)/2),p=s((i-e)/2),_=o((i-e)/2);switch(r){case\\\\\\\"XYX\\\\\\\":t.set(a*u,l*h,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*u,l*h,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*h,l*d,a*u,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*u,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*u,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*u,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+r)}}});class fx{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. 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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),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}fx.prototype.isVector2=!0;class gx{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,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,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,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=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 r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*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],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,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,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+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,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+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 vx(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}gx.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function yx(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let xx;class bx{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===xx&&(xx=yx(\\\\\\\"canvas\\\\\\\")),xx.width=t.width,xx.height=t.height;const n=xx.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=xx}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 wx=0;class Tx extends nx{constructor(t=Tx.DEFAULT_IMAGE,e=Tx.DEFAULT_MAPPING,n=1001,i=1001,r=1006,s=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:wx++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=s,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new fx(0,0),this.repeat=new fx(1,1),this.center=new fx(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gx,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=lx()),!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 wy:t.x=t.x-Math.floor(t.x);break;case Ty:t.x=t.x<0?0:1;break;case Ay: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 wy:t.y=t.y-Math.floor(t.y);break;case Ty:t.y=t.y<0?0:1;break;case Ay: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 Ax(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?bx.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.\\\\\\\"),{})}Tx.DEFAULT_IMAGE=void 0,Tx.DEFAULT_MAPPING=300,Tx.prototype.isTexture=!0;class Ex{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,r=this.w,s=t.elements;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.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-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,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}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],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+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(cx(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,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*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,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*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=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,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(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Cx.prototype.isQuaternion=!0;class Nx{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(Ox.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Ox.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);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}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,o=t.z,a=t.w,l=a*e+s*i-o*n,c=a*n+o*e-r*i,u=a*i+r*n-s*e,h=-r*e-s*n-o*i;return this.x=l*a+h*-r+c*-o-u*-s,this.y=c*a+h*-s+u*-r-l*-o,this.z=u*a+h*-o+l*-s-c*-r,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,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[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,r=t.z,s=e.x,o=e.y,a=e.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,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 Lx.copy(this).projectOnVector(t),this.sub(Lx)}reflect(t){return this.sub(Lx.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(cx(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}}Nx.prototype.isVector3=!0;const Lx=new Nx,Ox=new Cx;class Rx{constructor(t=new Nx(1/0,1/0,1/0),e=new Nx(-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,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.length;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.count;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,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,Ix),Ix.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(Vx),Hx.subVectors(this.max,Vx),Dx.subVectors(t.a,Vx),kx.subVectors(t.b,Vx),Bx.subVectors(t.c,Vx),zx.subVectors(kx,Dx),Ux.subVectors(Bx,kx),Gx.subVectors(Dx,Bx);let e=[0,-zx.z,zx.y,0,-Ux.z,Ux.y,0,-Gx.z,Gx.y,zx.z,0,-zx.x,Ux.z,0,-Ux.x,Gx.z,0,-Gx.x,-zx.y,zx.x,0,-Ux.y,Ux.x,0,-Gx.y,Gx.x,0];return!!qx(e,Dx,kx,Bx,Hx)&&(e=[1,0,0,0,1,0,0,0,1],!!qx(e,Dx,kx,Bx,Hx)&&(jx.crossVectors(zx,Ux),e=[jx.x,jx.y,jx.z],qx(e,Dx,kx,Bx,Hx)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Ix.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Ix).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()||(Px[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Px[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Px[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Px[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Px[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Px[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Px[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Px[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Px)),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)}}Rx.prototype.isBox3=!0;const Px=[new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx],Ix=new Nx,Fx=new Rx,Dx=new Nx,kx=new Nx,Bx=new Nx,zx=new Nx,Ux=new Nx,Gx=new Nx,Vx=new Nx,Hx=new Nx,jx=new Nx,Wx=new Nx;function qx(t,e,n,i,r){for(let s=0,o=t.length-3;s<=o;s+=3){Wx.fromArray(t,s);const o=r.x*Math.abs(Wx.x)+r.y*Math.abs(Wx.y)+r.z*Math.abs(Wx.z),a=e.dot(Wx),l=n.dot(Wx),c=i.dot(Wx);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Xx=new Rx,Yx=new Nx,$x=new Nx,Jx=new Nx;class Zx{constructor(t=new Nx,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):Xx.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=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){Jx.subVectors(t,this.center);const e=Jx.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Jx.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return $x.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Yx.copy(t.center).add($x)),this.expandByPoint(Yx.copy(t.center).sub($x)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Qx=new Nx,Kx=new Nx,tb=new Nx,eb=new Nx,nb=new Nx,ib=new Nx,rb=new Nx;class sb{constructor(t=new Nx,e=new Nx(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,Qx)),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=Qx.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Qx.copy(this.direction).multiplyScalar(e).add(this.origin),Qx.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Kx.copy(t).add(e).multiplyScalar(.5),tb.copy(e).sub(t).normalize(),eb.copy(this.origin).sub(Kx);const r=.5*t.distanceTo(e),s=-this.direction.dot(tb),o=eb.dot(this.direction),a=-eb.dot(tb),l=eb.lengthSq(),c=Math.abs(1-s*s);let u,h,d,p;if(c>0)if(u=s*a-o,h=s*o-a,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+s*h+2*o)+h*(s*u+h+2*a)+l}else h=r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h=-r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h<=-p?(u=Math.max(0,-(-s*r+o)),h=u>0?-r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-a),r),d=h*(h+2*a)+l):(u=Math.max(0,-(s*r+o)),h=u>0?r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l);else h=s>0?-r:r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(tb).multiplyScalar(h).add(Kx),d}intersectSphere(t,e){Qx.subVectors(t.center,this.origin);const n=Qx.dot(this.direction),i=Qx.dot(Qx)-n*n,r=t.radius*t.radius;if(i>r)return null;const s=Math.sqrt(r-i),o=n-s,a=n+s;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,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Qx)}intersectTriangle(t,e,n,i,r){nb.subVectors(e,t),ib.subVectors(n,t),rb.crossVectors(nb,ib);let s,o=this.direction.dot(rb);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}eb.subVectors(this.origin,t);const a=s*this.direction.dot(ib.crossVectors(eb,ib));if(a<0)return null;const l=s*this.direction.dot(nb.cross(eb));if(l<0)return null;if(a+l>o)return null;const c=-s*eb.dot(rb);return c<0?null:this.at(c/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ob{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,r,s,o,a,l,c,u,h,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=r,f[5]=s,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=u,f[14]=h,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 ob).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/ab.setFromMatrixColumn(t,0).length(),r=1/ab.setFromMatrixColumn(t,1).length(),s=1/ab.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]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*s,e[9]=n[9]*s,e[10]=n[10]*s,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,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+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(cb,t,ub)}lookAt(t,e,n){const i=this.elements;return pb.subVectors(t,e),0===pb.lengthSq()&&(pb.z=1),pb.normalize(),hb.crossVectors(n,pb),0===hb.lengthSq()&&(1===Math.abs(n.z)?pb.x+=1e-4:pb.z+=1e-4,pb.normalize(),hb.crossVectors(n,pb)),hb.normalize(),db.crossVectors(pb,hb),i[0]=hb.x,i[4]=db.x,i[8]=pb.x,i[1]=hb.y,i[5]=db.y,i[9]=pb.y,i[2]=hb.z,i[6]=db.z,i[10]=pb.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,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=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],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+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],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+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],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*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]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],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),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*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,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,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-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*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 r=ab.set(i[0],i[1],i[2]).length();const s=ab.set(i[4],i[5],i[6]).length(),o=ab.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],lb.copy(this);const a=1/r,l=1/s,c=1/o;return lb.elements[0]*=a,lb.elements[1]*=a,lb.elements[2]*=a,lb.elements[4]*=l,lb.elements[5]*=l,lb.elements[6]*=l,lb.elements[8]*=c,lb.elements[9]*=c,lb.elements[10]*=c,e.setFromRotationMatrix(lb),n.x=r,n.y=s,n.z=o,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,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}}ob.prototype.isMatrix4=!0;const ab=new Nx,lb=new ob,cb=new Nx(0,0,0),ub=new Nx(1,1,1),hb=new Nx,db=new Nx,pb=new Nx,_b=new ob,mb=new Cx;class fb{constructor(t=0,e=0,n=0,i=fb.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,r=i[0],s=i[4],o=i[8],a=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(cx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(h,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-cx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(cx(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-s,l)):(this._y=0,this._z=Math.atan2(a,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-cx(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(a,r)):(this._x=0,this._z=Math.atan2(-s,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(cx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-cx(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(o,r)):(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 _b.makeRotationFromQuaternion(t),this.setFromRotationMatrix(_b,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return mb.setFromEuler(this),this.setFromQuaternion(mb,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 Nx(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isEuler=!0,fb.DefaultOrder=\\\\\\\"XYZ\\\\\\\",fb.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class gb{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),r.length>0&&(n.textures=r),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 s(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(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){Rb.subVectors(i,e),Pb.subVectors(n,e),Ib.subVectors(t,e);const s=Rb.dot(Rb),o=Rb.dot(Pb),a=Rb.dot(Ib),l=Pb.dot(Pb),c=Pb.dot(Ib),u=s*l-o*o;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*a-o*c)*h,p=(s*c-o*a)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Fb),Fb.x>=0&&Fb.y>=0&&Fb.x+Fb.y<=1}static getUV(t,e,n,i,r,s,o,a){return this.getBarycoord(t,e,n,i,Fb),a.set(0,0),a.addScaledVector(r,Fb.x),a.addScaledVector(s,Fb.y),a.addScaledVector(o,Fb.z),a}static isFrontFacing(t,e,n,i){return Rb.subVectors(n,e),Pb.subVectors(t,e),Rb.cross(Pb).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 Rb.subVectors(this.c,this.b),Pb.subVectors(this.a,this.b),.5*Rb.cross(Pb).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Vb.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Vb.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return Vb.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return Vb.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Vb.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;Db.subVectors(i,n),kb.subVectors(r,n),zb.subVectors(t,n);const a=Db.dot(zb),l=kb.dot(zb);if(a<=0&&l<=0)return e.copy(n);Ub.subVectors(t,i);const c=Db.dot(Ub),u=kb.dot(Ub);if(c>=0&&u<=c)return e.copy(i);const h=a*u-c*l;if(h<=0&&a>=0&&c<=0)return s=a/(a-c),e.copy(n).addScaledVector(Db,s);Gb.subVectors(t,r);const d=Db.dot(Gb),p=kb.dot(Gb);if(p>=0&&d<=p)return e.copy(r);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(kb,o);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return Bb.subVectors(r,i),o=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(Bb,o);const f=1/(m+_+h);return s=_*f,o=h*f,e.copy(n).addScaledVector(Db,s).addScaledVector(kb,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Hb=0;class jb extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Hb++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=By,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=my,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=Qy,this.stencilZFail=Qy,this.stencilZPass=Qy,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!==By&&(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),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}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++}}jb.prototype.isMaterial=!0;const Wb={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},qb={h:0,s:0,l:0},Xb={h:0,s:0,l:0};function Yb(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 $b(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Jb(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Zb{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=ux(t,1),e=cx(e,0,1),n=cx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=Yb(r,i,t+1/3),this.g=Yb(r,i,t),this.b=Yb(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Wb[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=$b(t.r),this.g=$b(t.g),this.b=$b(t.b),this}copyLinearToSRGB(t){return this.r=Jb(t.r),this.g=Jb(t.g),this.b=Jb(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,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n65535?iw:nw)(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 gx).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 ow.makeRotationFromQuaternion(t),this.applyMatrix4(ow),this}rotateX(t){return ow.makeRotationX(t),this.applyMatrix4(ow),this}rotateY(t){return ow.makeRotationY(t),this.applyMatrix4(ow),this}rotateZ(t){return ow.makeRotationZ(t),this.applyMatrix4(ow),this}translate(t,e,n){return ow.makeTranslation(t,e,n),this.applyMatrix4(ow),this}scale(t,e,n){return ow.makeScale(t,e,n),this.applyMatrix4(ow),this}lookAt(t){return aw.lookAt(t),aw.updateMatrix(),this.applyMatrix4(aw.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(lw).negate(),this.translate(lw.x,lw.y,lw.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 r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));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 r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=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,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),mw.copy(n.boundingSphere),mw.applyMatrix4(r),!1===t.ray.intersectsSphere(mw))return;if(pw.copy(r).invert(),_w.copy(t.ray).applyMatrix4(pw),null!==n.boundingBox&&!1===_w.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:Nw.clone(),object:t}}(t,e,n,i,fw,gw,vw,Cw);if(p){a&&(Ew.fromBufferAttribute(a,c),Mw.fromBufferAttribute(a,u),Sw.fromBufferAttribute(a,h),p.uv=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx)),l&&(Ew.fromBufferAttribute(l,c),Mw.fromBufferAttribute(l,u),Sw.fromBufferAttribute(l,h),p.uv2=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx));const t={a:c,b:u,c:h,normal:new Nx,materialIndex:0};Vb.getNormal(fw,gw,vw,t.normal),p.face=t}return p}Lw.prototype.isMesh=!0;class Rw extends dw{constructor(t=1,e=1,n=1,i=1,r=1,s=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:s};const o=this;i=Math.floor(i),r=Math.floor(r),s=Math.floor(s);const a=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,s,p,_,m,f,g){const v=s/m,y=p/f,x=s/2,b=p/2,w=_/2,T=m+1,A=f+1;let E=0,M=0;const S=new Nx;for(let s=0;s0?1:-1,c.push(S.x,S.y,S.z),u.push(a/m),u.push(1-s/f),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}}Dw.prototype.isShaderMaterial=!0;class kw extends Ob{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ob,this.projectionMatrix=new ob,this.projectionMatrixInverse=new ob}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)}}kw.prototype.isCamera=!0;class Bw extends kw{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*sx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*rx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*sx*Math.atan(Math.tan(.5*rx*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,r,s){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=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*rx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const s=this.view;if(null!==this.view&&this.view.enabled){const t=s.fullWidth,o=s.fullHeight;r+=s.offsetX*i/t,e-=s.offsetY*n/o,i*=s.width/t,n*=s.height/o}const o=this.filmOffset;0!==o&&(r+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+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}}Bw.prototype.isPerspectiveCamera=!0;const zw=90;class Uw extends Ob{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 Bw(zw,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Nx(1,0,0)),this.add(i);const r=new Bw(zw,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new Nx(-1,0,0)),this.add(r);const s=new Bw(zw,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new Nx(0,1,0)),this.add(s);const o=new Bw(zw,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new Nx(0,-1,0)),this.add(o);const a=new Bw(zw,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new Nx(0,0,1)),this.add(a);const l=new Bw(zw,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Nx(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,o,a,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class Gw extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:fy,n,i,r,s,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Gw.prototype.isCubeTexture=!0;class Vw extends Mx{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 Gw(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:Cy,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=By,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 Rw(5,5,5),r=new Dw({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:Pw(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const s=new Lw(i,r),o=e.minFilter;e.minFilter===Ly&&(e.minFilter=Cy);return new Uw(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}Vw.prototype.isWebGLCubeRenderTarget=!0;const Hw=new Nx,jw=new Nx,Ww=new gx;class qw{constructor(t=new Nx(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=Hw.subVectors(n,e).cross(jw.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(Hw),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).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||Ww.getNormalMatrix(t),i=this.coplanarPoint(Hw).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}qw.prototype.isPlane=!0;const Xw=new Zx,Yw=new Nx;class $w{constructor(t=new qw,e=new qw,n=new qw,i=new qw,r=new qw,s=new qw){this.planes=[t,e,n,i,r,s]}set(t,e,n,i,r,s){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(r),o[5].copy(s),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],r=n[1],s=n[2],o=n[3],a=n[4],l=n[5],c=n[6],u=n[7],h=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,u-a,_-h,v-m).normalize(),e[1].setComponents(o+i,u+a,_+h,v+m).normalize(),e[2].setComponents(o+r,u+l,_+d,v+f).normalize(),e[3].setComponents(o-r,u-l,_-d,v-f).normalize(),e[4].setComponents(o-s,u-c,_-p,v-g).normalize(),e[5].setComponents(o+s,u+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Xw.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSprite(t){return Xw.center.set(0,0,0),Xw.radius=.7071067811865476,Xw.applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}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,Yw.y=i.normal.y>0?t.max.y:t.min.y,Yw.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(Yw)<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 Jw(){let t=null,e=!1,n=null,i=null;function r(e,s){n(e,s),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Zw(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,r){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}\\\\\\\"},tT={common:{diffuse:{value:new Zb(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gx},uv2Transform:{value:new gx},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 fx(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 Zb(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 Zb(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}},sprite:{diffuse:{value:new Zb(16777215)},opacity:{value:1},center:{value:new fx(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}}},eT={basic:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.fog]),vertexShader:Kw.meshbasic_vert,fragmentShader:Kw.meshbasic_frag},lambert:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshlambert_vert,fragmentShader:Kw.meshlambert_frag},phong:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},specular:{value:new Zb(1118481)},shininess:{value:30}}]),vertexShader:Kw.meshphong_vert,fragmentShader:Kw.meshphong_frag},standard:{uniforms:Iw([tT.common,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.roughnessmap,tT.metalnessmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag},toon:{uniforms:Iw([tT.common,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.gradientmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshtoon_vert,fragmentShader:Kw.meshtoon_frag},matcap:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,{matcap:{value:null}}]),vertexShader:Kw.meshmatcap_vert,fragmentShader:Kw.meshmatcap_frag},points:{uniforms:Iw([tT.points,tT.fog]),vertexShader:Kw.points_vert,fragmentShader:Kw.points_frag},dashed:{uniforms:Iw([tT.common,tT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Kw.linedashed_vert,fragmentShader:Kw.linedashed_frag},depth:{uniforms:Iw([tT.common,tT.displacementmap]),vertexShader:Kw.depth_vert,fragmentShader:Kw.depth_frag},normal:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,{opacity:{value:1}}]),vertexShader:Kw.meshnormal_vert,fragmentShader:Kw.meshnormal_frag},sprite:{uniforms:Iw([tT.sprite,tT.fog]),vertexShader:Kw.sprite_vert,fragmentShader:Kw.sprite_frag},background:{uniforms:{uvTransform:{value:new gx},t2D:{value:null}},vertexShader:Kw.background_vert,fragmentShader:Kw.background_frag},cube:{uniforms:Iw([tT.envmap,{opacity:{value:1}}]),vertexShader:Kw.cube_vert,fragmentShader:Kw.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Kw.equirect_vert,fragmentShader:Kw.equirect_frag},distanceRGBA:{uniforms:Iw([tT.common,tT.displacementmap,{referencePosition:{value:new Nx},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Kw.distanceRGBA_vert,fragmentShader:Kw.distanceRGBA_frag},shadow:{uniforms:Iw([tT.lights,tT.fog,{color:{value:new Zb(0)},opacity:{value:1}}]),vertexShader:Kw.shadow_vert,fragmentShader:Kw.shadow_frag}};function nT(t,e,n,i,r){const s=new Zb(0);let o,a,l=0,c=null,u=0,h=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,r)}return{getClearColor:function(){return s},setClearColor:function(t,e=1){s.set(t),l=e,d(s,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(s,l)},render:function(n,r){let p=!1,_=!0===r.isScene?r.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(s,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===xy)?(void 0===a&&(a=new Lw(new Rw(1,1,1),new Dw({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:Pw(eT.cube.uniforms),vertexShader:eT.cube.vertexShader,fragmentShader:eT.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===_&&u===_.version&&h===t.toneMapping||(a.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new Lw(new Qw(2,2),new Dw({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:Pw(eT.background.uniforms),vertexShader:eT.background.vertexShader,fragmentShader:eT.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===_&&u===_.version&&h===t.toneMapping||(o.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function iT(t,e,n,i){const r=t.getParameter(34921),s=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==s,a={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):s.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):s.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let s=l[e];if(void 0===s&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&r.instanceMatrix&&(s=r.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&r.instanceColor&&(s=r.instanceColor)),void 0!==s){const e=s.normalized,o=s.itemSize,l=n.get(s);if(void 0===l)continue;const c=l.buffer,u=l.type,h=l.bytesPerElement;if(s.isInterleavedBufferAttribute){const n=s.data,l=n.stride,d=s.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 s=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=r(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,u=t.getParameter(34930),h=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=h>0,y=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,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:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(36183):0}}function oT(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new qw,a=new gx,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length0){const o=t.getRenderTarget(),a=new Vw(s.height/2);return a.fromEquirectangularTexture(t,r),e.set(r,a),t.setRenderTarget(o),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}eT.physical={uniforms:Iw([eT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new fx(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new Zb(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new fx},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Zb(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Zb(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag};class lT extends kw{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=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=r,this.far=s,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,r,s){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=r,this.view.height=s,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 r=n-t,s=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;r+=t*this.view.offsetX,s=r+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,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}}lT.prototype.isOrthographicCamera=!0;class cT extends Dw{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}cT.prototype.isRawShaderMaterial=!0;const uT=Math.pow(2,8),hT=[.125,.215,.35,.446,.526,.582],dT=5+hT.length,pT=20,_T={[Yy]:0,[$y]:1,[Zy]:2,3004:3,3005:4,3006:5,[Jy]:6},mT=new lT,{_lodPlanes:fT,_sizeLods:gT,_sigmas:vT}=MT(),yT=new Zb;let xT=null;const bT=(1+Math.sqrt(5))/2,wT=1/bT,TT=[new Nx(1,1,1),new Nx(-1,1,1),new Nx(1,1,-1),new Nx(-1,1,-1),new Nx(0,bT,wT),new Nx(0,bT,-wT),new Nx(wT,0,bT),new Nx(-wT,0,bT),new Nx(bT,wT,0),new Nx(-bT,wT,0)];class AT{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Nx(0,1,0);return new cT({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:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),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${RT()}\\\\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})}(pT),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){xT=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=LT(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=NT(),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?uT:0,uT,uT),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===By&&e.type===Oy&&e.encoding===$y?t.value=_T[3e3]:t.value=_T[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=LT()):null==this._equirectShader&&(this._equirectShader=NT());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new Lw(fT[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),CT(e,0,0,3*uT,2*uT),n.setRenderTarget(e),n.render(r,mT)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;epT&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,mT)}}function ET(t){return void 0!==t&&t.type===Oy&&(t.encoding===Yy||t.encoding===$y||t.encoding===Jy)}function MT(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=hT[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);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(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new dw;y.setAttribute(\\\\\\\"position\\\\\\\",new ew(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new ew(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new ew(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function ST(t){const e=new Mx(3*uT,3*uT,t);return e.texture.mapping=xy,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function CT(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function NT(){const t=new fx(1,1);return new cT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),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${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function LT(){return new cT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\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 OT(){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 RT(){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 r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const 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sE(t,e){const n=Kw[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return rE(n)}const oE=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,aE=/#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 lE(t){return t.replace(aE,uE).replace(oE,cE)}function cE(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),uE(t,e,n,i)}function uE(t,e,n,i){let r=\\\\\\\"\\\\\\\";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(tE).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\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[hE(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 \\\\\\\"+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.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define 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y=v+g+a,x=XA(r,35633,v+f+o),b=XA(r,35632,y);if(r.attachShader(m,x),r.attachShader(m,b),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(x).trim(),n=r.getShaderInfoLog(b).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,35714)){i=!1;const e=JA(r,x,\\\\\\\"vertex\\\\\\\"),n=JA(r,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(s=!1);s&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return r.deleteShader(x),r.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new qA(r,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let r=0;r0,C=s.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:s.type,vertexShader:A,fragmentShader:E,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==M?f(M.texture):t.outputEncoding,map:!!s.map,mapEncoding:f(s.map),matcap:!!s.matcap,matcapEncoding:f(s.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===xy||b.mapping===by),lightMap:!!s.lightMap,lightMapEncoding:f(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:f(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:1===s.normalMapType,tangentSpaceNormalMap:0===s.normalMapType,clearcoat:C,clearcoatMap:C&&!!s.clearcoatMap,clearcoatRoughnessMap:C&&!!s.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,specularIntensityMap:!!s.specularIntensityMap,specularTintMap:!!s.specularTintMap,specularTintMapEncoding:f(s.specularTintMap),alphaMap:!!s.alphaMap,alphaTest:S,gradientMap:!!s.gradientMap,sheen:s.sheen>0,transmission:s.transmission>0,transmissionMap:!!s.transmissionMap,thicknessMap:!!s.thicknessMap,combine:s.combine,vertexTangents:!!s.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatMap||s.clearcoatRoughnessMap||s.clearcoatNormalMap||s.displacementMap||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmission>0||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap||!s.displacementMap),fog:!!y,useFog:s.fog,fogExp2:y&&y.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:u,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:s.format,dithering:s.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:s.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,doubleSided:2===s.side,flipSided:1===s.side,depthPacking:void 0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.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:s.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?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||mE),r.length>1&&r.sort(e||fE),s.length>1&&s.sort(e||fE)}}}function vE(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new gE(t),e.set(n,[r])):i>=e.get(n).length?(r=new gE(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function yE(){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 Nx,color:new Zb};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new Nx,direction:new Nx,color:new Zb,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new Nx,color:new Zb,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new Nx,skyColor:new Zb,groundColor:new Zb};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new Zb,position:new Nx,halfWidth:new Nx,halfHeight:new Nx}}return t[e.id]=n,n}}}let xE=0;function bE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function wE(t,e){const n=new yE,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 fx};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={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++)r.probe.push(new Nx);const s=new Nx,o=new ob,a=new ob;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(bE);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_FLOAT_1,r.rectAreaLTC2=tT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_HALF_1,r.rectAreaLTC2=tT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=xE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new TE(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class EE extends jb{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}}EE.prototype.isMeshDepthMaterial=!0;class ME extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Nx,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}}ME.prototype.isMeshDistanceMaterial=!0;function SE(t,e,n){let i=new $w;const r=new fx,s=new fx,o=new Ex,a=new EE({depthPacking:3201}),l=new ME,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new Dw({uniforms:{shadow_pass:{value:null},resolution:{value:new fx},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 dw;_.setAttribute(\\\\\\\"position\\\\\\\",new ew(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new Lw(_,d),f=this;function g(n,i){const r=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,r,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,r,p,m,null)}function v(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.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 r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[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===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function y(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;lu||r.y>u)&&(r.x>u&&(s.x=Math.floor(u/p.x),r.x=s.x*p.x,h.mapSize.x=s.x),r.y>u&&(s.y=Math.floor(u/p.y),r.y=s.y*p.y,h.mapSize.y=s.y)),null===h.map&&!h.isPointLightShadow&&3===this.type){const t={minFilter:Cy,magFilter:Cy,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Mx(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){const t={minFilter:Ey,magFilter:Ey,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.camera.updateProjectionMatrix()}t.setRenderTarget(h.map),t.clear();const _=h.getViewportCount();for(let t=0;t<_;t++){const e=h.getViewport(t);o.set(s.x*e.x,s.y*e.y,s.x*e.z,s.y*e.w),d.viewport(o),h.updateMatrices(c,t),i=h.getFrustum(),y(n,a,h.camera,c,this.type)}h.isPointLightShadow||3!==this.type||g(h,a),h.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,h)}}function CE(t,e,n){const i=n.isWebGL2;const r=new function(){let e=!1;const n=new Ex;let i=null;const r=new Ex(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,s,o,a){!0===a&&(e*=o,i*=o,s*=o),n.set(e,i,s,o),!1===r.equals(n)&&(t.clearColor(e,i,s,o),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}},s=new function(){let e=!1,n=null,i=null,r=null;return{setTest:function(t){t?k(2929):B(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){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?k(2960):B(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&r===n&&s===o||(t.stencilFunc(e,n,o),i=e,r=n,s=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,r=null,s=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},u=null,h=!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 E=t.getParameter(35661);let M=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),M=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),M=S>=2);let N=null,L={};const O=t.getParameter(3088),R=t.getParameter(2978),P=(new Ex).fromArray(O),I=(new Ex).fromArray(R);function F(e,n,i){const r=new Uint8Array(4),s=t.createTexture();t.bindTexture(e,s),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(r=i/Math.max(t.width,t.height)),r<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?_x:Math.floor,s=i(r*t.width),o=i(r*t.height);void 0===p&&(p=m(s,o));const a=n?m(s,o):p;a.width=s,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,s,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+s+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return t}function g(t){return dx(t.width)&&dx(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==Ey&&t.minFilter!==Cy}function y(e,n,r,s,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(r,s,o))}function x(n,i,r,s){if(!1===a)return i;if(null!==n){if(void 0!==t[n])return t[n];console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\"+n+\\\\\\\"'\\\\\\\")}let o=i;return 6403===i&&(5126===r&&(o=33326),5131===r&&(o=33325),5121===r&&(o=33321)),6407===i&&(5126===r&&(o=34837),5131===r&&(o=34843),5121===r&&(o=32849)),6408===i&&(5126===r&&(o=34836),5131===r&&(o=34842),5121===r&&(o=s===$y?35907:32856)),33325!==o&&33326!==o&&34842!==o&&34836!==o||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(t){return t===Ey||t===My||t===Sy?9728:9729}function w(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",w),function(e){const n=i.get(e);if(void 0===n.__webglInit)return;t.deleteTexture(n.__webglTexture),i.remove(e)}(n),n.isVideoTexture&&d.delete(n),o.memory.textures--}function T(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",T),function(e){const n=e.texture,r=i.get(e),s=i.get(n);if(!e)return;void 0!==s.__webglTexture&&(t.deleteTexture(s.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(r.__webglFramebuffer[e]),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer[e]);else t.deleteFramebuffer(r.__webglFramebuffer),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer),r.__webglMultisampledFramebuffer&&t.deleteFramebuffer(r.__webglMultisampledFramebuffer),r.__webglColorRenderbuffer&&t.deleteRenderbuffer(r.__webglColorRenderbuffer),r.__webglDepthRenderbuffer&&t.deleteRenderbuffer(r.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,r=n.length;e0&&r.__version!==t.version){const n=t.image;if(void 0===n)console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");else{if(!1!==n.complete)return void O(r,t,e);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(33984+e),n.bindTexture(3553,r.__webglTexture)}function M(e,r){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,r){if(6!==i.image.length)return;L(e,i),n.activeTexture(33984+r),n.bindTexture(34067,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const o=i&&(i.isCompressedTexture||i.image[0].isCompressedTexture),l=i.image[0]&&i.image[0].isDataTexture,u=[];for(let t=0;t<6;t++)u[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const h=u[0],d=g(h)||a,p=s.convert(i.format),_=s.convert(i.type),m=x(i.internalFormat,p,_,i.encoding);let b;if(N(34067,i,d),o){for(let t=0;t<6;t++){b=u[t].mipmaps;for(let e=0;e1||i.get(s).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(s.anisotropy,r.getMaxAnisotropy())),i.get(s).__currentAnisotropy=s.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",w),e.__webglTexture=t.createTexture(),o.memory.textures++)}function O(e,i,r){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=function(t){return!a&&(t.wrapS!==Ty||t.wrapT!==Ty||t.minFilter!==Ey&&t.minFilter!==Cy)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=6402,a?m=i.type===Iy?36012:i.type===Py?33190:i.type===Dy?35056:33189:i.type===Iy&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===zy&&6402===m&&i.type!==Ry&&i.type!==Py&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=Ry,_=s.convert(i.type)),i.format===Uy&&6402===m&&(m=34041,i.type!==Dy&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=Dy,_=s.convert(i.type))),n.texImage2D(3553,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let t=0,e=b.length;t0&&h){for(let t=0,e=b.length;t=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),A+=1,t},this.resetTextureUnits=function(){A=0},this.setTexture2D=E,this.setTexture2DArray=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=M,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==ky||l.type!==Iy&&l.type!==Fy||(l.format=By,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;ea+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class FE extends nx{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new Bw;w.layers.enable(1),w.viewport=new Ex;const T=new Bw;T.layers.enable(2),T.viewport=new Ex;const A=[w,T],E=new OE;E.layers.enable(1),E.layers.enable(2);let M=null,S=null;function C(t){const e=b.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function N(){b.forEach((function(t,e){t.disconnect(e)})),b.clear(),M=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),u=null,_=null,m=null,f=null,d=null,h=null,c=null,r=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=r.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 r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;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?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.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,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):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 kE(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=yx(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=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\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const p=[],_=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=Yy,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const m=this;let f=!1,g=0,v=0,y=null,x=-1,b=null;const w=new Ex,T=new Ex;let A=null,E=e.width,M=e.height,S=1,C=null,N=null;const L=new Ex(0,0,E,M),O=new Ex(0,0,E,M);let R=!1;const P=[],I=new $w;let F=!1,D=!1,k=null;const B=new ob,z=new Nx,U={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function G(){return null===y?S:1}let V,H,j,W,q,X,Y,$,J,Z,Q,K,tt,et,nt,it,rt,st,ot,at,lt,ct,ut,ht=n;function dt(t,n){for(let i=0;i0&&function(t,e,n){if(null===k){const t=!0===o&&!0===H.isWebGL2;k=new(t?Sx:Mx)(1024,1024,{generateMipmaps:!0,type:null!==ct.convert(Fy)?Fy:Oy,minFilter:Ly,magFilter:Ey,wrapS:Ty,wrapT:Ty})}const i=m.getRenderTarget();m.setRenderTarget(k),m.clear();const r=m.toneMapping;m.toneMapping=0,At(t,e,n),m.toneMapping=r,X.updateMultisampleRenderTarget(k),X.updateRenderTargetMipmap(k),m.setRenderTarget(i)}(r,e,n),i&&j.viewport(w.copy(i)),r.length>0&&At(r,e,n),s.length>0&&At(s,e,n),a.length>0&&At(a,e,n)}function At(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?_[_.length-1]:null,p.pop(),h=p.length>0?p[p.length-1]:null},this.getActiveCubeFace=function(){return g},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTarget=function(t,e=0,n=0){y=t,g=e,v=n,t&&void 0===q.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!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=R;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(P.length!==n.length||36064!==P[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&ht.readPixels(e,n,i,r,ct.convert(a),ct.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==y?q.get(y).__webglFramebuffer:null;j.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=ct.convert(e.format);H.isWebGL2&&(6407===o&&(o=32849),6408===o&&(o=32856)),X.setTexture2D(e,0),ht.copyTexImage2D(3553,n,o,t.x,t.y,r,s,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ht.pixelStorei(37440,n.flipY),ht.pixelStorei(37441,n.premultiplyAlpha),ht.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ht.texSubImage2D(3553,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?ht.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ht.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ht.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=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 u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports 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srcTexture.\\\\\\\"),ht.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,g.data)):ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g),ht.pixelStorei(3314,h),ht.pixelStorei(32878,d),ht.pixelStorei(3316,p),ht.pixelStorei(3315,_),ht.pixelStorei(32877,f),0===r&&i.generateMipmaps&&ht.generateMipmap(u),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ut.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends kE{}).prototype.isWebGL1Renderer=!0;class BE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.density=e}clone(){return new BE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}BE.prototype.isFogExp2=!0;class zE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.near=e,this.far=n}clone(){return new zE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}zE.prototype.isFog=!0;class UE extends Ob{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}}UE.prototype.isScene=!0;class GE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Ky,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=lx()}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,r=this.stride;it.far||e.push({distance:a,point:qE.clone(),uv:Vb.getUV(qE,QE,KE,tM,eM,nM,iM,new fx),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 sM(t,e,n,i,r,s){$E.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(JE.x=s*$E.x-r*$E.y,JE.y=r*$E.x+s*$E.y):JE.copy($E),t.copy(e),t.x+=JE.x,t.y+=JE.y,t.applyMatrix4(ZE)}rM.prototype.isSprite=!0;const oM=new Nx,aM=new Ex,lM=new Ex,cM=new Nx,uM=new ob;class hM extends Lw{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ob,this.bindMatrixInverse=new ob}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 Ex,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);dt.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;na)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);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. Use THREE.BufferGeometry instead.\\\\\\\")}}}MM.prototype.isLine=!0;const SM=new Nx,CM=new Nx;class NM extends MM{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 kM(t,e,n,i,r,s,o){const a=PM.distanceSqToPoint(t);if(ar.far)return;s.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}DM.prototype.isPoints=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:ky,this.minFilter=void 0!==s?s:Cy,this.magFilter=void 0!==r?r:Cy,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 BM extends Tx{constructor(t,e,n,i,r,s,o,a,l,c,u,h){super(null,s,o,a,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}BM.prototype.isCompressedTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){if((c=void 0!==c?c:zy)!==zy&&c!==Uy)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===zy&&(n=Ry),void 0===n&&c===Uy&&(n=Dy),super(null,i,r,s,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:Ey,this.minFilter=void 0!==a?a:Ey,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new Nx,new Nx,new Nx,new Vb;class zM{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),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const s=this.getPoint(i),o=this.getPoint(r),a=e||(s.isVector2?new fx:new Nx);return a.copy(o).sub(s).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Nx,i=[],r=[],s=[],o=new Nx,a=new ob;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Nx)}r[0]=new Nx,s[0]=new Nx;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],o),s[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),s[e]=s[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(cx(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(a.makeRotationAxis(o,t))}s[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(cx(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(o.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),s[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:s}}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 UM extends zM{constructor(t=0,e=0,n=1,i=1,r=0,s=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=r,this.aEndAngle=s,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new fx,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const s=Math.abs(r)i;)r-=i;r0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?o=i[(l-1)%r]:(HM.subVectors(i[0],i[1]).add(i[0]),o=HM);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2i.length-2?i.length-1:s+1],u=i[s>i.length-3?i.length-1:s+2];return n.set(YM(o,a.x,l.x,c.x,u.x),YM(o,a.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class oS extends sS{constructor(t){super(t),this.uuid=lx(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n80*n){a=c=t[0],l=u=t[1];for(let e=n;ec&&(c=h),d>u&&(u=d);p=Math.max(c-a,u-l),p=0!==p?1/p:0}return uS(s,o,n,a,l,p),o};function lS(t,e,n,i,r){let s,o;if(r===function(t,e,n,i){let r=0;for(let s=e,o=n-i;s0)for(s=e;s=e;s-=i)o=NS(s,t[s],t[s+1],o);return o&&TS(o,o.next)&&(LS(o),o=o.next),o}function cS(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!TS(i,i.next)&&0!==wS(i.prev,i,i.next))i=i.next;else{if(LS(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function uS(t,e,n,i,r,s,o){if(!t)return;!o&&s&&function(t,e,n,i){let 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wS(t.prev,t,t.next)<0?wS(t,e,t.next)>=0&&wS(t,t.prev,e)>=0:wS(t,e,t.prev)<0||wS(t,t.next,e)<0}function CS(t,e){const n=new OS(t.i,t.x,t.y),i=new OS(e.i,e.x,e.y),r=t.next,s=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,s.next=i,i.prev=s,i}function NS(t,e,n,i){const r=new OS(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function LS(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 OS(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 RS{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r2&&t[e-1].equals(t[0])&&t.pop()}function IS(t,e){for(let n=0;nNumber.EPSILON){const h=Math.sqrt(u),d=Math.sqrt(l*l+c*c),p=e.x-a/h,_=e.y+o/h,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,r=_+a*m-t.y;const f=i*i+r*r;if(f<=2)return new fx(i,r);s=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,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new fx(i/s,r/s)}const O=[];for(let t=0,e=M.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=M.length;t=0;){const i=n;let r=n-1;r<0&&(r=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}}US.prototype.isMeshPhysicalMaterial=!0;class GS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new Zb(16777215),this.specular=new Zb(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(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}}GS.prototype.isMeshPhongMaterial=!0;class VS extends jb{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(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}}VS.prototype.isMeshToonMaterial=!0;class HS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(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}}HS.prototype.isMeshNormalMaterial=!0;class jS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(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}}jS.prototype.isMeshLambertMaterial=!0;class WS extends jb{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new Zb(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(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}}WS.prototype.isMeshMatcapMaterial=!0;class qS extends xM{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}}qS.prototype.isLineDashedMaterial=!0;const XS={arraySlice:function(t,e,n){return XS.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,r=new t.constructor(i);for(let s=0,o=0;o!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[o++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let o=s[i];if(void 0!==o)if(Array.isArray(o))do{o=s[i],void 0!==o&&(e.push(s.time),n.push.apply(n,o)),s=t[r++]}while(void 0!==s);else if(void 0!==o.toArray)do{o=s[i],void 0!==o&&(e.push(s.time),o.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{o=s[i],void 0!==o&&(e.push(s.time),n.push(o)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const s=t.clone();s.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;ns.tracks[t].times[0]&&(a=s.tracks[t].times[0]);for(let t=0;t=i.times[h]){const t=h*l+a,e=t+l-a;d=XS.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(s),d=XS.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===r){(new Cx).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;te;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=XS.arraySlice(n,r,s),this.values=XS.arraySlice(this.values,r*t,s*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,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;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!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&XS.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=XS.arraySlice(this.times),e=XS.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Hy,r=t.length-1;let s=1;for(let o=1;o0){t[s]=t[r];for(let t=r*n,i=s*n,o=0;o!==n;++o)e[i+o]=e[t+o];++s}return s!==t.length?(this.times=XS.arraySlice(t,0,s),this.values=XS.arraySlice(e,0,s*n)):(this.times=t,this.values=e),this}clone(){const t=XS.arraySlice(this.times,0),e=XS.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}QS.prototype.TimeBufferType=Float32Array,QS.prototype.ValueBufferType=Float32Array,QS.prototype.DefaultInterpolation=Vy;class KS extends QS{}KS.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",KS.prototype.ValueBufferType=Array,KS.prototype.DefaultInterpolation=Gy,KS.prototype.InterpolantFactoryMethodLinear=void 0,KS.prototype.InterpolantFactoryMethodSmooth=void 0;class tC extends QS{}tC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class eC extends QS{}eC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class nC extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)Cx.slerpFlat(r,0,s,l-o,s,l,a);return r}}class iC extends QS{InterpolantFactoryMethodLinear(t){return new nC(this.times,this.values,this.getValueSize(),t)}}iC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",iC.prototype.DefaultInterpolation=Vy,iC.prototype.InterpolantFactoryMethodSmooth=void 0;class rC extends QS{}rC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",rC.prototype.ValueBufferType=Array,rC.prototype.DefaultInterpolation=Gy,rC.prototype.InterpolantFactoryMethodLinear=void 0,rC.prototype.InterpolantFactoryMethodSmooth=void 0;class sC extends QS{}sC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class oC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=lx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(aC(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],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(QS.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,s=[];for(let t=0;t1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,r){if(0!==n.length){const s=[],o=[];XS.flattenJSON(n,s,o,i),0!==s.length&&r.push(new t(e,s,o))}},i=[],r=t.name||\\\\\\\"default\\\\\\\",s=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,r=e+e;t!==r;++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,r=i;t!==r;++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!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Cx.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;Cx.multiplyQuaternionsFlat(t,s,t,e,t,n),Cx.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}const HC=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",jC=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),WC=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",qC=\\\\\\\"[^\\\\\\\"+HC.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",XC=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",WC),YC=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",qC),$C=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",WC),JC=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",WC),ZC=new RegExp(\\\\\\\"^\\\\\\\"+XC+YC+$C+JC+\\\\\\\"$\\\\\\\"),QC=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class KC{constructor(t,e,n){this.path=e,this.parsedPath=n||KC.parseTrackName(e),this.node=KC.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 KC.Composite(t,e,n):new KC(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(jC,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=ZC.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!==QC.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(s),e[n].accumulateAdditive(o);break;case Xy:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),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,r=this._loopCount;const s=2202===n;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(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===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;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,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Wy,i.endingEnd=Wy):(i.endingStart=t?this.zeroSlopeAtStart?Wy:jy:qy,i.endingEnd=e?this.zeroSlopeAtEnd?Wy:jy:qy)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}(class extends nx{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,r=i.length,s=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!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new VC(KC.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.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,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);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 nN.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)}}iN.prototype.isBox2=!0;const rN=new Nx,sN=new Nx;class oN{constructor(t=new Nx,e=new Nx){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){rN.subVectors(t,this.start),sN.subVectors(this.end,this.start);const n=sN.dot(sN);let i=sN.dot(rN)/n;return e&&(i=cx(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 Ob{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 aN=new Nx,lN=new ob,cN=new ob;function uN(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 r=Math.abs(n/(Math.abs(n)+Math.abs(i))),s=fN.copy(t.start).lerp(t.end,r);if(e.containsPoint(s))return{normal:vN.normal.clone(),point:s.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=yN.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),r=a,n=e[s])}}return r<1e100&&{distance:r,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 AN(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)}}const MN={type:\\\\\\\"change\\\\\\\"},SN={type:\\\\\\\"lock\\\\\\\"},CN={type:\\\\\\\"unlock\\\\\\\"},NN=Math.PI/2;class LN extends EN{constructor(t,e){super(),this.camera=t,this.domElement=e,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.speed=1,this.euler=new Wv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.vec=new p.a,this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._cameraTmp=new tf.a,this.velocity=new p.a,this.direction=new p.a,this._moveForward=!1,this._moveBackward=!1,this._moveLeft=!1,this._moveRight=!1,this.prevTime=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.euler.x-=.002*n,this.euler.x=Math.max(NN-this.maxPolarAngle,Math.min(NN-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this.dispatchEvent(MN)}}onPointerlockChange(){this.velocity.set(0,0,0),this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(SN),this.isLocked=!0):(this.dispatchEvent(CN),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}moveForward(t,e){this.vec.setFromMatrixColumn(t.matrix,0),this.vec.crossVectors(t.up,this.vec),t.position.addScaledVector(this.vec,e)}moveRight(t,e){this.vec.setFromMatrixColumn(t.matrix,0),t.position.addScaledVector(this.vec,e)}_copyToCameraTmp(){this._cameraTmp.position.copy(this.camera.position),this._cameraTmp.matrix.copy(this.camera.matrix),this._cameraTmp.up.copy(this.camera.up)}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}setMoveForward(t){this._moveForward=t}setMoveBackward(t){this._moveBackward=t}setMoveLeft(t){this._moveLeft=t}setMoveRight(t){this._moveRight=t}update(){const t=performance.now();if(!0===this.isLocked){const e=(t-this.prevTime)/1e3;if(this.velocity.x-=10*this.velocity.x*e,this.velocity.z-=10*this.velocity.z*e,this.velocity.y-=9.8*100*e,this.direction.z=Number(this._moveForward)-Number(this._moveBackward),this.direction.x=Number(this._moveRight)-Number(this._moveLeft),this.direction.normalize(),(this._moveForward||this._moveBackward)&&(this.velocity.z-=400*this.direction.z*e*this.speed),(this._moveLeft||this._moveRight)&&(this.velocity.x-=400*this.direction.x*e*this.speed),this._playerCollisionController){this._copyToCameraTmp(),this.moveRight(this._cameraTmp,-this.velocity.x*e),this.moveForward(this._cameraTmp,-this.velocity.z*e);const t=this._playerCollisionController.testPosition(this._cameraTmp.position);t?(this._cameraTmp.position.add(t.normal.multiplyScalar(t.depth)),this.camera.position.copy(this._cameraTmp.position)):this._applyVelocity(e)}this._applyVelocity(e)}this.prevTime=t}_applyVelocity(t){this.moveRight(this.camera,-this.velocity.x*t),this.moveForward(this.camera,-this.velocity.z*t)}}async function ON(t,e){var n;if(e.pv.collideWithGeo){const i=e.pv.collidingGeo.nodeWithContext(Ki.OBJ);if(i){await i.compute();const r=await(null===(n=i.displayNodeController)||void 0===n?void 0:n.displayNode()),s=(await(null==r?void 0:r.compute())).coreContent();if(!s)return void console.error(\\\\\\\"obj node contains invalid sop\\\\\\\");const o=s.objectsWithGeo()[0];t.setCheckCollisions(o),t.setCollisionCapsule(new mN(new p.a(0,e.pv.capsuleHeightRange.x,0),new p.a(e.pv.capsuleHeightRange.y),e.pv.capsuleRadius))}}else t.setCheckCollisions()}const RN=\\\\\\\"lock\\\\\\\",PN=\\\\\\\"change\\\\\\\",IN=\\\\\\\"unlock\\\\\\\";const FN=new class extends aa{constructor(){super(...arguments),this.lock=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_lock_controls(t)}}),this.minPolarAngle=oa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.speed=oa.FLOAT(1),this.collideWithGeo=oa.BOOLEAN(0),this.collidingGeo=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.GEO]},visibleIf:{collideWithGeo:!0}}),this.recomputeCollidingGeo=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class DN extends jv{constructor(){super(...arguments),this.paramsConfig=FN,this._controls_by_element_id=new Map}static type(){return rr.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(RN,$o.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(RN,$o.BASE),new Jo(PN,$o.BASE),new Jo(IN,$o.BASE)])}async create_controls_instance(t,e){const n=new LN(t,e);return 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(RN,(()=>{this._createKeysEvents(t),this.dispatchEventToOutput(RN,{})})),t.addEventListener(PN,(()=>{this.dispatchEventToOutput(PN,{})})),t.addEventListener(IN,(()=>{this._removeKeysEvents(),this.dispatchEventToOutput(IN,{})}))}update_required(){return!0}setup_controls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.speed=this.pv.speed,this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}lockControls(){let t;this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t&&t.lock()}static PARAM_CALLBACK_lock_controls(t){t.lockControls()}_onKeyDown(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!0);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!0);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!0);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!0)}}_onKeyUp(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!1)}}_createKeysEvents(t){this._onKeyDownBound=e=>{this._onKeyDown(e,t)},this._onKeyUpBound=e=>{this._onKeyUp(e,t)},document.addEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound)}_removeKeysEvents(){this._onKeyDownBound&&this._onKeyUpBound&&(document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound))}}var kN,BN;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}(kN||(kN={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(BN||(BN={}));const zN=new class extends aa{constructor(){super(...arguments),this.maxCount=oa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=oa.BUTTON(null,{callback:t=>{UN.PARAM_CALLBACK_reset(t)}})}};class UN extends Ba{constructor(){super(...arguments),this.paramsConfig=zN,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(kN.TRIGGER,$o.BASE,this.process_event_trigger.bind(this)),new Jo(kN.RESET,$o.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(BN.OUT,$o.BASE),new Jo(BN.LAST,$o.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class jN{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}}const WN={rotationSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI},translationSpeed:.1},qN={type:\\\\\\\"change\\\\\\\"};class XN extends EN{constructor(t,e){super(),this._camera=t,this.domElement=e,this.translationData={direction:{x:0,y:0}},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 Wv.a,this._velocity=new p.a,this._rotationSpeed=WN.rotationSpeed,this._rotationRange={min:WN.rotationRange.min,max:WN.rotationRange.max},this._translationSpeed=WN.translationSpeed,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.prevTime=performance.now(),this._camTmpPost=new p.a,this._camWorldDir=new p.a,this._up=new p.a(0,1,0),this._camSideVector=new p.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),r=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=`${r}px`,t.style.left=`${r}px`,t}_addEvents(){var t;jN.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;jN.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.domElement.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}setTranslationSpeed(t){this._translationSpeed=t}_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=rs.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this.dispatchEvent(qN)}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);const e=this._getTouch(t,this._translateDomElement);e&&(this._translationStartPosition.set(e.clientX,e.clientY),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect())}_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._translationSpeed*this._translationDelta.x/this._translateDomElementRect.width,this.translationData.direction.y=this._translationSpeed*-this._translationDelta.y/this._translateDomElementRect.height,this.dispatchEvent(qN))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.y=0}update(){const t=performance.now(),e=t-this.prevTime;this.prevTime=t,this._translateCamera(this.translationData,e)}_translateCamera(t,e){this._camera.getWorldDirection(this._camWorldDir),this._camWorldDir.y=0,this._camWorldDir.normalize(),this._camSideVector.crossVectors(this._up,this._camWorldDir),this._camSideVector.normalize(),this._camSideVector.multiplyScalar(-t.direction.x),this._camWorldDir.multiplyScalar(t.direction.y),this._velocity.copy(this._camWorldDir),this._velocity.add(this._camSideVector);const n=this._camera.position.y;if(this._camTmpPost.copy(this._camera.position),this._playerCollisionController){const t=1;this._velocity.addScaledVector(this._velocity,t);const n=this._velocity.clone().multiplyScalar(e);this._camTmpPost.add(n);const i=this._playerCollisionController.testPosition(this._camTmpPost);i&&this._camTmpPost.add(i.normal.multiplyScalar(i.depth)),this._camera.position.copy(this._camTmpPost)}else this._camTmpPost.add(this._camSideVector),this._camTmpPost.add(this._camWorldDir),this._camera.position.copy(this._camTmpPost);this._camera.position.y=n}_getTouch(t,e){for(let n=0;n{QN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class QN extends jv{constructor(){super(...arguments),this.paramsConfig=ZN,this._controls_by_element_id=new Map}static type(){return rr.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(YN,$o.BASE),new Jo($N,$o.BASE),new Jo(JN,$o.BASE)])}async create_controls_instance(t,e){const n=new XN(t,e);return 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(YN,(()=>{this.dispatchEventToOutput(YN,{})})),t.addEventListener($N,(()=>{this.dispatchEventToOutput($N,{})})),t.addEventListener(JN,(()=>{this.dispatchEventToOutput(JN,{})}))}update_required(){return!0}setup_controls(t){t.setRotationSpeed(this.pv.rotationSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle}),t.setTranslationSpeed(this.pv.translationSpeed),this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}}var KN;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(KN||(KN={}));const tL=[KN.ALL_TOGETHER,KN.BATCH];const eL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{nL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=oa.BOOLEAN(0),this.cookMode=oa.INTEGER(tL.indexOf(KN.ALL_TOGETHER),{menu:{entries:tL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=oa.INTEGER(1,{visibleIf:{cookMode:tL.indexOf(KN.BATCH)},separatorAfter:!0}),this.updateResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_print_resolve(t)}})}};class nL extends Ba{constructor(){super(...arguments),this.paramsConfig=eL,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 Jo(nL.INPUT_TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(nL.OUTPUT_FIRST_NODE,$o.BASE),new Jo(nL.OUTPUT_EACH_NODE,$o.BASE),new Jo(nL.OUTPUT_ALL_NODES,$o.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=tL[this.pv.cookMode];switch(t){case KN.ALL_TOGETHER:return this._cook_nodes_all_together();case KN.BATCH:return this._cook_nodes_batch()}ar.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(nL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(nL.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(nL.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 iL,rL;nL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",nL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",nL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",nL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(iL||(iL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(rL||(rL={}));const sL=new class extends aa{};class oL extends Ba{constructor(){super(...arguments),this.paramsConfig=sL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(iL.TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(rL.OUT,$o.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(rL.OUT,t)}}var aL,lL=n(39),cL=n(36);class uL{constructor(t,e,n=0,i=1/0){this.ray=new lL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new cL.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 dL(t,this,n,e),n.sort(hL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,r=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=mL[this._node.pv.intersectWith];switch(e){case _L.GEOMETRY:return this._intersect_with_geometry(t);case _L.PLANE:return this._intersect_with_plane(t)}ar.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=kr[this._node.pv.geoAttributeType],n=gL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Dr.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Dr.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ar.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Nr(t.object.constructor)){case Sr.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Sr.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 r=i.getAttribute(e);if(r){switch(n){case Dr.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(r,t.face.a),this._uvB.fromBufferAttribute(r,t.face.b),this._uvC.fromBufferAttribute(r,t.face.c),t.uv=Qr.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Dr.STRING:{const t=new ps(i).points()[0];return t?t.stringAttribValue(e):void 0}}ar.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Dr.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Dr.STRING:{const n=new ps(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ar.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(ai.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Es.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Es.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(Ki.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=SL[this._node.pv.targetType];switch(t){case ML.NODE:return this._update_target_from_node();case ML.SCENE_GRAPH:return this._update_target_from_scene_graph()}ar.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(Ki.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)}}gL._vA=new p.a,gL._vB=new p.a,gL._vC=new p.a,gL._uvA=new d.a,gL._uvB=new d.a,gL._uvC=new d.a,gL._hitUV=new d.a;class vL{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,r=i.renderController;if(r){if(this._render_target=this._render_target||new Z(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,s=r.resolved_scene||i.scene().threejsScene(),o=r.renderer(n);this._modify_scene_and_renderer(s,o),o.setRenderTarget(this._render_target),o.clear(),o.render(s,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(s,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()==Ki.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 yL=1e3/60;var xL;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(xL||(xL={}));const bL=[xL.CPU,xL.GPU];function wL(t={}){return t.mode=bL.indexOf(xL.CPU),{visibleIf:t}}function TL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.GEOMETRY),{visibleIf:t}}function AL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.PLANE),{visibleIf:t}}function EL(t={}){return t.mode=bL.indexOf(xL.GPU),{visibleIf:t}}var ML;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(ML||(ML={}));const SL=[ML.SCENE_GRAPH,ML.NODE];const CL=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(bL.indexOf(xL.CPU),{menu:{entries:bL.map(((t,e)=>({name:t,value:e})))}}),this.mouse=oa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=oa.BOOLEAN(0),this.overrideRay=oa.BOOLEAN(0,{visibleIf:{mode:bL.indexOf(xL.CPU),overrideCamera:1}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=oa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=oa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=oa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{vL.PARAM_CALLBACK_update_material(t)},...EL()}),this.pixelValue=oa.VECTOR4([0,0,0,0],{cook:!1,...EL()}),this.hitThreshold=oa.FLOAT(.5,{cook:!1,...EL()}),this.intersectWith=oa.INTEGER(mL.indexOf(_L.GEOMETRY),{menu:{entries:mL.map(((t,e)=>({name:t,value:e})))},...wL()}),this.pointsThreshold=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...wL()}),this.planeDirection=oa.VECTOR3([0,1,0],{...AL()}),this.planeOffset=oa.FLOAT(0,{...AL()}),this.targetType=oa.INTEGER(0,{menu:{entries:SL.map(((t,e)=>({name:t,value:e})))},...TL()}),this.targetNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.NODE)})}),this.objectMask=oa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=oa.BUTTON(null,{callback:(t,e)=>{gL.PARAM_CALLBACK_print_resolve(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.traverseChildren=oa.BOOLEAN(!0,{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL(),separatorAfter:!0}),this.tpositionTarget=oa.BOOLEAN(0,{cook:!1,...wL()}),this.position=oa.VECTOR3([0,0,0],{cook:!1,...wL({tpositionTarget:0})}),this.positionTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tpositionTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.tvelocity=oa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=oa.BOOLEAN(0,{cook:!1,...wL({tvelocity:1})}),this.velocity=oa.VECTOR3([0,0,0],{cook:!1,...wL({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tvelocity:1,tvelocityTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.geoAttribute=oa.BOOLEAN(0,TL()),this.geoAttributeName=oa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...TL({geoAttribute:1})}),this.geoAttributeType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},...TL({geoAttribute:1})}),this.geoAttributeValue1=oa.FLOAT(0,{cook:!1,...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.NUMERIC)})}),this.geoAttributeValues=oa.STRING(\\\\\\\"\\\\\\\",{...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.STRING)})})}};class NL extends Ba{constructor(){super(...arguments),this.paramsConfig=CL,this.cpuController=new gL(this),this.gpuController=new vL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(NL.INPUT_TRIGGER,$o.BASE,this._process_trigger_event_throttled.bind(this)),new Jo(NL.INPUT_MOUSE,$o.MOUSE,this._process_mouse_event.bind(this)),new Jo(NL.INPUT_UPDATE_OBJECTS,$o.BASE,this._process_trigger_update_objects.bind(this)),new Jo(NL.INPUT_TRIGGER_VEL_RESET,$o.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(NL.OUTPUT_HIT,$o.BASE),new Jo(NL.OUTPUT_MISS,$o.BASE)])}trigger_hit(t){this.dispatchEventToOutput(NL.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(NL.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=ai.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),yL-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.velocity_controller.reset()}}var LL;NL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",NL.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",NL.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",NL.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",NL.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",NL.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(LL||(LL={}));const OL=[LL.SET,LL.TOGGLE];const RL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=oa.BOOLEAN(0),this.displayMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tdisplay:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.display=oa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:OL.indexOf(LL.SET)},separatorAfter:!0}),this.tbypass=oa.BOOLEAN(0),this.bypassMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tbypass:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.bypass=oa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:OL.indexOf(LL.SET)}}),this.execute=oa.BUTTON(null,{callback:t=>{PL.PARAM_CALLBACK_execute(t)}})}};class PL extends Ba{constructor(){super(...arguments),this.paramsConfig=RL}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${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=OL[this.pv.displayMode];switch(i){case LL.SET:return void n.set(this.pv.display);case LL.TOGGLE:return void n.set(!n.active())}ar.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=OL[this.pv.bypassMode];switch(i){case LL.SET:return void n.set(this.pv.bypass);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var IL;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(IL||(IL={}));const FL=[IL.BOOLEAN,IL.BUTTON,IL.NUMBER,IL.VECTOR2,IL.VECTOR3,IL.VECTOR4,IL.STRING],DL=FL.indexOf(IL.BOOLEAN),kL=FL.indexOf(IL.NUMBER),BL=FL.indexOf(IL.VECTOR2),zL=FL.indexOf(IL.VECTOR3),UL=FL.indexOf(IL.VECTOR4),GL=FL.indexOf(IL.STRING),VL=\\\\\\\"output\\\\\\\";const HL=new class extends aa{constructor(){super(...arguments),this.param=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=oa.INTEGER(kL,{menu:{entries:FL.map(((t,e)=>({name:t,value:e})))}}),this.toggle=oa.BOOLEAN(0,{visibleIf:{type:DL}}),this.boolean=oa.BOOLEAN(0,{visibleIf:{type:DL,toggle:0}}),this.number=oa.FLOAT(0,{visibleIf:{type:kL}}),this.vector2=oa.VECTOR2([0,0],{visibleIf:{type:BL}}),this.vector3=oa.VECTOR3([0,0,0],{visibleIf:{type:zL}}),this.vector4=oa.VECTOR4([0,0,0,0],{visibleIf:{type:UL}}),this.increment=oa.BOOLEAN(0,{visibleIf:[{type:kL},{type:BL},{type:zL},{type:UL}]}),this.string=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:GL}}),this.execute=oa.BUTTON(null,{callback:t=>{jL.PARAM_CALLBACK_execute(t)}})}};class jL extends Ba{constructor(){super(...arguments),this.paramsConfig=HL,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 Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(VL,$o.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(VL,t)}async _new_param_value(t){const e=FL[this.pv.type];switch(e){case IL.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case IL.BUTTON:return t.options.executeCallback();case IL.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Es.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case IL.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Es.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 IL.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Es.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 IL.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Es.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 IL.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ar.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 WL=new class extends aa{constructor(){super(...arguments),this.outputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class qL extends Ba{constructor(){super(...arguments),this.paramsConfig=WL}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[$o.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($o.BASE),t}_output_name(t){return`out${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 QL extends ia{static context(){return Ki.EVENT}cook(){this.cookController.endCook()}}class KL extends QL{}class tO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class eO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class nO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rO extends QL{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const oO=\\\\\\\"int\\\\\\\";const aO=new class extends aa{constructor(){super(...arguments),this.float=oa.FLOAT(0)}};class lO extends df{constructor(){super(...arguments),this.paramsConfig=aO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(oO)} = int(${uf.float(e)})`;t.addBodyLines(this,[n])}}const cO=\\\\\\\"float\\\\\\\";const uO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class hO extends df{constructor(){super(...arguments),this.paramsConfig=uO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(cO,Do.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(cO)} = float(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const dO=\\\\\\\"bool\\\\\\\";const pO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class _O extends df{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(dO,Do.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(dO)} = bool(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const mO=new class extends aa{constructor(){super(...arguments),this.bool=oa.BOOLEAN(0)}};class fO extends df{constructor(){super(...arguments),this.paramsConfig=mO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(oO)} = int(${uf.bool(e)})`;t.addBodyLines(this,[n])}}const gO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0)}};class vO extends df{constructor(){super(...arguments),this.paramsConfig=gO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(vO.OUTPUT_NAME,Do.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(vO.OUTPUT_NAME)} = ${uf.float2(e,n)}`;t.addBodyLines(this,[i])}}vO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const yO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0)}};class xO extends df{constructor(){super(...arguments),this.paramsConfig=yO}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(xO.OUTPUT_NAME,Do.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=`vec3 ${this.glVarName(xO.OUTPUT_NAME)} = ${uf.float3(e,n,i)}`;t.addBodyLines(this,[r])}}xO.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const bO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0),this.w=oa.FLOAT(0)}};class wO extends df{constructor(){super(...arguments),this.paramsConfig=bO}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(wO.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=this.variableForInputParam(this.p.w),s=`vec4 ${this.glVarName(wO.OUTPUT_NAME)} = ${uf.float4(e,n,i,r)}`;t.addBodyLines(this,[s])}}wO.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const TO=new class extends aa{};class AO extends df{constructor(){super(...arguments),this.paramsConfig=TO}}function EO(t,e){const n=e.components,i=e.param_type;return class extends AO{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Vo(t,Do.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 MO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],SO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],CO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class NO extends(EO(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Es.VECTOR2})){}class LO extends(EO(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Es.VECTOR3})){}class OO extends(EO(\\\\\\\"vec4ToFloat\\\\\\\",{components:CO,param_type:Es.VECTOR4})){}class RO extends AO{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(RO.OUTPUT_NAME_VEC3,Do.VEC3),new Vo(RO.OUTPUT_NAME_W,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR4,RO.INPUT_NAME_VEC4,CO.map((t=>0)))}setLines(t){const e=[],n=RO.INPUT_NAME_VEC4,i=RO.OUTPUT_NAME_VEC3,r=RO.OUTPUT_NAME_W,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${s}.xyz`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.w`)}t.addBodyLines(this,e)}}RO.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",RO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",RO.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class PO extends AO{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(PO.OUTPUT_NAME_VEC2,Do.VEC2),new Vo(PO.OUTPUT_NAME_Z,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR3,PO.INPUT_NAME_VEC3,SO.map((t=>0)))}setLines(t){const e=[],n=PO.INPUT_NAME_VEC3,i=PO.OUTPUT_NAME_VEC2,r=PO.OUTPUT_NAME_Z,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${s}.xy`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.z`)}t.addBodyLines(this,e)}}PO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",PO.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",PO.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class IO extends AO{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(IO.OUTPUT_NAME_VEC3,Do.VEC3)])}createParams(){this.addParam(Es.VECTOR2,IO.INPUT_NAME_VEC2,MO.map((t=>0))),this.addParam(Es.FLOAT,IO.INPUT_NAME_Z,0)}setLines(t){const e=[],n=IO.INPUT_NAME_VEC2,i=IO.INPUT_NAME_Z,r=IO.OUTPUT_NAME_VEC3,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec3 ${a} = vec3(${s}.xy, ${o})`),t.addBodyLines(this,e)}}IO.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",IO.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",IO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class FO extends AO{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(FO.OUTPUT_NAME_VEC4,Do.VEC4)])}createParams(){this.addParam(Es.VECTOR3,FO.INPUT_NAME_VEC3,SO.map((t=>0))),this.addParam(Es.FLOAT,FO.INPUT_NAME_W,0)}setLines(t){const e=[],n=FO.INPUT_NAME_VEC3,i=FO.INPUT_NAME_W,r=FO.OUTPUT_NAME_VEC4,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec4 ${a} = vec4(${s}.xyz, ${o})`),t.addBodyLines(this,e)}}FO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",FO.INPUT_NAME_W=\\\\\\\"w\\\\\\\",FO.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const DO=new class extends aa{};class kO extends df{constructor(){super(...arguments),this.paramsConfig=DO}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${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 BO extends kO{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Do.FLOAT]}}class zO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}}class UO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t]}}class GO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t]}}class VO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t,t]}}function HO(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||\\\\\\\"in\\\\\\\";return class extends BO{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 r}_gl_output_name(t){return i}gl_method_name(){return n}}}class jO extends(HO(\\\\\\\"abs\\\\\\\")){}class WO extends(HO(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class qO extends(HO(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class XO extends(HO(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class YO extends(HO(\\\\\\\"ceil\\\\\\\")){}class $O extends(HO(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class JO extends(HO(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class ZO extends(HO(\\\\\\\"exp\\\\\\\")){}class QO extends(HO(\\\\\\\"exp2\\\\\\\")){}class KO extends(HO(\\\\\\\"floor\\\\\\\")){}class tR extends(HO(\\\\\\\"fract\\\\\\\")){}class eR extends(HO(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class nR extends(HO(\\\\\\\"log\\\\\\\")){}class iR extends(HO(\\\\\\\"log2\\\\\\\")){}class rR extends(HO(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class sR extends(HO(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class oR extends(HO(\\\\\\\"sign\\\\\\\")){}class aR extends(HO(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class lR extends(HO(\\\\\\\"sqrt\\\\\\\")){}class cR extends(HO(\\\\\\\"tan\\\\\\\")){}function uR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],s=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends zO{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 r[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():s}const e=t||s||Do.FLOAT;return[e,e]}}}class hR extends(uR(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class dR extends(uR(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class pR extends(uR(\\\\\\\"max\\\\\\\")){}class _R extends(uR(\\\\\\\"min\\\\\\\")){}class mR extends(uR(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Do.FLOAT;return[t,t]}}class fR extends(uR(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class gR extends(uR(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Do.VEC3})){}class vR extends(uR(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function yR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],s=e.default||{},o=e.out_type||Do.FLOAT,a=e.functions||[];return class extends UO{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 r[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return s[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class xR extends(yR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class bR extends(yR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class wR extends(yR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function TR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.operation,s=e.allowed_in_types;return class extends zO{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 uf.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 r}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&s&&!s.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Do.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,r=[];for(let t=0;t{const n=t.name();return uf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function NR(t,e){return class extends CR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class LR extends(NR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class OR extends(NR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var RR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(RR||(RR={}));var PR,IR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(PR||(PR={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(IR||(IR={}));const FR=[PR.POSITION,PR.VELOCITY,PR.MASS,PR.FORCE],DR=[IR.POSITION,IR.VELOCITY],kR={[PR.POSITION]:[0,0,0],[PR.VELOCITY]:[0,0,0],[PR.MASS]:1,[PR.FORCE]:[0,-9.8,0]};const BR=new class extends aa{};class zR extends df{constructor(){super(...arguments),this.paramsConfig=BR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(IR.POSITION,Do.VEC3),new Vo(IR.VELOCITY,Do.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()||Do.VEC3;return[t,t,Do.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 DR[t]}paramDefaultValue(t){return kR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Do.FLOAT,RR.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 r=uf.any(this.variableForInput(PR.POSITION)),s=uf.any(this.variableForInput(PR.VELOCITY)),o=uf.float(this.variableForInput(PR.MASS)),a=uf.any(this.variableForInput(PR.FORCE)),l=this.glVarName(IR.POSITION),c=this.glVarName(IR.VELOCITY),u=`${e} ${c} = compute_velocity_from_acceleration(${[s,a,o,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,h=`${e} ${l} = compute_position_from_velocity(${[r,c,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[u,h])}}var UR,GR=\\\\\\\"\\\\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\\\\\\\"}(UR||(UR={}));const VR=[UR.DIR,UR.UP],HR={[UR.DIR]:[0,0,1],[UR.UP]:[0,1,0]};class jR extends zO{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>VR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return HR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var WR;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(WR||(WR={}));const qR=[WR.LINEAR,WR.GAMMA,WR.SRGB,WR.RGBE,WR.RGBM,WR.RGBD,WR.LogLuv];const XR=new class extends aa{constructor(){super(...arguments),this.color=oa.VECTOR4([1,1,1,1]),this.from=oa.INTEGER(qR.indexOf(WR.LINEAR),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.to=oa.INTEGER(qR.indexOf(WR.GAMMA),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=oa.FLOAT(2.2)}};class YR extends df{constructor(){super(...arguments),this.paramsConfig=XR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(YR.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=qR[this.pv.from],n=qR[this.pv.to],i=this.glVarName(YR.OUTPUT_NAME),r=uf.any(this.variableForInput(YR.INPUT_NAME)),s=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(r),e==WR.GAMMA||n==WR.GAMMA){const t=uf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}s.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else s.push(`vec4 ${i} = ${r}`);t.addBodyLines(this,s)}}var $R,JR;YR.INPUT_NAME=\\\\\\\"color\\\\\\\",YR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",YR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}($R||($R={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(JR||(JR={}));const ZR=[$R.EQUAL,$R.LESS_THAN,$R.GREATER_THAN,$R.LESS_THAN_OR_EQUAL,$R.GREATER_THAN_OR_EQUAL,$R.NOT_EQUAL],QR=[JR.EQUAL,JR.LESS_THAN,JR.GREATER_THAN,JR.LESS_THAN_OR_EQUAL,JR.GREATER_THAN_OR_EQUAL,JR.NOT_EQUAL],KR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const tP=new class extends aa{constructor(){super(...arguments),this.test=oa.INTEGER(0,{menu:{entries:ZR.map(((t,e)=>({name:`${QR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}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((()=>[Do.BOOL]))}set_test_name(t){this.p.test.set(ZR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=QR[this.pv.test],r=uf.any(this.variableForInput(this._gl_input_name(0))),s=uf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Go[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=oa.BOOLEAN(0,iP(Do.BOOL)),this.int=oa.INTEGER(0,iP(Do.INT)),this.float=oa.FLOAT(0,iP(Do.FLOAT)),this.vec2=oa.VECTOR2([0,0],iP(Do.VEC2)),this.vec3=oa.VECTOR3([0,0,0],iP(Do.VEC3)),this.vec4=oa.VECTOR4([0,0,0,0],iP(Do.VEC4))}};class sP extends df{constructor(){super(...arguments),this.paramsConfig=rP,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>sP.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=uf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=uf.integer(e.value));const r=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[r])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=ko[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([[Do.BOOL,this.p.bool],[Do.INT,this.p.int],[Do.FLOAT,this.p.float],[Do.VEC2,this.p.vec2],[Do.VEC3,this.p.vec3],[Do.VEC4,this.p.vec4]]);const t=ko[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(sP.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(ko.indexOf(t))}}sP.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const oP=\\\\\\\"cross\\\\\\\";const aP=new class extends aa{constructor(){super(...arguments),this.x=oa.VECTOR3([0,0,1]),this.y=oa.VECTOR3([0,1,0])}};class lP extends df{constructor(){super(...arguments),this.paramsConfig=aP}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(oP,Do.VEC3)])}setLines(t){const e=uf.float(this.variableForInputParam(this.p.x)),n=uf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(oP)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class cP extends(yR(\\\\\\\"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 uP=\\\\\\\"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 hP=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR2([0,0]),this.center=oa.VECTOR2([0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class dP extends df{constructor(){super(...arguments),this.paramsConfig=hP}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}var pP=\\\\\\\"\\\\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 _P=[\\\\\\\"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\\\\\\\"],mP={\\\\\\\"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\\\\\\\":pP,\\\\\\\"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\\\\\\\"},fP={\\\\\\\"bounce-in\\\\\\\":[pP],\\\\\\\"bounce-in-out\\\\\\\":[pP]},gP={\\\\\\\"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\\\\\\\"},vP=_P.indexOf(\\\\\\\"sine-in-out\\\\\\\");const yP=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(vP,{menu:{entries:_P.map(((t,e)=>({name:t,value:e})))}}),this.input=oa.FLOAT(0)}};class xP extends df{constructor(){super(...arguments),this.paramsConfig=yP}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.FLOAT)])}setLines(t){const e=_P[this.pv.type],n=gP[e];let i=[new Tf(this,mP[e])];const r=(fP[e]||[]).map((t=>new Tf(this,t)));r&&(i=r.concat(i));const s=uf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${s})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var bP=\\\\\\\"//\\\\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 wP={srcMin:0,srcMax:1,destMin:0,destMax:1};class TP extends VO{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return wP[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const AP={srcMin:0,srcMax:1};class EP extends UO{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return AP[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const MP={destMin:0,destMax:1};class SP extends UO{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return MP[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const CP={center:.5,variance:.5};class NP extends UO{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return CP[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const LP=\\\\\\\"color\\\\\\\";const OP=new class extends aa{constructor(){super(...arguments),this.mvPosition=oa.VECTOR4([0,0,0,0]),this.baseColor=oa.COLOR([0,0,0]),this.fogColor=oa.COLOR([1,1,1]),this.near=oa.FLOAT(0),this.far=oa.FLOAT(0)}};class RP extends df{constructor(){super(...arguments),this.paramsConfig=OP}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(LP,Do.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Ef(this,Do.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const r=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}\\\\\\\"),s=uf.vector4(this.variableForInputParam(this.p.mvPosition)),o=uf.vector3(this.variableForInputParam(this.p.baseColor)),a=uf.vector3(this.variableForInputParam(this.p.fogColor)),l=uf.vector3(this.variableForInputParam(this.p.near)),c=uf.vector3(this.variableForInputParam(this.p.far)),u=`vec3 ${this.glVarName(LP)} = compute_fog(${[s,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,r]),t.addBodyLines(this,[u])}}}const PP=new class extends aa{};class IP extends df{constructor(){super(...arguments),this.paramsConfig=PP}static type(){return er.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${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class FP extends df{constructor(){super(...arguments),this._children_controller_context=Ki.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(IP.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=0;r=0){const e=t.dest_connection_point(),i=uf.any(this.variableForInput(e.name())),r=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(r)}const s=this.pv.start,o=this.pv.max,a=this.pv.step,l=uf.float(s),c=uf.float(o),u=uf.float(a),h=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${h} = ${l}; ${h} < ${c}; ${h}+= ${u}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(BP.START_INDEX)} = ${h}`;if(n.push(p),r)for(let t of r)if(t&&t.input_index>=0){const i=t.dest_connection_point(),r=this.glVarName(i.name()),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}setLines(t){}}const VP=new class extends aa{};class HP extends df{constructor(){super(...arguments),this.paramsConfig=VP}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 jP=new class extends aa{constructor(){super(...arguments),this.hsluv=oa.VECTOR3([1,1,1])}};class WP extends df{constructor(){super(...arguments),this.paramsConfig=jP}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.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=uf.vector3(this.variableForInputParam(this.p.hsluv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const qP=new class extends aa{constructor(){super(...arguments),this.hsv=oa.VECTOR3([1,1,1])}};class XP extends df{constructor(){super(...arguments),this.paramsConfig=qP}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.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=uf.vector3(this.variableForInputParam(this.p.hsv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const YP=\\\\\\\"condition\\\\\\\";const $P=new class extends aa{};class JP extends kP{constructor(){super(...arguments),this.paramsConfig=$P}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=[Do.BOOL],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=1;r[\\\\\\\"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()||Do.FLOAT;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const pI=\\\\\\\"mvMult\\\\\\\";const _I=new class extends aa{constructor(){super(...arguments),this.vector=oa.VECTOR3([0,0,0])}};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(pI,Do.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=uf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(pI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const fI={mult:1};var gI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(gI||(gI={}));class vI extends GO{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[gI.VALUE,gI.PRE_ADD,gI.MULT,gI.POST_ADD][t]}paramDefaultValue(t){return fI[t]}setLines(t){const e=uf.any(this.variableForInput(gI.VALUE)),n=uf.any(this.variableForInput(gI.PRE_ADD)),i=uf.any(this.variableForInput(gI.MULT)),r=uf.any(this.variableForInput(gI.POST_ADD)),s=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${s} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${r}`;t.addBodyLines(this,[a])}}class yI extends BO{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var xI;!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\\\\\\\"}(xI||(xI={}));const bI=[xI.CLASSIC_PERLIN_2D,xI.CLASSIC_PERLIN_3D,xI.CLASSIC_PERLIN_4D,xI.NOISE_2D,xI.NOISE_3D,xI.NOISE_4D],wI={[xI.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',[xI.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',[xI.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',[xI.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\\\\\\\",[xI.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\\\\\\\",[xI.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\\\\\\\"},TI={[xI.CLASSIC_PERLIN_2D]:Do.VEC2,[xI.CLASSIC_PERLIN_3D]:Do.VEC3,[xI.CLASSIC_PERLIN_4D]:Do.VEC4,[xI.NOISE_2D]:Do.VEC2,[xI.NOISE_3D]:Do.VEC3,[xI.NOISE_4D]:Do.VEC4},AI={[xI.CLASSIC_PERLIN_2D]:Do.FLOAT,[xI.CLASSIC_PERLIN_3D]:Do.FLOAT,[xI.CLASSIC_PERLIN_4D]:Do.FLOAT,[xI.NOISE_2D]:Do.FLOAT,[xI.NOISE_3D]:Do.FLOAT,[xI.NOISE_4D]:Do.FLOAT},EI={[xI.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[xI.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var MI;!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\\\\\\\"}(MI||(MI={}));const SI=[MI.NoChange,MI.Float,MI.Vec2,MI.Vec3,MI.Vec4],CI={[MI.NoChange]:\\\\\\\"Same as noise\\\\\\\",[MI.Float]:\\\\\\\"Float\\\\\\\",[MI.Vec2]:\\\\\\\"Vec2\\\\\\\",[MI.Vec3]:\\\\\\\"Vec3\\\\\\\",[MI.Vec4]:\\\\\\\"Vec4\\\\\\\"},NI={[MI.NoChange]:Do.FLOAT,[MI.Float]:Do.FLOAT,[MI.Vec2]:Do.VEC2,[MI.Vec3]:Do.VEC3,[MI.Vec4]:Do.VEC4},LI=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],OI=\\\\\\\"noise\\\\\\\",RI=bI.indexOf(xI.NOISE_3D),PI=MI.NoChange,II={amp:1,freq:1};var FI;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(FI||(FI={}));const DI=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(RI,{menu:{entries:bI.map(((t,e)=>({name:`${t} (output: ${AI[t]})`,value:e})))}}),this.outputType=oa.INTEGER(PI,{menu:{entries:SI.map((t=>{const e=SI[t];return{name:CI[e],value:e}}))}}),this.octaves=oa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class kI extends df{constructor(){super(...arguments),this.paramsConfig=DI}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 Vo(OI,Do.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((()=>OI))}_gl_input_name(t){return[FI.AMP,FI.POSITION,FI.FREQ,FI.OFFSET][t]}paramDefaultValue(t){return II[t]}_expected_input_types(){const t=bI[this.pv.type],e=this._expected_output_types()[0],n=TI[t];return[e,n,n,n]}_expected_output_types(){const t=bI[this.pv.type],e=SI[this.pv.outputType];return e==MI.NoChange?[TI[t]]:[NI[e]]}setLines(t){const e=[],n=[],i=bI[this.pv.type],r=wI[i],s=AI[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,r)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==s){const t=this.single_noise_line();n.push(t)}else{const t=Go[o],e=[],r=this.glVarName(\\\\\\\"noise\\\\\\\");for(let s=0;suf.float(1e3*s))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const s=`vec${t} ${r} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(s)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=bI[this.pv.type];return`fbm_${EI[t]}_${this.name()}`}fbm_function(){const t=bI[this.pv.type],e=EI[t],n=TI[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 < ${uf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${uf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${uf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),r=uf.any(this.variableForInput(FI.AMP)),s=uf.any(this.variableForInput(FI.POSITION)),o=uf.any(this.variableForInput(FI.FREQ));let a=uf.any(this.variableForInput(FI.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${s}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(OI),u=`${r}*${i}(${l})`;if(e)return`float ${c}${t} = (${u}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${u}`}}class BI extends BO{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const zI=new class extends aa{};class UI extends df{constructor(){super(...arguments),this.paramsConfig=zI}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 GI{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const VI=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(ko.indexOf(Do.FLOAT),{menu:{entries:ko.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:ko.indexOf(Do.VEC3)}})}};class HI extends df{constructor(){super(...arguments),this.paramsConfig=VI,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((()=>[ko[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=ko[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=ko[this.pv.type],e=Uo[t];let n=Bo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Es.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=ko.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class jI extends kO{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()||Do.VEC3;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const WI=\\\\\\\"SSSModel\\\\\\\";const qI=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.thickness=oa.FLOAT(.1),this.power=oa.FLOAT(2),this.scale=oa.FLOAT(16),this.distortion=oa.FLOAT(.1),this.ambient=oa.FLOAT(.4),this.attenuation=oa.FLOAT(.8)}};class XI extends df{constructor(){super(...arguments),this.paramsConfig=qI}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(WI,Do.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(WI);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()} = ${uf.vector3(this.variableForInputParam(e))};`}}class YI extends BO{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((()=>[Do.VEC4,Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var $I;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}($I||($I={}));const JI=[$I.AXIS,$I.ANGLE],ZI={[$I.AXIS]:[0,0,1],[$I.ANGLE]:0};class QI extends zO{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>JI[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return ZI[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class KI extends BO{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((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class tF extends BO{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((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}const eF=\\\\\\\"val\\\\\\\";const nF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=oa.FLOAT(0)}};class iF extends df{constructor(){super(...arguments),this.paramsConfig=nF}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 Vo(eF,Do.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Do.FLOAT,n=this._uniform_name(),i=this.glVarName(eF),r=new Af(this,Do.SAMPLER_2D,n);t.addDefinitions(this,[r]);const s=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${s}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.RAMP,this.pv.name,xo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(eF)}}const rF=\\\\\\\"rand\\\\\\\";const sF=new class extends aa{constructor(){super(...arguments),this.seed=oa.VECTOR2([1,1])}};class oF extends df{constructor(){super(...arguments),this.paramsConfig=sF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(rF,Do.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=uf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(rF)} = rand(${n})`;t.addBodyLines(this,[i])}}const aF=new class extends aa{constructor(){super(...arguments),this.rgb=oa.VECTOR3([1,1,1])}};class lF extends df{constructor(){super(...arguments),this.paramsConfig=aF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"hsv\\\\\\\",Do.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=uf.vector3(this.variableForInputParam(this.p.rgb)),r=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${r} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var cF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(cF||(cF={}));const uF=[cF.AXIS,cF.QUAT],hF={[cF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[cF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},dF={[cF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[cF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},pF={[cF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[cF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},_F={[cF.AXIS]:[Do.VEC3,Do.VEC3,Do.FLOAT],[cF.QUAT]:[Do.VEC3,Do.VEC4]},mF={vector:[0,0,1],axis:[0,1,0]};const fF=new class extends aa{constructor(){super(...arguments),this.signature=oa.INTEGER(cF.AXIS,{menu:{entries:uF.map(((t,e)=>({name:hF[t],value:e})))}})}};class gF extends df{constructor(){super(...arguments),this.paramsConfig=fF}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=uF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=uF[this.pv.signature];return dF[e][t]}paramDefaultValue(t){return mF[t]}gl_method_name(){const t=uF[this.pv.signature];return pF[t]}_expected_input_types(){const t=uF[this.pv.signature];return _F[t]}_expected_output_types(){return[Do.VEC3]}gl_function_definitions(){return[new Tf(this,GR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.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 vF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class yF extends BO{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=uf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],r=this.glVarName(i.name()),s=[];if(1==Go[i.type()])s.push(`${i.type()} ${r} = ${this._simple_line(n)}`);else{const t=vF.map((t=>this._simple_line(`${n}.${t}`)));s.push(`${i.type()} ${r} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,s)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const xF=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR3([0,0,0]),this.center=oa.VECTOR3([0,0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}const wF=new class extends aa{};class TF extends df{constructor(){super(...arguments),this.paramsConfig=wF}static type(){return er.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${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 AF=new class extends aa{};class EF extends df{constructor(){super(...arguments),this.paramsConfig=AF}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?EF.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Do.FLOAT,e=this.io.connections.inputConnections(),n=e?rs.clamp(e.length,2,16):2,i=[Do.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(SF.OUTPUT_NAME),i=this._uniform_name(),r=new Af(this,Do.SAMPLER_2D,i),s=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[r]),t.addBodyLines(this,[s])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.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 CF;SF.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(CF||(CF={}));const NF=[CF.POSITION,CF.DIR_VEC];const LF=new class extends aa{constructor(){super(...arguments),this.vec=oa.VECTOR3([0,0,0]),this.interpretation=oa.INTEGER(0,{menu:{entries:NF.map(((t,e)=>({name:t,value:e})))}})}};class OF extends df{constructor(){super(...arguments),this.paramsConfig=LF}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=uf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(NF[this.pv.interpretation]){case CF.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case CF.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var RF;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(RF||(RF={}));const PF=[RF.CONDITION,RF.IF_TRUE,RF.IF_FALSE];class IF extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return PF[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Do.FLOAT;return[Do.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=uf.bool(this.variableForInput(RF.CONDITION)),r=uf.any(this.variableForInput(RF.IF_TRUE)),s=uf.any(this.variableForInput(RF.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${r}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${s}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const FF=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const DF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:FF.map(((t,e)=>({name:t,value:e})))}})}};class kF extends df{constructor(){super(...arguments),this.paramsConfig=DF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[FF[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return kF.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Ef(this,this.gl_type(),e),i=this.glVarName(kF.OUTPUT_NAME),r=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[r])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(FF.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}kF.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const BF={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class zF extends(yR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t,t]}_expected_output_types(){return[Do.VEC4]}paramDefaultValue(t){return BF[t]}}const UF={start:[0,0,1],end:[1,0,0]};class GF extends(uR(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t]}_expected_output_types(){return[Do.FLOAT]}paramDefaultValue(t){return UF[t]}}const VF={only:[`${JP.context()}/${JP.type()}`,`${kP.context()}/${kP.type()}`,`${GP.context()}/${GP.type()}`]};class HF extends ia{static context(){return Ki.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${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 aa{};const jF=[Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4];const WF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:jF.map(((t,e)=>({name:t,value:e})))}})}};class qF extends HF{constructor(){super(...arguments),this.paramsConfig=WF,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((()=>[jF[this.pv.type]]))}get input_name(){return qF.INPUT_NAME}get output_name(){return qF.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(jF.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(qF.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(qF.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())}}qF.INPUT_NAME=\\\\\\\"export\\\\\\\",qF.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const XF=new class extends aa{};class YF extends HF{constructor(){super(...arguments),this.paramsConfig=XF}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 $F=new class extends aa{};class JF extends HF{constructor(){super(...arguments),this.paramsConfig=$F}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 ZF{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(),r=t.get(i);r?r.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 ${r.data_type} from node '${r.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 QF;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(QF||(QF={}));class KF{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 ZF}}class tD extends KF{constructor(t,e,n){super(QF.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class eD 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 Es.BOOLEAN:case Es.BUTTON:return{value:0};case Es.COLOR:return{value:new D.a(0,0,0)};case Es.FLOAT:case Es.FOLDER:case Es.INTEGER:case Es.OPERATOR_PATH:case Es.NODE_PATH:case Es.PARAM_PATH:return{value:0};case Es.RAMP:case Es.STRING:return{value:null};case Es.VECTOR2:return{value:new d.a(0,0)};case Es.VECTOR3:return{value:new p.a(0,0,0)};case Es.VECTOR4:return{value:new _.a(0,0,0,0)}}ar.unreachable(t)}}const nD=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(jo.indexOf(Ho.FLOAT),{menu:{entries:jo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:jo.indexOf(Ho.VEC3)}})}};class iD extends HF{constructor(){super(...arguments),this.paramsConfig=nD,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((()=>[jo[this.pv.type]]))}setLines(t){const e=[],n=jo[this.pv.type],i=this.uniform_name();e.push(new tD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=jo[this.pv.type],e=Xo[t];let n=Wo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new eD(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new eD(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=jo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class rD extends ia{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return Ki.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 sD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const oD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},aD=Object.keys(oD);function lD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=oa.BOOLEAN(0),this.front=oa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=oa.BOOLEAN(0),this.shadowDoubleSided=oa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=oa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=oa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthWrite=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthTest=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.premultipliedAlpha=oa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=oa.INTEGER(w.xb,{menu:{entries:aD.map((t=>({name:t,value:oD[t]})))}}),this.dithering=oa.BOOLEAN(0),this.polygonOffset=oa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}lD(aa);class cD extends sD{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 r=t.customMaterials;if(r){const t=Object.keys(r);for(let n of t){const t=r[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class uD extends(lD(aa)){constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.lineWidth=oa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const hD=new uD;class dD extends rD{constructor(){super(...arguments),this.paramsConfig=hD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new wr.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 pD(t){return class extends t{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}pD(aa);class _D extends sD{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 mD 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=[],r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]);const s=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});s||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:s||{},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),r=this._material.uniforms[n],s=Object.keys(this._material.customMaterials);let o;for(let t of s){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(r||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(r&&$f.callback(i,r),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(ai.playerMode())return this._material}}function fD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=oa.BOOLEAN(0,{callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}})}}}fD(aa);class gD extends rD{constructor(){super(...arguments),this._children_controller_context=Ki.GL,this.persisted_config=new mD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(Ki.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${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 vD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}vD(aa);class yD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function xD(t){return class extends t{constructor(){super(...arguments),this.default=oa.FOLDER(null)}}}function bD(t){return class extends t{constructor(){super(...arguments),this.advanced=oa.FOLDER(null)}}}class wD extends(vD(lD(fD(bD(pD(xD(aa))))))){constructor(){super(...arguments),this.linewidth=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const TD=new wD;class AD extends gD{constructor(){super(...arguments),this.paramsConfig=TD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_LINE}_create_assembler_controller(){return ai.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),yD.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function ED(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.useVertexColors=oa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}O.a;ED(aa);class MD extends sD{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 SD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}SD(aa);class CD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function ND(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function LD(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:Ki.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class OD extends sD{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 r=t,s=e;await this._update_texture_on_uniforms(r,s,n,i)}if(this._update_options.directParams){const r=t,s=e;await this._update_texture_on_material(r,s,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 r=null!=e[n]&&null!=e[n].value;let s=!1;if(r){e[n].value.uuid!=i.uuid&&(s=!0)}if(!r||s){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let e of t){const t=r[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 ai.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 r=null!=e[n];let s=!1;if(r){e[n].uuid!=i.uuid&&(s=!0)}r&&!s||(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,r,s,o){i.isDirty()&&await i.compute();if(i.value){r.isDirty()&&await r.compute();const i=r.value.nodeWithContext(Ki.COP);if(i){const r=(await i.compute()).texture();if(r)return void s(t,e,n,r)}}o(t,e,n)}}function RD(t){return class extends t{constructor(){super(...arguments),this.useMap=oa.BOOLEAN(0,ND(PD)),this.map=oa.NODE_PATH(gi.EMPTY,LD(PD,\\\\\\\"useMap\\\\\\\"))}}}O.a;RD(aa);class PD extends OD{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 ID(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(FD)}),this.alphaMap=oa.NODE_PATH(gi.EMPTY,LD(FD,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;ID(aa);class FD extends OD{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 DD(t){return class extends t{constructor(){super(...arguments),this.useAOMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(kD)}),this.aoMap=oa.NODE_PATH(gi.EMPTY,LD(kD,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;DD(aa);class kD extends OD{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 BD;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(BD||(BD={}));const zD=[BD.MULT,BD.ADD,BD.MIX],UD={[BD.MULT]:w.nb,[BD.ADD]:w.c,[BD.MIX]:w.lb};function GD(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,ND(VD)),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(VD,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=oa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:zD.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}GD(aa);class VD extends OD{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=UD[zD[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 HD(t){return class extends t{constructor(){super(...arguments),this.useLightMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(jD)}),this.lightMap=oa.NODE_PATH(gi.EMPTY,LD(jD,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=oa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;HD(aa);class jD extends OD{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 WD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(WD||(WD={}));const qD=[WD.ROUND,WD.BUTT,WD.SQUARE];var XD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(XD||(XD={}));const YD=[XD.ROUND,XD.BEVEL,XD.MITER];function $D(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=oa.INTEGER(0,{menu:{entries:qD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=oa.INTEGER(0,{menu:{entries:YD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;$D(aa);class JD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=qD[n.wireframeLinecap],e.wireframeLinejoin=YD[n.wireframeLinejoin],e.needsUpdate=!0}}function ZD(t){return class extends t{constructor(){super(...arguments),this.textures=oa.FOLDER(null)}}}const QD={directParams:!0};class KD extends(SD($D(lD(bD(HD(GD(DD(ID(RD(ZD(ED(xD(aa))))))))))))){}const tk=new KD;class ek extends rD{constructor(){super(...arguments),this.paramsConfig=tk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,QD),aoMap:new kD(this,QD),envMap:new VD(this,QD),lightMap:new jD(this,QD),map:new PD(this,QD)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new at.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();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function nk(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0)}}}nk(aa);class ik extends sD{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 rk={uniforms:!0};class sk extends(vD(nk(lD(fD(bD(GD(DD(ID(RD(ZD(pD(xD(aa))))))))))))){}const ok=new sk;class ak extends gD{constructor(){super(...arguments),this.paramsConfig=ok,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,rk),aoMap:new kD(this,rk),envMap:new VD(this,rk),map:new PD(this,rk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_BASIC}_create_assembler_controller(){return ai.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),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function lk(t){return class extends t{constructor(){super(...arguments),this.emissive=oa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=oa.BOOLEAN(0,ND(ck)),this.emissiveMap=oa.NODE_PATH(gi.EMPTY,LD(ck,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=oa.FLOAT(1)}}}O.a;lk(aa);class ck extends OD{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 uk={directParams:!0};class hk extends(SD($D(lD(bD(HD(GD(lk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))){}const dk=new hk;class pk extends rD{constructor(){super(...arguments),this.paramsConfig=dk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,uk),aoMap:new kD(this,uk),emissiveMap:new ck(this,uk),envMap:new VD(this,uk),lightMap:new jD(this,uk),map:new PD(this,uk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new br.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();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function _k(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=oa.BOOLEAN(0,{callback:t=>{mk.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=oa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=oa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}_k(aa);class mk extends sD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${uf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${uf.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=B;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=>mk.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 fk={uniforms:!0};class gk extends(_k(vD(nk(lD(fD(bD(HD(GD(lk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))){}const vk=new gk;class yk extends gD{constructor(){super(...arguments),this.paramsConfig=vk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,fk),aoMap:new kD(this,fk),emissiveMap:new ck(this,fk),envMap:new VD(this,fk),lightMap:new jD(this,fk),map:new PD(this,fk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_LAMBERT}_create_assembler_controller(){return ai.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),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function xk(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(bk)}),this.bumpMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(bk,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;xk(aa);class bk extends OD{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 wk;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(wk||(wk={}));const Tk=[wk.TANGENT,wk.OBJECT],Ak={[wk.TANGENT]:w.Uc,[wk.OBJECT]:w.zb};function Ek(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Mk)}),this.normalMap=oa.NODE_PATH(gi.EMPTY,LD(Mk,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=oa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:Tk.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=oa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;Ek(aa);class Mk extends OD{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=Ak[Tk[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 Sk(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Ck)}),this.displacementMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;Sk(aa);class Ck extends OD{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 Nk(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=oa.BOOLEAN(0,ND(Lk)),this.matcapMap=oa.NODE_PATH(gi.EMPTY,LD(Lk,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;Nk(aa);class Lk extends OD{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 Ok={directParams:!0};class Rk extends(SD(lD(bD(Ek(Sk(xk(ID(RD(Nk(ZD(ED(xD(aa))))))))))))){}const Pk=new Rk;class Ik extends rD{constructor(){super(...arguments),this.paramsConfig=Pk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Ok),bumpMap:new bk(this,Ok),displacementMap:new Ck(this,Ok),map:new PD(this,Ok),matcap:new Lk(this,Ok),normalMap:new Mk(this,Ok)},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();MD.update(this),CD.update(this),this.setMaterial(this.material)}}function Fk(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=oa.BOOLEAN(0,ND(Dk)),this.specularMap=oa.NODE_PATH(gi.EMPTY,LD(Dk,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;Fk(aa);class Dk extends OD{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 kk={directParams:!0};class Bk extends(SD($D(lD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){constructor(){super(...arguments),this.flatShading=oa.BOOLEAN(0)}}const zk=new Bk;class Uk extends rD{constructor(){super(...arguments),this.paramsConfig=zk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,kk),aoMap:new kD(this,kk),bumpMap:new bk(this,kk),displacementMap:new Ck(this,kk),emissiveMap:new ck(this,kk),envMap:new VD(this,kk),lightMap:new jD(this,kk),map:new PD(this,kk),normalMap:new Mk(this,kk),specularMap:new Dk(this,kk)},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();MD.update(this),CD.update(this),JD.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Gk={uniforms:!0};class Vk extends(_k(vD(nk(lD(fD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const Hk=new Vk;class jk extends gD{constructor(){super(...arguments),this.paramsConfig=Hk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Gk),aoMap:new kD(this,Gk),bumpMap:new bk(this,Gk),displacementMap:new Ck(this,Gk),emissiveMap:new ck(this,Gk),envMap:new VD(this,Gk),lightMap:new jD(this,Gk),map:new PD(this,Gk),normalMap:new Mk(this,Gk),specularMap:new Dk(this,Gk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHONG}_create_assembler_controller(){return ai.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),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Wk(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(qk)}),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(qk,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Wk(aa);class qk extends OD{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 Xk(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.metalnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=oa.FLOAT(1),this.useRoughnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.roughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=oa.FLOAT(.5)}}}O.a;Xk(aa);class Yk extends OD{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 $k(t){return class extends t{constructor(){super(...arguments),this.clearcoat=oa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatNormalMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=oa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=oa.FLOAT(0),this.useClearCoatRoughnessMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatRoughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=oa.BOOLEAN(0),this.sheen=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=oa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.reflectivity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.transmission=oa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=oa.BOOLEAN(0),this.transmissionMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.thickness=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!1]}),this.useThicknessMap=oa.BOOLEAN(0),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=oa.FLOAT(0),this.attenuationColor=oa.COLOR([1,1,1])}}}$k(aa);class Jk extends OD{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;if(this._update_options.uniforms){const e=this.node.material;e.uniforms.clearcoat.value=t.clearcoat,e.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,e.uniforms.reflectivity.value=t.reflectivity,e.uniforms.transmission.value=t.transmission,e.uniforms.thickness.value=t.thickness,e.uniforms.attenuationDistance.value=t.attenuationDistance,e.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.uniforms.sheen.value=t.sheen,e.uniforms.sheenRoughness.value=t.sheenRoughness,e.uniforms.sheenTint.value=this._sheenColorClone):e.uniforms.sheen.value=0}if(this._update_options.directParams){const e=this.node.material;e.clearcoat=t.clearcoat,e.clearcoatNormalScale.copy(t.clearcoatNormalScale),e.clearcoatRoughness=t.clearcoatRoughness,e.reflectivity=t.reflectivity,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.sheen=t.sheen,e.sheenRoughness=t.sheenRoughness,e.sheenTint=this._sheenColorClone):e.sheen=0,e.transmission=t.transmission,e.thickness=t.thickness,e.attenuationDistance=t.attenuationDistance,e.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Zk={directParams:!0};class Qk extends(SD($D(lD(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))))){}const Kk=new Qk;class tB extends rD{constructor(){super(...arguments),this.paramsConfig=Kk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Zk),aoMap:new kD(this,Zk),bumpMap:new bk(this,Zk),displacementMap:new Ck(this,Zk),emissiveMap:new ck(this,Zk),envMap:new qk(this,Zk),lightMap:new jD(this,Zk),map:new PD(this,Zk),metalnessRoughnessMap:new Yk(this,Zk),normalMap:new Mk(this,Zk),physical:new Jk(this,Zk)},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();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const eB={uniforms:!0};class nB extends(function(t){return class extends(_k(vD(nk(lD(fD(t)))))){}}(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa))))))))))))))))){}const iB=new nB;class rB extends gD{constructor(){super(...arguments),this.paramsConfig=iB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,eB),aoMap:new kD(this,eB),bumpMap:new bk(this,eB),displacementMap:new Ck(this,eB),emissiveMap:new ck(this,eB),envMap:new qk(this,eB),lightMap:new jD(this,eB),map:new PD(this,eB),metalnessRoughnessMap:new Yk(this,eB),normalMap:new Mk(this,eB),physical:new Jk(this,eB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHYSICAL}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const sB={directParams:!0};class oB extends(SD($D(lD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){}const aB=new oB;class lB extends rD{constructor(){super(...arguments),this.paramsConfig=aB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,sB),aoMap:new kD(this,sB),bumpMap:new bk(this,sB),displacementMap:new Ck(this,sB),emissiveMap:new ck(this,sB),envMap:new qk(this,sB),lightMap:new jD(this,sB),map:new PD(this,sB),metalnessRoughnessMap:new Yk(this,sB),normalMap:new Mk(this,sB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new xr.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();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const cB={uniforms:!0};class uB extends(_k(vD(nk(lD(fD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const hB=new uB;class dB extends gD{constructor(){super(...arguments),this.paramsConfig=hB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,cB),aoMap:new kD(this,cB),bumpMap:new bk(this,cB),displacementMap:new Ck(this,cB),emissiveMap:new ck(this,cB),envMap:new qk(this,cB),lightMap:new jD(this,cB),map:new PD(this,cB),metalnessRoughnessMap:new Yk(this,cB),normalMap:new Mk(this,cB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_STANDARD}_create_assembler_controller(){return ai.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),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const pB=z.meshphong_frag.slice(0,z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),_B=z.meshphong_frag.slice(z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),mB={uniforms:I.merge([V.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\\\\\\\",z.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",pB,\\\\\\\"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;\\\\\\\",\\\\\\\"}\\\\\\\",_B.replace(\\\\\\\"#include \\\\\\\",(fB=z.lights_fragment_begin,gB=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",vB=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fB.split(gB).join(vB)))].join(\\\\\\\"\\\\n\\\\\\\")};var fB,gB,vB;function yB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function xB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const bB={uniforms:!0};class wB extends(SD(nk(lD(bD(ID(RD(ZD(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=oa.COLOR([1,1,1],{...yB(\\\\\\\"diffuse\\\\\\\")}),this.shininess=oa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{nodeSelection:{context:Ki.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=oa.COLOR([.5,.3,0],{...yB(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=oa.FLOAT(.1,{...xB(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=oa.FLOAT(.4,{...xB(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=oa.FLOAT(.8,{...xB(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=oa.FLOAT(2,{range:[0,10],...xB(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=oa.FLOAT(16,{range:[0,100],...xB(\\\\\\\"thicknessScale\\\\\\\")})}}}(xD(aa)))))))))){}const TB=new wB;class AB extends rD{constructor(){super(...arguments),this.paramsConfig=TB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,bB),map:new PD(this,bB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(mB.uniforms),e=new F({uniforms:t,vertexShader:mB.vertexShader,fragmentShader:mB.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();CD.update(this),ik.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(Ki.COP);n||(this.material.uniforms[e].value=null);const i=n,r=await i.compute();this.material.uniforms[e].value=r.texture()}}function EB(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=oa.BOOLEAN(0,ND(MB)),this.gradientMap=oa.NODE_PATH(gi.EMPTY,LD(MB,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;EB(aa);class MB extends OD{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 SB={directParams:!0};class CB extends(SD($D(lD(bD(Ek(HD(EB(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))){}const NB=new CB;class LB extends rD{constructor(){super(...arguments),this.paramsConfig=NB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,SB),aoMap:new kD(this,SB),bumpMap:new bk(this,SB),displacementMap:new Ck(this,SB),emissiveMap:new ck(this,SB),gradientMap:new MB(this,SB),lightMap:new jD(this,SB),map:new PD(this,SB),normalMap:new Mk(this,SB)},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();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const OB={directParams:!0};class RB extends(vD(lD(bD(ID(RD(ZD(ED(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1),this.sizeAttenuation=oa.BOOLEAN(1)}}}(xD(aa)))))))))){}const PB=new RB;class IB extends rD{constructor(){super(...arguments),this.paramsConfig=PB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,OB),map:new PD(this,OB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new yr.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();MD.update(this),yD.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class FB extends(vD(lD(fD(bD(pD(xD(aa))))))){}const DB=new FB;class kB extends gD{constructor(){super(...arguments),this.paramsConfig=DB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return Hn.GL_POINTS}_create_assembler_controller(){return ai.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),yD.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class BB extends(lD(ED(aa))){}const zB=new BB;class UB extends rD{constructor(){super(...arguments),this.paramsConfig=zB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new Bf({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();MD.update(this),this.setMaterial(this.material)}}class GB extends k.a{constructor(){const t=GB.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)}}GB.prototype.isSky=!0,GB.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 VB=new class extends aa{constructor(){super(...arguments),this.turbidity=oa.FLOAT(2,{range:[0,20]}),this.rayleigh=oa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=oa.FLOAT(.005),this.mieDirectional=oa.FLOAT(.8),this.inclination=oa.FLOAT(.5),this.azimuth=oa.FLOAT(.25),this.up=oa.VECTOR3([0,1,0])}};class HB extends rD{constructor(){super(...arguments),this.paramsConfig=VB}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new GB).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 jB=\\\\\\\"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}\\\\\\\",WB=\\\\\\\"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 qB={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)}};var XB=n(16);function YB(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.stepSize=oa.FLOAT(.01),this.density=oa.FLOAT(1),this.shadowDensity=oa.FLOAT(1),this.lightDir=oa.VECTOR3([-1,-1,-1])}}}YB(aa);class $B{constructor(t){this.node=t}static render_hook(t,e,n,i,r,s,o){if(o){this._object_bbox.setFromObject(o);const t=r;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}$B._object_bbox=new XB.a;class JB extends(YB(aa)){}const ZB=new JB;class QB extends rD{constructor(){super(...arguments),this.paramsConfig=ZB,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:jB,fragmentShader:WB,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(qB)});return fs.add_user_data_render_hook(t,$B.render_hook.bind($B)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class KB extends(fD(YB(aa))){}const tz=new KB;class ez extends gD{constructor(){super(...arguments),this.paramsConfig=tz,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return Hn.GL_VOLUME}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class nz extends ia{static context(){return Ki.MAT}cook(){this.cookController.endCook()}}class iz extends nz{}class rz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class az extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lz extends nz{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var uz=n(87);const hz=\\\\\\\"parent object\\\\\\\",dz=[hz,hz,hz,hz];var pz;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(pz||(pz={}));class _z extends ia{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return Ki.OBJ}static displayedInputNames(){return dz}_create_children_group(){const t=new In.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 Q.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 mz extends _z{constructor(){super(...arguments),this.flags=new Fi(this),this.renderOrder=pz.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 fz=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1)}};class gz extends mz{constructor(){super(...arguments),this.paramsConfig=fz}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new uz.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 vz extends nv.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}}vz.prototype.isRectAreaLight=!0;var yz,xz=n(60);class bz{static init(){const 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Float32Array(t),i=new Float32Array(e);G.LTC_FLOAT_1=new mo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_FLOAT_2=new mo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const r=new Uint16Array(t.length);t.forEach((function(t,e){r[e]=xz.a.toHalfFloat(t)}));const s=new Uint16Array(e.length);e.forEach((function(t,e){s[e]=xz.a.toHalfFloat(t)})),G.LTC_HALF_1=new mo.a(r,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_HALF_2=new mo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1)}}!function(t){t.OBJECTS=\\\\\\\"objects\\\\\\\",t.GEOMETRIES=\\\\\\\"geometries\\\\\\\"}(yz||(yz={}));const wz=[yz.GEOMETRIES,yz.OBJECTS];var Tz;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(Tz||(Tz={}));const Az=[Tz.XYZ,Tz.XZY,Tz.YXZ,Tz.YZX,Tz.ZXY,Tz.ZYX],Ez=Tz.XYZ;class Mz{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new au.a,this._translation_matrix_s=new 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set_params_from_object(t,e){t.position.toArray(this.set_params_from_object_position_array),t.rotation.toArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.multiplyScalar(180/Math.PI),this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_object_position_array),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_object_rotation_array)}))}translation_matrix(t){return this._translation_matrix.compose(t,this._translation_matrix_q,this._translation_matrix_s),this._translation_matrix}matrix(t,e,n,i,r){return this._matrix_euler.set(Object(Ln.e)(e.x),Object(Ln.e)(e.y),Object(Ln.e)(e.z),r),this._matrix_q.setFromEuler(this._matrix_euler),this._matrix_s.copy(n).multiplyScalar(i),this._matrix.compose(t,this._matrix_q,this._matrix_s),this._matrix}rotate_geometry(t,e,n){this._rotate_geometry_vec_dest.copy(n),this._rotate_geometry_vec_dest.normalize(),this._rotate_geometry_q.setFromUnitVectors(e,this._rotate_geometry_vec_dest),this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q),t.applyMatrix4(this._rotate_geometry_m)}static decompose_matrix(t){t.matrix.decompose(t.position,t.quaternion,t.scale)}}function Sz(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=oa.FOLDER(),this.keepPosWhenParenting=oa.BOOLEAN(0),this.rotationOrder=oa.INTEGER(Az.indexOf(Tz.XYZ),{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3([0,0,0]),this.r=oa.VECTOR3([0,0,0]),this.s=oa.VECTOR3([1,1,1]),this.scale=oa.FLOAT(1),this.matrixAutoUpdate=oa.BOOLEAN(n?1:0),this.updateTransformFromObject=oa.BUTTON(null,{callback:t=>{Nz.PARAM_CALLBACK_update_transform_from_object(t)}})}}}Mz.set_params_from_matrix_position=new p.a,Mz.set_params_from_matrix_quaternion=new au.a,Mz.set_params_from_matrix_scale=new p.a,Mz.set_params_from_matrix_euler=new Wv.a,Mz.set_params_from_matrix_rotation=new p.a,Mz.set_params_from_matrix_t=[0,0,0],Mz.set_params_from_matrix_r=[0,0,0],Mz.set_params_from_matrix_s=[0,0,0],Mz.set_params_from_object_position_array=[0,0,0],Mz.set_params_from_object_rotation_deg=new p.a,Mz.set_params_from_object_rotation_array=[0,0,0];Sz(aa);const Cz=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class Nz{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 Mz,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(Cz)||this.node.dirtyController.addPostDirtyHook(Cz,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,Az[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={}){Mz.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),Mz.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(),Mz.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 Lz{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&&Nz.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(){Lz.on_input_updated(this.node)}}Sz(aa);class Oz extends mz{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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t.matrixAutoUpdate=!1,bz.initialized||(bz.init(),bz.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 Gz=n(72);const Vz=new p.a,Hz=new tf.a;class jz extends Tr.a{constructor(t){const e=new S.a,n=new wr.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],s={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),u=new D.a(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===s[t]&&(s[t]=[]),s[t].push(i.length/3-1)}h(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),h(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),h(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),h(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),h(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),h(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),h(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",u),h(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",u),h(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",u),h(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",u),h(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",u),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(r,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=s,this.update()}update(){const t=this.geometry,e=this.pointMap;Hz.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),Wz(\\\\\\\"c\\\\\\\",e,t,Hz,0,0,-1),Wz(\\\\\\\"t\\\\\\\",e,t,Hz,0,0,1),Wz(\\\\\\\"n1\\\\\\\",e,t,Hz,-1,-1,-1),Wz(\\\\\\\"n2\\\\\\\",e,t,Hz,1,-1,-1),Wz(\\\\\\\"n3\\\\\\\",e,t,Hz,-1,1,-1),Wz(\\\\\\\"n4\\\\\\\",e,t,Hz,1,1,-1),Wz(\\\\\\\"f1\\\\\\\",e,t,Hz,-1,-1,1),Wz(\\\\\\\"f2\\\\\\\",e,t,Hz,1,-1,1),Wz(\\\\\\\"f3\\\\\\\",e,t,Hz,-1,1,1),Wz(\\\\\\\"f4\\\\\\\",e,t,Hz,1,1,1),Wz(\\\\\\\"u1\\\\\\\",e,t,Hz,.7,1.1,-1),Wz(\\\\\\\"u2\\\\\\\",e,t,Hz,-.7,1.1,-1),Wz(\\\\\\\"u3\\\\\\\",e,t,Hz,0,2,-1),Wz(\\\\\\\"cf1\\\\\\\",e,t,Hz,-1,0,1),Wz(\\\\\\\"cf2\\\\\\\",e,t,Hz,1,0,1),Wz(\\\\\\\"cf3\\\\\\\",e,t,Hz,0,-1,1),Wz(\\\\\\\"cf4\\\\\\\",e,t,Hz,0,1,1),Wz(\\\\\\\"cn1\\\\\\\",e,t,Hz,-1,0,-1),Wz(\\\\\\\"cn2\\\\\\\",e,t,Hz,1,0,-1),Wz(\\\\\\\"cn3\\\\\\\",e,t,Hz,0,-1,-1),Wz(\\\\\\\"cn4\\\\\\\",e,t,Hz,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function Wz(t,e,n,i,r,s,o){Vz.set(r,s,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&&(s[t+0]+=1),n<.2&&(s[t+2]+=1),i<.2&&(s[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(r.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(s,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new Kz(t.vertices,t.indices,t.radius,t.details)}}class tU extends Kz{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 tU(t.radius,t.detail)}}class eU extends Dz{constructor(){super(...arguments),this._geometry=new tU(1),this._quat=new au.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new k.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,r,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new pU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class _U extends pU{constructor(t=1,e=1,n=8,i=1,r=!1,s=0,o=2*Math.PI){super(0,t,e,n,i,r,s,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:s,thetaLength:o}}static fromJSON(t){return new _U(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class mU{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;dU.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 _U(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 k.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 fU extends(Sz(aa)){constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.angle=oa.FLOAT(45,{range:[0,180]}),this.penumbra=oa.FLOAT(.1),this.decay=oa.FLOAT(.1,{range:[0,1]}),this.distance=oa.FLOAT(100,{range:[0,100]}),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=oa.FOLDER(),this.castShadow=oa.BOOLEAN(1),this.shadowAutoUpdate=oa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=oa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=oa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=oa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=oa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=oa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=oa.FOLDER(),this.tvolumetric=oa.BOOLEAN(0),this.volAttenuation=oa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=oa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const gU=new fU;class vU extends Oz{constructor(){super(...arguments),this.paramsConfig=gU,this._helperController=new Rz(this,dU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new mU(this)}static type(){return Xz.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new hU.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 yU;const xU=function(){return void 0===yU&&(yU=new(window.AudioContext||window.webkitAudioContext)),yU},bU=new p.a,wU=new au.a,TU=new p.a,AU=new p.a;class EU extends Q.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=xU(),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(bU,wU,TU),AU.set(0,0,-1).applyQuaternion(wU),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(bU.x,t),e.positionY.linearRampToValueAtTime(bU.y,t),e.positionZ.linearRampToValueAtTime(bU.z,t),e.forwardX.linearRampToValueAtTime(AU.x,t),e.forwardY.linearRampToValueAtTime(AU.y,t),e.forwardZ.linearRampToValueAtTime(AU.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(bU.x,bU.y,bU.z),e.setOrientation(AU.x,AU.y,AU.z,n.x,n.y,n.z)}}class MU extends(Sz(aa)){}const SU=new MU;class CU extends _z{constructor(){super(...arguments),this.paramsConfig=SU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new EU;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class NU extends Tr.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 wr.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var LU;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(LU||(LU={}));const OU=[LU.TOGETHER,LU.SEPARATELY];const RU=new class extends aa{constructor(){super(...arguments),this.object0=oa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.object1=oa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.mode=oa.INTEGER(OU.indexOf(LU.TOGETHER),{menu:{entries:OU.map(((t,e)=>({name:t,value:e})))}}),this.blend=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class PU extends _z{constructor(){super(...arguments),this.paramsConfig=RU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._t0=new p.a,this._q0=new au.a,this._s0=new p.a,this._t1=new p.a,this._q1=new au.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new In.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(Ki.OBJ),e=this.p.object1.found_node_with_context(Ki.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=OU[this.pv.mode];switch(n){case LU.TOGETHER:return this._blend_together(t,e);case LU.SEPARATELY:return this._blend_separately(t,e)}ar.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 IU={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 FU={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}\\\\\\\"},DU=1/256e3;class kU{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(IU),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(FU),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new st.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new k.a(t)}applyBlur(t,e,n,i){const r=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*r*DU,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*r*DU,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var BU;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(BU||(BU={}));const zU=[BU.ON_RENDER,BU.MANUAL];class UU extends(Sz(aa)){constructor(){super(...arguments),this.shadow=oa.FOLDER(),this.dist=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=oa.VECTOR2([1,1]),this.shadowRes=oa.VECTOR2([256,256]),this.blur=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=oa.BOOLEAN(1),this.blur2=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=oa.FLOAT(1),this.opacity=oa.FLOAT(1),this.showHelper=oa.BOOLEAN(0),this.updateMode=oa.INTEGER(zU.indexOf(BU.ON_RENDER),{callback:t=>{HU.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:zU.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:zU.indexOf(BU.MANUAL)}}),this.scene=oa.FOLDER(),this.include=oa.STRING(\\\\\\\"\\\\\\\"),this.exclude=oa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const GU=new UU,VU=new d.a(256,256);class HU extends _z{constructor(){super(...arguments),this.paramsConfig=GU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._renderTarget=this._createRenderTarget(VU),this._coreRenderBlur=this._createCoreRenderBlur(VU),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new Nz(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 Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new kU(t)}createObject(){const t=new In.a;this._shadowGroup=new In.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 at.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new k.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 st.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new jz(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Mn,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,r=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=r,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=zU[this.pv.updateMode];switch(t){case BU.ON_RENDER:return this._addRenderHook();case BU.MANUAL:return this._removeRenderHook()}ar.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,r,s){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=ai.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 jU=\\\\\\\"display\\\\\\\";class WU{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 In.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(jU),i=this.node.params.boolean(jU),r=this.node.usedInScene(),s=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return r&&s&&(!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 qU extends(Sz(aa)){constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.renderOrder=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const XU=new qU;class YU extends _z{constructor(){super(...arguments),this.paramsConfig=XU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new In.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 $U extends(Sz(aa)){}const JU=new $U;class ZU extends _z{constructor(){super(...arguments),this.paramsConfig=JU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._helper=new NU(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new In.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 QU=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR3([0,0,0]),this.longitude=oa.FLOAT(0,{range:[0,360]}),this.latitude=oa.FLOAT(0,{range:[-180,180]}),this.depth=oa.FLOAT(1,{range:[0,10]})}},KU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",tG=new p.a(0,1,0),eG=new p.a(-1,0,0);class nG extends _z{constructor(){super(...arguments),this.paramsConfig=QU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(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 au.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(KU)||this.dirtyController.addPostDirtyHook(KU,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(tG,Object(Ln.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(eG,Object(Ln.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 iG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=hG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||Ml.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=sr.escapeLineBreaks(t)}}class rG{constructor(t){this._node=t}data(t={}){var e,n,i,r,s,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===(r=this._node.flags.display)||void 0===r?void 0:r.active())),this._node.flags.hasOptimize()&&(null===(s=this._node.flags.optimize)||void 0===s?void 0:s.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=hG.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=iG.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 r=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const s=r.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[s])||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=hG.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=hG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class sG{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 oG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class aG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class lG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class cG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class uG extends rG{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 hG{static dispatch_node(t){return t.polyNodeController?new uG(t):new rG(t)}static dispatch_param(t){return t instanceof no?new oG(t):t instanceof po?new aG(t):t instanceof bo?new lG(t):t instanceof xo?new cG(t):new sG(t)}}class dG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=ai.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 dG;if(this._objects){const e=[];for(let t of this._objects)e.push(vs.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 vs(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=ps.pointsCount(t.geometry)),{type:Nr(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 ps(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 ps(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+=ps.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=sr.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new dG;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=>sr.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 XB.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 Vr.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=dG.geometryFromObject(n);if(i){new ps(i).renameAttrib(t,e)}}));break;case Vr.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new vs(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=sr.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(sr.matchMask(t,i))n.push(t);else{t==Wr.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=Wr.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new In.a;return t.children.forEach((t=>{const n=vs.clone(t);e.add(n)})),e}}class pG extends Fl{static context(){return Ki.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new dG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Sr.MESH){const n=pG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return pG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Cr[e])(t,n=n||Vr.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){pG.createIndexIfNone(t)}static createIndexIfNone(t){hs.createIndexIfNone(t)}}var _G;!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\\\\\\\"}(_G||(_G={}));const mG=\\\\\\\"input geometry\\\\\\\",fG=[mG,mG,mG,mG];class gG extends ia{constructor(){super(...arguments),this.flags=new zi(this)}static context(){return Ki.SOP}static displayedInputNames(){return fG}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,_G.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],_G.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,_G.FROM_SET_OBJECTS)}setGeometry(t,e=Sr.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],_G.FROM_SET_GEOMETRY)}setGeometries(t,e=Sr.MESH){const n=[];let i;for(let r of t)i=this.createObject(r,e),n.push(i);this._setContainerObjects(n,_G.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new dG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return pG.createObject(t,e,n)}createObject(t,e,n){return gG.createObject(t,e,n)}static createIndexIfNone(t){pG.createIndexIfNone(t)}_createIndexIfNone(t){gG.createIndexIfNone(t)}}const vG=new class extends aa{};class yG extends gG{constructor(){super(...arguments),this.paramsConfig=vG}static type(){return er.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){this.setCoreGroup(t[0])}}class xG extends gG{constructor(){super(...arguments),this.childrenDisplayController=new wG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.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 bG={dependsOnDisplayNode:!0};class wG{constructor(t,e=bG){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(yG.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 Ai(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function TG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends xG{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new MG(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const AG=TG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.SOP,inputs:[0,4]});class EG extends AG{}class MG{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 zl(this.node);i.create_nodes(n,t);const r=this._definition.ui;r&&i.process_nodes_ui_data(n,r),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=hG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),r={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(r))}}static createNodeClass(t,e,n){switch(e){case Ki.SOP:return TG(t,n);case Ki.OBJ:return SG(t,n)}}}function SG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends _z{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this.polyNodeController=new MG(this,e)}static type(){return t}createObject(){const t=new In.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 CG=SG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.OBJ});class NG extends CG{}class LG extends Q.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 UG;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(UG||(UG={}));const GG=[UG.LINEAR,UG.INVERSE,UG.EXPONENTIAL];class VG extends(Sz(aa)){constructor(){super(...arguments),this.audio=oa.FOLDER(),this.listener=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=oa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Ls.AUDIO]}}),this.volume=oa.FLOAT(1),this.loop=oa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=oa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=oa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=oa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=oa.INTEGER(GG.indexOf(UG.LINEAR),{menu:{entries:GG.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=oa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=oa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0),this.play=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_pause(t)}})}}const HG=new VG;class jG extends _z{constructor(){super(...arguments),this.paramsConfig=HG,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new In.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(GG[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 DG(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(Ki.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 FG(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new zG(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 WG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(WG||(WG={}));const qG=[WG.ON_RENDER,WG.MANUAL];const XG=new class extends aa{constructor(){super(...arguments),this.object=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{YG.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=oa.INTEGER(0,{range:[0,100]}),this.updateMode=oa.INTEGER(qG.indexOf(WG.ON_RENDER),{callback:t=>{YG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:qG.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{YG.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:qG.indexOf(WG.MANUAL)}})}};class YG extends _z{constructor(){super(...arguments),this.paramsConfig=XG,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(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 k.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=qG[this.pv.updateMode];switch(t){case WG.ON_RENDER:return this._add_render_hook();case WG.MANUAL:return this._remove_render_hook()}ar.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,r,s){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()==Ki.OBJ&&t.type()==YU.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 $G extends(Ca(Ea(va(ma(ua(aa)))))){}const JG=new $G;class ZG extends _z{constructor(){super(...arguments),this.paramsConfig=JG,this.hierarchyController=new Lz(this),this.SceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.SceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new fr;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 QG{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._update_required=this._controls_node.update_required()}update_required(){return this._update_required}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 KG=\\\\\\\"controls\\\\\\\";class tV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(KG)?this.node.params.get(KG):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(pr.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 r=!1,s=this._applied_controls_by_element_id.get(e.id);if(s&&s.get(i)&&(r=!0),!r){s=new Map,this._applied_controls_by_element_id.set(e.id,s),s.set(i,n);const r=await n.apply_controls(this.node.object,t);if(!r)return;const o=new QG(this.node.graphNodeId(),n,r);return this.set_controls_events(r),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.dispose_controls_for_html_element_id(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=qV[this.node.pv.updateFromControlsMode];switch(e){case WV.ON_END:return this._set_controls_events_to_update_on_end(t);case WV.ALWAYS:return this._set_controls_events_to_update_always(t);case WV.NEVER:return this._reset(t)}ar.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 eV(t){return class extends t{constructor(){super(...arguments),this.layer=oa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class nV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const iV={callback:t=>{eH.PARAM_CALLBACK_reset_effects_composer(t)}};function rV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=oa.BOOLEAN(0),this.postProcessNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[tr.POST]},...iV})}}}class sV{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,r=this.node.p.postProcessNode.value.node();if(r){if(r.type()==tr.POST){const s=r,o=this.node.renderController.canvas_resolution(t);return s.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 oV extends ia{constructor(){super(...arguments),this.flags=new Oi(this)}static context(){return Ki.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var aV,lV,cV,uV;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(aV||(aV={})),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\\\\\\\"}(lV||(lV={})),(uV=cV||(cV={}))[uV.Linear=w.U]=\\\\\\\"Linear\\\\\\\",uV[uV.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",uV[uV.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",uV[uV.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",uV[uV.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",uV[uV.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",uV[uV.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",uV[uV.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const hV=[lV.Linear,lV.sRGB,lV.Gamma,lV.RGBE,lV.LogLuv,lV.RGBM7,lV.RGBM16,lV.RGBD],dV=[cV.Linear,cV.sRGB,cV.Gamma,cV.RGBE,cV.LogLuv,cV.RGBM7,cV.RGBM16,cV.RGBD],pV=cV.sRGB;var _V,mV,fV;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(_V||(_V={})),(fV=mV||(mV={}))[fV.No=w.vb]=\\\\\\\"No\\\\\\\",fV[fV.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",fV[fV.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",fV[fV.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",fV[fV.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const gV=[_V.No,_V.Linear,_V.Reinhard,_V.Cineon,_V.ACESFilmic],vV=[mV.No,mV.Linear,mV.Reinhard,mV.Cineon,mV.ACESFilmic],yV=mV.ACESFilmic,xV=gV.map(((t,e)=>({name:t,value:vV[e]})));var bV;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(bV||(bV={}));const wV=[bV.HIGH,bV.MEDIUM,bV.LOW];var TV;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(TV||(TV={}));const AV=[TV.HIGH,TV.LOW,TV.DEFAULT];var EV,MV,SV;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(EV||(EV={})),(SV=MV||(MV={}))[SV.Basic=w.k]=\\\\\\\"Basic\\\\\\\",SV[SV.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",SV[SV.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",SV[SV.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const CV=[EV.Basic,EV.PCF,EV.PCFSoft,EV.VSM],NV=[MV.Basic,MV.PCF,MV.PCFSoft,MV.VSM],LV=(w.k,w.Fb,w.Gb,w.gd,MV.PCFSoft),OV={alpha:!1,precision:bV.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:TV.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const RV=new class extends aa{constructor(){super(...arguments),this.tprecision=oa.BOOLEAN(0),this.precision=oa.INTEGER(wV.indexOf(bV.HIGH),{visibleIf:{tprecision:1},menu:{entries:wV.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=oa.BOOLEAN(0),this.powerPreference=oa.INTEGER(AV.indexOf(TV.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:AV.map(((t,e)=>({value:e,name:t})))}}),this.alpha=oa.BOOLEAN(1),this.premultipliedAlpha=oa.BOOLEAN(1),this.antialias=oa.BOOLEAN(1),this.stencil=oa.BOOLEAN(1),this.depth=oa.BOOLEAN(1),this.logarithmicDepthBuffer=oa.BOOLEAN(0),this.toneMapping=oa.INTEGER(yV,{menu:{entries:xV}}),this.toneMappingExposure=oa.FLOAT(1,{range:[0,2]}),this.outputEncoding=oa.INTEGER(pV,{menu:{entries:hV.map(((t,e)=>({name:t,value:dV[e]})))}}),this.physicallyCorrectLights=oa.BOOLEAN(1),this.sortObjects=oa.BOOLEAN(1),this.tpixelRatio=oa.BOOLEAN(0),this.pixelRatio=oa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=oa.BOOLEAN(1),this.shadowMapAutoUpdate=oa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=oa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=oa.INTEGER(LV,{visibleIf:{tshadowMap:1},menu:{entries:CV.map(((t,e)=>({name:t,value:NV[e]})))}})}};class PV extends oV{constructor(){super(...arguments),this.paramsConfig=RV,this._renderers_by_canvas_id={}}static type(){return aV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(OV);let r;for(r of i)n[r]=OV[r];if(this.pv.tprecision){const t=wV[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=AV[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 s=ai.renderersController.createWebGLRenderer(n);return ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),ai.renderersController.printDebugMessage({params:n})),this._update_renderer(s),this._renderers_by_canvas_id[t.id]=s,s}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:FV.defaultPixelRatio();ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),ai.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 IV(t){return class extends t{constructor(){super(...arguments),this.render=oa.FOLDER(),this.setScene=oa.BOOLEAN(0),this.scene=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]}}),this.setRenderer=oa.BOOLEAN(0),this.renderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:Ki.ROP,types:[PV.type()]}}),this.setCSSRenderer=oa.BOOLEAN(0),this.CSSRenderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:Ki.ROP,types:[aV.CSS2D,aV.CSS3D]}})}}}class FV{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(Ki.OBJ,ZG.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(Ki.ROP,aV.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(Ki.ROP,[aV.CSS2D,aV.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=ai.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=FV._createDefaultRenderer(t,n)),ai.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=ai.renderersController.createWebGLRenderer(n),r=this.defaultPixelRatio();return i.setPixelRatio(r),i.shadowMap.enabled=!0,i.shadowMap.type=LV,i.physicallyCorrectLights=!0,i.toneMapping=yV,i.toneMappingExposure=1,i.outputEncoding=pV,ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),ai.renderersController.printDebugMessage({params:n,pixelRatio:r})),i}delete_renderer(t){const e=this.renderer(t);e&&ai.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 DV{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(){this._config&&this._controls&&this._config.update_required()&&this._controls.update()}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 Ai(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.viewer.controlsController.create_controls()})),t}}class kV{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 BV{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 zV=\\\\\\\"hovered\\\\\\\";class UV{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=UV._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 kV(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Ga(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new BV(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(zV)}setContainerClassHovered(){this.domElement().classList.add(zV)}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 ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}UV._next_viewer_id=0;class GV extends UV{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${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 DV(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._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(this._scene.timeController.incrementTimeIfPlaying(),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t();this.render(),null===(t=this._controls_controller)||void 0===t||t.update()}}_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(){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let t of this._onBeforeRenderCallbacks)t();const t=this.camerasController.size,e=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,t,e),this._onAfterRenderCallbacks)for(let t of this._onAfterRenderCallbacks)t()}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const VV={type:\\\\\\\"change\\\\\\\"},HV=1,jV=100;var WV;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(WV||(WV={}));const qV=[WV.ON_END,WV.ALWAYS,WV.NEVER];function XV(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=oa.BUTTON(null,{callback:(t,e)=>{tH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function YV(t){return class extends t{constructor(){super(...arguments),this.camera=oa.FOLDER(),this.controls=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.EVENT}}),this.updateFromControlsMode=oa.INTEGER(qV.indexOf(WV.ON_END),{menu:{entries:qV.map(((t,e)=>({name:t,value:e})))}}),this.near=oa.FLOAT(HV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=oa.FLOAT(jV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0)}}}var $V;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}($V||($V={}));const JV=[$V.DEFAULT,$V.COVER,$V.CONTAIN];function ZV(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=oa.INTEGER(JV.indexOf($V.DEFAULT),{menu:{entries:JV.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=oa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:JV.indexOf($V.COVER)},{fovAdjustMode:JV.indexOf($V.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}XV(aa);rV(IV(Sz(eV(YV(XV(aa))))));class QV extends _z{constructor(){super(...arguments),this.renderOrder=pz.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(VV),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(){Mz.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class KV extends QV{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new tV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new nV(this)}get renderController(){return this._render_controller=this._render_controller||new FV(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new sV(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(VV),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 GV(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 jz(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 tH extends QV{}class eH extends KV{PARAM_CALLBACK_update_effects_composer(t){}}const nH=-.5,iH=.5,rH=.5,sH=-.5;class oH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1)}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const aH=new oH;class lH extends KV{constructor(){super(...arguments),this.paramsConfig=aH}static type(){return nr.ORTHOGRAPHIC}createObject(){return new st.a(2*nH,2*iH,2*rH,2*sH,HV,jV)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.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=nH*e*1,this._object.right=iH*e*1,this._object.top=rH*t*1,this._object.bottom=sH*t*1}}const cH=50;class uH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.fov=oa.FLOAT(cH,{range:[0,100]})}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const hH=new uH;class dH extends KV{constructor(){super(...arguments),this.paramsConfig=hH}static type(){return nr.PERSPECTIVE}createObject(){return new K.a(cH,1,HV,jV)}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=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}}}class pH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=oa.FOLDER(),this.resolution=oa.INTEGER(256),this.excludedObjects=oa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_printResolve(t)}}),this.near=oa.FLOAT(1),this.far=oa.FLOAT(100),this.render=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_render(t)}}),this.renderTarget=oa.FOLDER(),this.tencoding=oa.BOOLEAN(0),this.encoding=oa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(Sz(aa))){}const _H=new pH;class mH extends _z{constructor(){super(...arguments),this.paramsConfig=_H,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new NU(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 In.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,r=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&r==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new it(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 et(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=ai.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 fH extends _z{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class gH extends fH{}class vH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class yH extends vH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER}}class xH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class bH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class wH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TH extends fH{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const EH=[\\\\\\\"input pass\\\\\\\"];const MH={cook:!1,callback:function(t,e){SH.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class SH extends ia{constructor(){super(...arguments),this.flags=new ki(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return Ki.POST}static displayedInputNames(){return EH}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 CH={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 NH={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 LH 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===Pm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Pm;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===CH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(CH.uniforms),vertexShader:CH.vertexShader,fragmentShader:CH.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===NH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(NH.uniforms),vertexShader:NH.vertexShader,fragmentShader:NH.fragmentShader,blending:w.ub}),this.fsQuad=new km(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 Z(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Z(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 Z(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 at.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 OH=new class extends aa{constructor(){super(...arguments),this.adaptive=oa.BOOLEAN(1,{...MH}),this.averageLuminance=oa.FLOAT(.7,{...MH}),this.midGrey=oa.FLOAT(.04,{...MH}),this.maxLuminance=oa.FLOAT(16,{range:[0,20],...MH}),this.adaptiveRange=oa.FLOAT(2,{range:[0,10],...MH})}};class RH extends SH{constructor(){super(...arguments),this.paramsConfig=OH}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new LH(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 PH={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 IH extends Im{constructor(t=.96){super(),void 0===PH&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=PH,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Z(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 km(this.shaderMaterial);const e=new at.a;this.copyFsQuad=new km(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 FH=new class extends aa{constructor(){super(...arguments),this.damp=oa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...MH})}};class DH extends SH{constructor(){super(...arguments),this.paramsConfig=FH}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new IH;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const kH={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 BH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...MH})}};class zH extends SH{constructor(){super(...arguments),this.paramsConfig=BH}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new Bm(kH);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const UH={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 GH=new class extends aa{constructor(){super(...arguments),this.brightness=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.contrast=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class VH extends SH{constructor(){super(...arguments),this.paramsConfig=GH}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new Bm(UH);return console.log(\\\\\\\"brightness\\\\\\\",e),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 HH 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 jH=new class extends aa{};class WH extends SH{constructor(){super(...arguments),this.paramsConfig=jH}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new HH;return this.updatePass(e),e}updatePass(t){}}const qH=new class extends aa{};class XH extends SH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const YH={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 $H=new class extends aa{constructor(){super(...arguments),this.pow=oa.VECTOR3([2,2,2],{...MH}),this.mult=oa.COLOR([1,1,1],{...MH}),this.add=oa.COLOR([0,0,0],{...MH})}};class JH extends SH{constructor(){super(...arguments),this.paramsConfig=$H}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new Bm(YH);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 ZH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class QH extends SH{constructor(){super(...arguments),this.paramsConfig=ZH}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new Bm(Pm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const KH={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}\\\\\\\"},tj={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 ej{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const r={};let s;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(s=a,s.uniforms)for(let t of Object.keys(n))s.uniforms[t].value=n[t].value}else s=ps.markedAsInstance(i)?e:t;s&&(r[o.uuid]=o.material,o.material=s)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=r[e.uuid])}}));for(let t of Object.keys(r))delete r[t]}}class nj{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 fr,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new ej(this._scene);const r=3,s=4;this._processing_camera=new st.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 Z(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Z(this._resolution.x,this._resolution.y,o);var a=KH;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:r,SAMPLES:s}}),this._quad=new k.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=tj;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=rj.smoothstep(e.near,e.far,t.pv.focalDepth),i=rj.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 ij=new class extends aa{constructor(){super(...arguments),this.focalDepth=oa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...MH}),this.fStep=oa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...MH}),this.maxBlur=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.vignetting=oa.BOOLEAN(0,{...MH}),this.depthBlur=oa.BOOLEAN(0,{...MH}),this.threshold=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...MH}),this.gain=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...MH}),this.bias=oa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...MH}),this.fringe=oa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...MH}),this.noise=oa.BOOLEAN(0,{...MH}),this.dithering=oa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...MH}),this.pentagon=oa.BOOLEAN(0,{...MH}),this.rings=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...MH}),this.samples=oa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...MH}),this.clearColor=oa.COLOR([1,1,1],{...MH})}};class rj extends SH{constructor(){super(...arguments),this.paramsConfig=ij}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 nj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Ai(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 sj={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 oj=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR2([.5,.5],{...MH}),this.angle=oa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...MH}),this.scale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH})}};class aj extends SH{constructor(){super(...arguments),this.paramsConfig=oj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new Bm(sj);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 lj={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 cj extends Im{constructor(t,e,n,i){super(),void 0===lj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const r=lj;this.uniforms=I.clone(r.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:r.vertexShader,fragmentShader:r.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 km(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 uj=new class extends aa{constructor(){super(...arguments),this.noiseIntensity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.scanlinesIntensity=oa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.scanlinesCount=oa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...MH}),this.grayscale=oa.BOOLEAN(1,{...MH})}};class hj extends SH{constructor(){super(...arguments),this.paramsConfig=uj}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new cj(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 dj={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 pj=new class extends aa{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(1,MH)}};class _j extends SH{constructor(){super(...arguments),this.paramsConfig=pj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new Bm(dj);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 mj={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 fj=new class extends aa{};class gj extends SH{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new Bm(mj);return this.updatePass(e),e}updatePass(t){}}const vj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class yj extends SH{constructor(){super(...arguments),this.paramsConfig=vj}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new Bm(IU);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 xj=new class extends aa{constructor(){super(...arguments),this.map=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP},...MH}),this.darkness=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH}),this.offset=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class bj extends SH{constructor(){super(...arguments),this.paramsConfig=xj}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 Bm(bj._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()==Ki.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 wj={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},Tj=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Aj=\\\\\\\"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 Ej extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(wj),this.material=new F({uniforms:this.uniforms,vertexShader:Tj,fragmentShader:Aj,transparent:!0}),this.fsQuad=new km(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 Mj=new class extends aa{};class Sj extends SH{constructor(){super(...arguments),this.paramsConfig=Mj}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=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=new Gm(e,i),o=new Gm(e,r);s.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=s,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new Ej(s,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const Cj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH}),this.inverse=oa.BOOLEAN(0,MH)}};class Nj extends SH{constructor(){super(...arguments),this.paramsConfig=Cj}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new zm(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 Lj=new class extends aa{};class Oj extends SH{constructor(){super(...arguments),this.paramsConfig=Lj}static type(){return\\\\\\\"null\\\\\\\"}}class Rj 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 r={minFilter:w.V,magFilter:w.V,format:w.Ib},s=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new at.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Mn,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 Z(this.resolution.x,this.resolution.y,r),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Z(s,o,r),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Z(s,o,r),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Z(s,o,r),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(s,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(s/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Pm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Pm;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 km(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,r&&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=Rj.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=Rj.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=Rj.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.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,r&&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})}}Rj.BlurDirectionX=new d.a(1,0),Rj.BlurDirectionY=new d.a(0,1);const Pj=new class extends aa{constructor(){super(...arguments),this.objectsMask=oa.STRING(\\\\\\\"*outlined*\\\\\\\",{...MH}),this.refreshObjects=oa.BUTTON(null,{...MH}),this.printObjects=oa.BUTTON(null,{cook:!1,callback:t=>{Ij.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=oa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.edgeThickness=oa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...MH}),this.edgeGlow=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.pulsePeriod=oa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...MH}),this.visibleEdgeColor=oa.COLOR([1,1,1],{...MH}),this.hiddenEdgeColor=oa.COLOR([.2,.1,.4],{...MH})}};class Ij extends SH{constructor(){super(...arguments),this.paramsConfig=Pj,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new Rj(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 Fj={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 Dj=new class extends aa{constructor(){super(...arguments),this.pixelSize=oa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...MH})}};class kj extends SH{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new Bm(Fj);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 Bj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH})}};class zj extends SH{constructor(){super(...arguments),this.paramsConfig=Bj}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(Ki.OBJ);if(e&&(e.type()==nr.PERSPECTIVE||e.type()==nr.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(Ki.OBJ);if(e&&e.type()==ZG.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const Uj={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 Gj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.angle=oa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...MH})}};class Vj extends SH{constructor(){super(...arguments),this.paramsConfig=Gj}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new Bm(Uj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const Hj={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 jj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...MH})}};class Wj extends SH{constructor(){super(...arguments),this.paramsConfig=jj}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new Bm(Hj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const qj=new class extends aa{};class Xj extends SH{constructor(){super(...arguments),this.paramsConfig=qj}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 Yj=I.clone(IU.uniforms);Yj.delta={value:new d.a};const $j={uniforms:Yj,vertexShader:IU.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const Jj=new class extends aa{constructor(){super(...arguments),this.delta=oa.VECTOR2([2,2],{...MH})}};class Zj extends SH{constructor(){super(...arguments),this.paramsConfig=Jj}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new Bm($j);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 Qj={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 Kj 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 r={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let s=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Z(s,o,r),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}\\\\\\\"})}}Kj.BlurDirectionX=new d.a(1,0),Kj.BlurDirectionY=new d.a(0,1);const tW=new class extends aa{constructor(){super(...arguments),this.strength=oa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...MH}),this.radius=oa.FLOAT(1,{...MH}),this.threshold=oa.FLOAT(0,{...MH})}};class eW extends SH{constructor(){super(...arguments),this.paramsConfig=tW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new Kj(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 nW=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class iW extends SH{constructor(){super(...arguments),this.paramsConfig=nW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new Bm(FU);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 rW={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 sW=new class extends aa{constructor(){super(...arguments),this.offset=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.darkness=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class oW extends SH{constructor(){super(...arguments),this.paramsConfig=sW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new Bm(rW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class aW extends ia{static context(){return Ki.POST}cook(){this.cookController.endCook()}}class lW extends aW{}class cW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class pW extends aW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _W extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mW extends ia{static context(){return Ki.ROP}cook(){this.cookController.endCook()}}class fW extends mW{}class gW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class vW extends Q.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 vW&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return Q.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class yW{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 vW){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,r=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=r,i.style.transform=r,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=rs.fit(t,this._fog_near,this._fog_far,0,1),r=rs.clamp(1-n,0,1);i.style.opacity=`${r}`,0==r&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var s=0,o=t.children.length;s{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class wW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class EW extends mW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class MW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class SW extends pG{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 r=[];const s=[];let o;for(let t=0;t0&&(s.push(t-1),s.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(r,r.length);const t=s[s.length-1];s.push(t),s.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s);const l=this.createObject(a,Sr.LINE_SEGMENTS);e.push(l)}}SW.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const CW=SW.DEFAULT_PARAMS;const NW=new class extends aa{constructor(){super(...arguments),this.createPoint=oa.BOOLEAN(CW.createPoint),this.pointsCount=oa.INTEGER(CW.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=oa.VECTOR3(CW.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=oa.BOOLEAN(CW.connectInputPoints),this.connectToLastPoint=oa.BOOLEAN(CW.connectToLastPoint)}};class LW extends gG{constructor(){super(...arguments),this.paramsConfig=NW}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 SW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const OW=new class extends aa{};class RW extends gG{constructor(){super(...arguments),this.paramsConfig=OW}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([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],r=n.animations;r?(i.animations=r.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class PW{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,o=new Array(s),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(s),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,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,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,r=i.time,s=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=r,a[1]=r+n,l[0]=t/s,l[1]=e/s,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 r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=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(s),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),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,r=this._loopCount;const s=n===w.db;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(n===w.cb){-1===r&&(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===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;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,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))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,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}var IW=n(71),FW=n(66);class DW{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,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,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}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,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++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,r=i;t!==r;++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!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){au.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;au.a.multiplyQuaternionsFlat(t,s,t,e,t,n),au.a.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}var kW=n(63);class BW extends $.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,r=i.length,s=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!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new DW(FW.a.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.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,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t{UW.PARAM_CALLBACK_reset(t,e)}})}};class UW extends gG{constructor(){super(...arguments),this.paramsConfig=zW}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.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(Ki.ANIM)){return new BW(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 GW extends pG{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,r=n.mult,s=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class QW extends gG{constructor(){super(...arguments),this.paramsConfig=ZW}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState($W.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>qW[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new $W(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class KW extends pG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,r=i.attribNamesMatchingMask(e.name);for(let t of r)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var r;const s=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>s.length)null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let u=t.getAttribute(c);if(u)this._fill_dest_array(u,o,i),u.needsUpdate=!0;else{const e=o.array.slice(0,l*s);t.setAttribute(c,new C.c(e,s))}}else null===(s=this.states)||void 0===s||s.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,r=e.array,s=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 KW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class iq extends pG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Ir[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=kr[n.type];switch(t){case Pr.VERTEX:return void await this.add_point_attribute(i,e,n);case Pr.OBJECT:return void await this.add_object_attribute(i,e,n)}ar.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Dr.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(Ir[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=kr[this.pv.type];switch(t){case Pr.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Pr.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ar.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t{r[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 s;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=s.value;for(let e of n)r[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()][s]=e}));else for(let e of t){n[e.index()][s]=r.value}}for(let e=0;e{i[t.index()]=e}));const r=Wr.arrayToIndexedArrays(i),s=t.coreGeometry();s&&s.setIndexedAttribute(this.pv.name,r.values,r.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,r=e[i];return r?r.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 Pr.VERTEX:this.delete_vertex_attribute(e,t);case Pr.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 ps(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new vs(n,t).deleteAttribute(e),t++}))}}}class uq{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,r=t.mult,s=this._data_from_texture(t.texture);if(!s)return;const{data:o,resx:a,resy:l}=s,c=o.length/(a*l),u=e.getAttribute(t.uvAttribName).array,h=u.length/2,d=new Array(h*n);let p,_,m,f,g,v,y,x,b;const w=rs.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 hq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var mq;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(mq||(mq={}));const fq=[mq.MIN_MAX_TO_01,mq.VECTOR_TO_LENGTH_1];class gq extends pG{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(),r=sr.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of r){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(fq[n.mode]){case mq.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case mq.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,r=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=oa.STRING(vq.name),this.changeName=oa.BOOLEAN(vq.changeName),this.newName=oa.STRING(vq.newName,{visibleIf:{changeName:1}})}};class xq extends gG{constructor(){super(...arguments),this.paramsConfig=yq}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(gq.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(fq.indexOf(t))}cook(t){this._operation=this._operation||new gq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var bq;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(bq||(bq={}));class wq extends pG{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=sr.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 Pr.VERTEX:return this.find_values_from_points(t,e);case Pr.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 r=0;r{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Fr.filter((t=>t.value==this.pv.classFrom))[0].name,e=Fr.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new wq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Sq=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(),this.ramp=oa.RAMP(),this.changeName=oa.BOOLEAN(0),this.newName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class Cq extends gG{constructor(){super(...arguments),this.paramsConfig=Sq}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let r=new Array(e.length);this._get_remaped_values(n,i,r);let s=this.pv.name;this.pv.changeName&&(s=this.pv.newName,t.hasAttrib(s)||t.addNumericVertexAttrib(s,n,0));let o=0;for(let t of r){e[o].setAttribValue(s,t),o++}}_get_remaped_values(t,e,n){switch(t){case zr.FLOAT:return this._get_normalized_float(e,n);case zr.VECTOR2:return this._get_normalized_vector2(e,n);case zr.VECTOR3:return this._get_normalized_vector3(e,n);case zr.VECTOR4:return this._get_normalized_vector4(e,n)}ar.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var Oq=n(18);class Rq{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new Rq(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${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 XB.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class Pq{constructor(t){this._root=new Rq(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new Oq.a(t,e);let r=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,r),null==n||r.length>n&&(r=f.sortBy(r,(e=>e.position().distanceTo(t))),r=r.slice(0,n)),r}}class Iq{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=ai.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=ai.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 Fq=new class extends aa{constructor(){super(...arguments),this.srcGroup=oa.STRING(),this.destGroup=oa.STRING(),this.name=oa.STRING(),this.maxSamplesCount=oa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=oa.FLOAT(1),this.blendWidth=oa.FLOAT(0)}};class Dq extends gG{constructor(){super(...arguments),this.paramsConfig=Fq}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([Qi.FROM_NODE,Qi.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 Pq(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 Iq;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,r){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,r);default:return this._interpolate_with_multiple_points(t,e,n,i,r)}}static _interpolate_with_1_point(t,e,n,i,r){const s=t.position(),o=e.position(),a=s.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,r):(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,r,s){if(i<=r)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,r,s);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,r){const s=e.map((e=>this._interpolate_with_1_point(t,e,n,i,r)));return f.max(s)||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 kq=new class extends aa{constructor(){super(...arguments),this.stepSize=oa.FLOAT(.1)}};class Bq extends gG{constructor(){super(...arguments),this.paramsConfig=kq}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(),r=i.min,s=i.max,o=[];for(let t=r.x;t<=s.x;t+=n)for(let e=r.y;e<=s.y;e+=n)for(let i=r.z;i<=s.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,Sr.POINTS)}}const zq=new class extends aa{constructor(){super(...arguments),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.blend=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class Uq extends gG{constructor(){super(...arguments),this.paramsConfig=zq}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([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),r=n.objects();let s,o;for(let t=0;tt.flip())),t}}Gq.fromPolygons=function(t){let e=new Gq;return e.polygons=t,e};class Vq{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 Vq(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(Hq.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,r=e.z,s=t.x,o=t.y,a=t.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let Hq=new Vq,jq=new Vq;class Wq{constructor(t,e,n,i){this.pos=(new Vq).copy(t),this.normal=(new Vq).copy(e),this.uv=(new Vq).copy(n),this.uv.z=0,i&&(this.color=(new Vq).copy(i))}clone(){return new Wq(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new Wq(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 qq{constructor(t,e){this.normal=t,this.w=e}clone(){return new qq(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,r){let s=0,o=[];for(let e=0;eqq.EPSILON?1:0;s|=i,o.push(i)}switch(s){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:r.push(t);break;case 3:let s=[],a=[];for(let e=0;e=3&&i.push(new Xq(s,t.shared)),a.length>=3&&r.push(new Xq(a,t.shared))}}}qq.EPSILON=1e-5,qq.fromPoints=function(t,e,n){let i=Hq.copy(e).sub(t).cross(jq.copy(n).sub(t)).normalize();return new qq(i.clone(),i.dot(t))};class Xq{constructor(t,e){this.vertices=t,this.shared=e,this.plane=qq.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new Xq(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class Yq{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new Yq;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 Xq(t.vertices.map((t=>new Wq(t.pos,t.normal,t.uv))),t.shared))))},Gq.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,r=t.vertices,s=[\\\\\\\"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}}),Qq=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var Kq;Gq.toMesh=function(t,e,n){let i,r,s=t.polygons;{let t=0;s.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=Zq(3*t*3),o=Zq(3*t*3),a=Qq(2*t*3),l=[];if(s.forEach((i=>{let r=i.vertices,s=r.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),s&&void 0!==r[0].color&&(e||(e=Zq(3*t*3)));for(let t=3;t<=s;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(r[0].pos),n.write(r[t-2].pos),n.write(r[t-1].pos),o.write(r[0].normal),o.write(r[t-2].normal),o.write(r[t-1].normal),a.write(r[0].uv),a.write(r[t-2].uv),a.write(r[t-1].uv),e&&(e.write(r[0].color)||e.write(r[t-2].color)||e.write(r[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=oa.BOOLEAN(nX.useBothMaterials)}};class rX extends gG{constructor(){super(...arguments),this.paramsConfig=iX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(eX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>tX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(tX.indexOf(t))}async cook(t){this._operation=this._operation||new eX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class sX extends pG{constructor(){super(...arguments),this._core_transform=new Mz}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(),r=i.max.clone().sub(i.min),s=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(r.x,r.y,r.z,n,n,n),a=this._core_transform.translation_matrix(s);return o.applyMatrix4(a),o}}sX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},sX.INPUT_CLONED_STATE=Qi.NEVER;const oX=sX.DEFAULT_PARAMS;const aX=new class extends aa{constructor(){super(...arguments),this.size=oa.FLOAT(oX.size),this.divisions=oa.INTEGER(oX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=oa.VECTOR3(oX.center)}};class lX extends gG{constructor(){super(...arguments),this.paramsConfig=aX}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(sX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new sX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class cX extends C.a{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}cX.prototype.isInstancedBufferAttribute=!0;const uX=new A.a,hX=new A.a,dX=[],pX=new k.a;class _X extends k.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new cX(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(pX.geometry=this.geometry,pX.material=this.material,void 0!==pX.material)for(let r=0;rt.far||e.push({distance:a,point:fX.clone(),uv:Qr.a.getUV(fX,wX,TX,AX,EX,MX,SX,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 NX(t,e,n,i,r,s){yX.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(xX.x=s*yX.x-r*yX.y,xX.y=r*yX.x+s*yX.y):xX.copy(yX),t.copy(e),t.x+=xX.x,t.y+=xX.y,t.applyMatrix4(bX)}CX.prototype.isSprite=!0;var LX=n(94),OX=n(81),RX=n(46);class PX{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}PX.prototype.isSphericalHarmonics3=!0;class IX extends nv.a{constructor(t=new PX,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}}IX.prototype.isLightProbe=!0;var FX=n(62),DX=n(44);class kX 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}}kX.prototype.isInstancedBufferGeometry=!0;class BX extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],s=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],r=new Uint32Array(i).buffer;return n[e]=r,r}(t,r.buffer),o=Object(Pt.c)(r.type,s),a=new as.a(o,r.stride);return a.uuid=r.uuid,e[i]=a,a}const r=t.isInstancedBufferGeometry?new kX:new S.a,s=t.data.index;if(void 0!==s){const t=Object(Pt.c)(s.type,s.array);r.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let s;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);s=new ls.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(Pt.c)(n.type,n.array);s=new(n.isInstancedBufferAttribute?cX:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(s.name=n.name),void 0!==n.usage&&s.setUsage(n.usage),void 0!==n.updateRange&&(s.updateRange.offset=n.updateRange.offset,s.updateRange.count=n.updateRange.count),r.setAttribute(e,s)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],s=[];for(let e=0,r=n.length;eNumber.EPSILON){const h=Math.sqrt(u),p=Math.sqrt(l*l+c*c),_=e.x-a/h,m=e.y+o/h,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,r=m+a*f-t.y;const g=i*i+r*r;if(g<=2)return new d.a(i,r);s=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,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new d.a(i/s,r/s)}const P=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);r=new FX.a(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new FX.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{dY.PARAM_CALLBACK_reset(t,e)}})}};class dY extends gG{constructor(){super(...arguments),this.paramsConfig=hY}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 aY,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 pY=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE],_Y={direction:new p.a(0,1,0)},mY=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],fY=new p.a(0,0,1);const gY=new class extends aa{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:pY}}),this.direction=oa.VECTOR3(_Y.direction),this.offset=oa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=oa.BOOLEAN(!0),this.stepSize=oa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{vY.PARAM_CALLBACK_update(t)}})}};class vY extends gG{constructor(){super(...arguments),this.paramsConfig=gY,this._plane=new X.a,this._raycaster=new uL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new Mz,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(Ki.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!pY.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 mY){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]),r=this._planeCorners[0].distanceTo(this._planeCorners[1]),s=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(r,s)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,fY,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 yY extends pG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),r=new Array(3*i.length);r.fill(0);for(let t=0;t0&&(s.push(t-1),s.push(t));s.push(e-1),s.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s),a}}const TY=new p.a(0,0,1);class AY extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=wY.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e,Sr.LINE_SEGMENTS)}_create_disk(t){const e=new zX(t.radius,t.segments);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e)}}AY.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const EY=AY.DEFAULT_PARAMS;const MY=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(EY.radius),this.segments=oa.INTEGER(EY.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=oa.BOOLEAN(EY.open),this.arcAngle=oa.FLOAT(EY.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=oa.VECTOR3(EY.direction)}};class SY extends gG{constructor(){super(...arguments),this.paramsConfig=MY}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new AY(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var CY;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(CY||(CY={}));const NY=[CY.SEGMENTS_COUNT,CY.SEGMENTS_LENGTH];var LY;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(LY||(LY={}));const OY=[LY.ABC,LY.ACB,LY.AB,LY.AC,LY.BC];class RY{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 r=0;for(r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r);this.params.full&&(n.push(r-1),n.push(0));const s=new S.a;if(s.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),s.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&s.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=s}_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 CY.SEGMENTS_COUNT:return this.params.segmentsCount+1;case CY.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)}}ar.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case LY.ABC:case LY.ACB:case LY.AB:case LY.AC:return this.x.copy(this.an);case LY.BC:return this.x.copy(this.bn)}ar.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case LY.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case LY.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case LY.AB:return void(this.angle=this.angle_ab);case LY.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case LY.BC:return void(this.angle=this.angle_bc)}ar.unreachable(t)}}const PY=new class extends aa{constructor(){super(...arguments),this.arc=oa.BOOLEAN(1),this.pointsCountMode=oa.INTEGER(NY.indexOf(CY.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:NY.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=oa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=oa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=oa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=oa.INTEGER(OY.indexOf(LY.ABC),{visibleIf:{arc:1,full:0},menu:{entries:OY.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=oa.BOOLEAN(1),this.addIdnAttribute=oa.BOOLEAN(1),this.center=oa.BOOLEAN(0)}};class IY extends gG{constructor(){super(...arguments),this.paramsConfig=PY,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([Qi.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 RY({arc:this.pv.arc,center:this.pv.center,pointsCountMode:NY[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:OY[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,Sr.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Sr.POINTS)),this.setObjects(n)}}class FY extends pG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}FY.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const DY=new D.a(1,1,1),kY=\\\\\\\"color\\\\\\\",BY=FY.DEFAULT_PARAMS;const zY=new class extends aa{constructor(){super(...arguments),this.fromAttribute=oa.BOOLEAN(BY.fromAttribute),this.attribName=oa.STRING(BY.attribName,{visibleIf:{fromAttribute:1}}),this.color=oa.COLOR(BY.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=oa.BOOLEAN(BY.asHsv,{visibleIf:{fromAttribute:0}})}};class UY extends gG{constructor(){super(...arguments),this.paramsConfig=zY,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(Qi.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(kY).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,DY);const n=e.points(),i=e.attribSize(this.pv.attribName),r=e.geometry(),s=r.getAttribute(this.pv.attribName).array,o=r.getAttribute(kY).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]=s;return a}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.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(jY.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):jY.PSCALE;this._point_scale.multiplyScalar(i);const r=this._matrices.scale;r.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const s=this._matrices.translate;if(s.makeTranslation(n.x,n.y,n.z),e.multiply(s),this._do_rotate_matrices){const n=this._matrices.rotate,i=jY.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(jY.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(r)}static create_instance_buffer_geo(t,e,n){const i=e.points(),r=new kX;r.copy(t),r.instanceCount=1/0;const s=i.length,o=new Float32Array(3*s),a=new Float32Array(3*s),l=new Float32Array(3*s),c=new Float32Array(4*s),u=e.hasAttrib(XY),h=new p.a(0,0,0),d=new au.a,_=new p.a(1,1,1),f=new JY(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(h,d,_),h.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(u?t.attribValue(XY,this._point_color):WY).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*s);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):qY).toArray(t,i)})),r.setAttribute(YY.UV,new cX(t,2))}r.setAttribute(YY.POSITION,new cX(o,3)),r.setAttribute(YY.SCALE,new cX(l,3)),r.setAttribute(YY.ORIENTATION,new cX(c,4)),r.setAttribute(YY.COLOR,new cX(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(s*n);i.forEach(((e,i)=>{const r=e.attribValue(t);m.isNumber(r)?o[i]=r:r.toArray(o,i*n)})),r.setAttribute(t,new cX(o,n))}));return new ps(r).markAsInstance(),r}}JY._point_color=new p.a,JY._point_uv=new d.a;class ZY extends ru{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\\\\\\\"}($Y||($Y={}));const QY=[$Y.OBJECT,$Y.GEOMETRY],KY=[{name:\\\\\\\"object\\\\\\\",value:$Y.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:$Y.GEOMETRY}];const t$=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=oa.BOOLEAN(0),this.transformMode=oa.INTEGER(0,{menu:{entries:KY}}),this.copyAttributes=oa.BOOLEAN(0),this.attributesToCopy=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=oa.BOOLEAN(0)}};class e$ extends gG{constructor(){super(...arguments),this.paramsConfig=t$,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.ALWAYS,Qi.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new JY(e).matrices();const r=new p.a,s=new au.a,o=new p.a;i[0].decompose(r,s,o),this._attribute_names_to_copy=sr.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=vs.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const r=e.attribValue(n);new vs(t,i).addAttribute(n,r)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new ZY(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const n$=\\\\\\\"id\\\\\\\",i$=\\\\\\\"class\\\\\\\",r$=\\\\\\\"html\\\\\\\";class s$ extends pG{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(n$):e.className,r=e.useClassAttrib?t.attribValue(i$):e.className,s=e.useHtmlAttrib?t.attribValue(r$):e.html,o=s$.create_css_object({id:n,className:r,html:s}),a=o.element;if(e.copyAttributes){const n=sr.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,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return s$.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 vW(e);return n.matrixAutoUpdate=!1,n}}s$.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},s$.INPUT_CLONED_STATE=Qi.FROM_NODE;const o$=s$.DEFAULT_PARAMS;const a$=new class extends aa{constructor(){super(...arguments),this.useIdAttrib=oa.BOOLEAN(o$.useIdAttrib),this.id=oa.STRING(o$.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=oa.BOOLEAN(o$.useClassAttrib),this.className=oa.STRING(o$.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=oa.BOOLEAN(o$.useHtmlAttrib),this.html=oa.STRING(o$.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=oa.BOOLEAN(o$.copyAttributes),this.attributesToCopy=oa.STRING(o$.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class l$ extends gG{constructor(){super(...arguments),this.paramsConfig=a$}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new s$(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c${constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Dr.STRING:Dr.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class u${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 ps(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=sr.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const r=Wr.remapName(n);let s=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Dr.STRING)if(this._options.doConvert&&sr.matchesOneMask(n,i)){const e=s.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(r,new C.c(e,a))}else{const t=Wr.arrayToIndexedArrays(s);e.setIndexedAttribute(r,t.values,t.indices)}else{const e=s;t.setAttribute(r,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=sr.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 r=[n,t].join(\\\\\\\":\\\\\\\"),s=i[n];sr.matchesOneMask(r,e)||(this._attribute_datas_by_name[r]=c$.from_value(s))}else sr.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=c$.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 h$=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const d$=new class extends aa{constructor(){super(...arguments),this.data=oa.STRING(h$)}};class p$ extends gG{constructor(){super(...arguments),this.paramsConfig=d$}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new u$;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Sr.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class _$ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new u$(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const r=this._parser.createObject();t(r)})).catch((t=>{ai.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const m$=\\\\\\\"position\\\\\\\";class f${constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(f$.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>Wr.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(f$.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=oa.STRING(y$,{fileBrowse:{type:[Ls.JSON]}}),this.jsonDataKeysPrefix=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.skipEntries=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convert=oa.BOOLEAN(0,{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convertToNumeric=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON),convert:1}}),this.readAttribNamesFromFile=oa.BOOLEAN(1,{visibleIf:{dataType:v$.indexOf(g$.CSV)}}),this.attribNames=oa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.CSV),readAttribNamesFromFile:0}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{b$.PARAM_CALLBACK_reload(t,e)}})}};class b$ extends gG{constructor(){super(...arguments),this.paramsConfig=x$}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(v$[this.pv.dataType]){case g$.JSON:return this._load_json();case g$.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new _$(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,Sr.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 f$(t),n=await e.load(this._url());n?this.setGeometry(n,Sr.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class w$ extends S.a{constructor(t,e,n,i){super();const r=[],s=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function u(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new T$(n.clone(),i.clone()))}function h(t,e){const n=[],r=.5*Math.abs(i.dot(e));for(let i=0;i0,h=t[i+1].position.dot(e)-r>0,p=t[i+2].position.dot(e)-r>0;switch(c=(u?1:0)+(h?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(u&&(s=t[i+1],o=t[i+2],a=d(t[i],s,e,r),l=d(t[i],o,e,r)),h){s=t[i],o=t[i+2],a=d(t[i+1],s,e,r),l=d(t[i+1],o,e,r),n.push(a),n.push(o.clone()),n.push(s.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(s=t[i],o=t[i+1],a=d(t[i+2],s,e,r),l=d(t[i+2],o,e,r)),n.push(s.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:u||(s=t[i].clone(),o=d(s,t[i+1],e,r),a=d(s,t[i+2],e,r),n.push(s),n.push(o),n.push(a)),h||(s=t[i+1].clone(),o=d(s,t[i+2],e,r),a=d(s,t[i],e,r),n.push(s),n.push(o),n.push(a)),p||(s=t[i+2].clone(),o=d(s,t[i],e,r),a=d(s,t[i+1],e,r),n.push(s),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const r=t.position.dot(n)-i,s=r/(r-(e.position.dot(n)-i));return new T$(new p.a(t.position.x+s*(e.position.x-t.position.x),t.position.y+s*(e.position.y-t.position.y),t.position.z+s*(e.position.z-t.position.z)),new p.a(t.normal.x+s*(e.normal.x-t.normal.x),t.normal.y+s*(e.normal.y-t.normal.y),t.normal.z+s*(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 L${constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,r)=>{n==e&&(t[i]=r,i++)})),t}}}var O$;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(O$||(O$={}));const R$=[O$.EQUAL,O$.LESS_THAN,O$.EQUAL_OR_LESS_THAN,O$.EQUAL_OR_GREATER_THAN,O$.GREATER_THAN,O$.DIFFERENT],P$={[O$.EQUAL]:(t,e)=>t==e,[O$.LESS_THAN]:(t,e)=>tt<=e,[O$.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[O$.GREATER_THAN]:(t,e)=>t>e,[O$.DIFFERENT]:(t,e)=>t!=e},I$=R$.map(((t,e)=>({name:t,value:e})));class F${constructor(t){this.node=t}evalForEntities(t){const e=kr[this.node.pv.attribType];switch(e){case Dr.NUMERIC:return void this._eval_for_numeric(t);case Dr.STRING:return void this._eval_for_string(t)}ar.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=Ur[this.node.pv.attribSize-1];switch(e){case zr.FLOAT:return this._eval_for_points_numeric_float(t);case zr.VECTOR2:return this._eval_for_points_numeric_vector2(t);case zr.VECTOR3:return this._eval_for_points_numeric_vector3(t);case zr.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ar.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const r=R$[this.node.pv.attribComparisonOperator],s=P$[r];for(let r of t)i=r.attribValue(e),s(i,n)&&this.node.entitySelectionHelper.select(r)}_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 r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_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 r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}}class D${constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class k${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 XB.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 B${constructor(t){this.node=t}eval_for_objects(t){const e=Lr[this.node.pv.objectType];for(let n of t){Nr(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class z${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 U$=new p.a(0,1,0),G$=new p.a(0,-1,0);class V${constructor(t){this.node=t,this._matDoubleSideTmpSetter=new z$,this._point_position=new p.a,this._raycaster=new uL,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 r=n.geometry;r.computeBoundingBox();const s=r.boundingBox;for(let e of t)e.getPosition(this._point_position),s.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,U$)&&this._isPositionInObject(this._point_position,i,G$)&&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 r=this._raycaster.intersectObject(e,!1,this._intersections);if(!r)return!1;if(0==r.length)return!1;const s=null===(i=r[0].face)||void 0===i?void 0:i.normal;if(!s)return!1;return this._raycaster.ray.direction.dot(s)>=0}}const H$=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Ir.indexOf(Pr.VERTEX),{menu:{entries:Fr}}),this.invert=oa.BOOLEAN(0),this.byObjectType=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}}),this.objectType=oa.INTEGER(Lr.indexOf(Sr.MESH),{menu:{entries:Or},visibleIf:{class:Ir.indexOf(Pr.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=oa.BOOLEAN(0),this.expression=oa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=oa.BOOLEAN(0),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},visibleIf:{byAttrib:1}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=oa.INTEGER(1,{range:Gr,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC)}}),this.attribComparisonOperator=oa.INTEGER(R$.indexOf(O$.EQUAL),{menu:{entries:I$},visibleIf:{byAttrib:!0,attribType:kr.indexOf(Dr.NUMERIC),attribSize:zr.FLOAT}}),this.attribValue1=oa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:1}}),this.attribValue2=oa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:2}}),this.attribValue3=oa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:3}}),this.attribValue4=oa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:4}}),this.attribString=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.STRING)},separatorAfter:!0}),this.byBbox=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.bboxSize=oa.VECTOR3([1,1,1],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0}}),this.bboxCenter=oa.VECTOR3([0,0,0],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.keepPoints=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}})}};class j$ extends gG{constructor(){super(...arguments),this.paramsConfig=H$,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new L$(this),this.byExpressionHelper=new D$(this),this.byAttributeHelper=new F$(this),this.byObjectTypeHelper=new B$(this),this.byBboxHelper=new k$(this),this.byBoundingObjectHelper=new V$(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Pr.VERTEX:await this._eval_for_points(e,n);break;case Pr.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,r=[];for(let t=0;t0){const e=ps.geometryFromPoints(a,Nr(t.constructor));e&&(t.geometry=e,r.push(t))}}}this.setObjects(r)}_point_object(t){const e=t.points(),n=ps.geometryFromPoints(e,Sr.POINTS);if(n)return this.createObject(n,Sr.POINTS)}}const W$=new class extends aa{constructor(){super(...arguments),this.start=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=oa.BOOLEAN(0),this.count=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class q$ extends gG{constructor(){super(...arguments),this.paramsConfig=W$}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class X${constructor(){this.pluginCallbacks=[],this.register((function(t){return new bJ(t)})),this.register((function(t){return new wJ(t)})),this.register((function(t){return new TJ(t)})),this.register((function(t){return new AJ(t)})),this.register((function(t){return new EJ(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 xJ,r=[];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,r=i.options,s=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(s);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===r.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=vJ(t.result),r=new DataView(new ArrayBuffer(8));r.setUint32(0,i.byteLength,!0),r.setUint32(4,5130562,!0);const s=vJ(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let 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i=this,r=i.cache,s=i.json,o=i.options,a=i.pending;r.images.has(t)||r.images.set(t,{});const l=r.images.get(t),c=e===By?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",u=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[u])return l[u];s.images||(s.images=[]);const h={mimeType:c};if(o.embedImages){const r=yJ=yJ||document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.min(t.width,o.maxTextureSize),r.height=Math.min(t.height,o.maxTextureSize);const s=r.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(s.translate(0,r.height),s.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)s.drawImage(t,0,0,r.width,r.height);else{e!==By&&e!==ky&&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===By)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 s=n.materials.push(i)-1;return e.materials.set(t,s),s}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=[],r={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)r[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===s.groups.length)return null;const m=_?t.material:[t.material],f=_?s.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=u),null!==s.index){let i=this.getUID(s.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(s.index,s,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(r,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:mx.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 r=(t=X$.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,s=[],o=[];for(let t=0;t0){const e=[];for(let i=0,r=t.children.length;i0&&(r.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,r)}));const s=e.nodes.push(r)-1;return i.set(t,s),s}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 r=[];for(let e=0,i=t.children.length;e0&&(i.nodes=r),this.serializeUserData(t,i)}processObjects(t){const e=new UE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(s.range=t.distance)):t.isSpotLight&&(s.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(s.range=t.distance),s.spot={},s.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,s.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.\\\\\\\"),r[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},r[this.name]=!0);const o=i.extensions[this.name].lights;o.push(s),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class wJ{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 TJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,r={};e.pbrMetallicRoughness.baseColorFactor&&(r.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const s=[1,1,1];if(t.specular.toArray(s,0),r.specularFactor=s,r.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(r.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),r.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class AJ{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,r={};if(r.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),r.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class EJ{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,r={};if(r.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),r.thicknessTexture=e}r.attenuationDistance=t.attenuationDistance,r.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}function MJ(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)}X$.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),r=new t.TimeBufferType(t.times.length+1),s=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){r[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){r.set(t.times.slice(0,l+1),0),r[l+1]=e,r.set(t.times.slice(l+1),l+2),s.set(t.values.slice(0,(l+1)*i),0),s.set(o.evaluate(e),(l+1)*i),s.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=r,t.values=s,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},r=t.tracks;for(let t=0;t{CJ.PARAM_CALLBACK_export(t)}})}};class CJ extends gG{constructor(){super(...arguments),this.paramsConfig=SJ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.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 fr;for(let t of n)i.add(t);(new X$).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",MJ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){MJ(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 NJ=new class extends aa{constructor(){super(...arguments),this.makeFacesUnique=oa.BOOLEAN(0),this.addFaceCenterAttribute=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=oa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class LJ extends gG{constructor(){super(...arguments),this.paramsConfig=NJ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.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),r=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],s=n.itemSize,o=new Float32Array(r*s);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(),u=t.coreGeometry();if(c.isMesh&&u){s=u.faces(),u.hasAttrib(e)||u.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==OJ.JSON&&this._options.format==OJ.AUTO)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new aY(this.loadingManager).parse(n,(e=>{IJ.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{IJ.decrement_in_progress_loads_count(),e(t)}));else{const r=await this._loaderForFormat();if(r)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),r.load(n,(e=>{this.on_load_success(e).then((e=>{IJ.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{ai.warn(\\\\\\\"error loading\\\\\\\",n,t),IJ.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==OJ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case PJ.PDB:return this.on_load_succes_pdb(t);case PJ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case PJ.DRC:return this.on_load_succes_drc(i);default:return[new k.a(i)]}const r=t;if(null!=r.scene)switch(e){case PJ.GLTF:case PJ.GLB:return this.on_load_succes_gltf(r);default:return[n]}const s=t;if(s.geometryAtoms||s.geometryBonds)switch(e){case PJ.PDB:return this.on_load_succes_pdb(s);default:return[]}return[]}on_load_succes_drc(t){return[new k.a(t,IJ._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 gr.a(t.geometryAtoms,IJ._default_mat_point),new Tr.a(t.geometryBonds,IJ._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case OJ.AUTO:return this.moduleNamesFromExt(e);case OJ.DRC:return[Vn.DRACOLoader];case OJ.FBX:return[Vn.FBXLoader];case OJ.JSON:return[];case OJ.GLTF:return[Vn.GLTFLoader];case OJ.GLTF_WITH_DRACO:return[Vn.GLTFLoader,Vn.DRACOLoader];case OJ.OBJ:return[Vn.OBJLoader];case OJ.PDB:return[Vn.PDBLoader];case OJ.PLY:return[Vn.PLYLoader];case OJ.STL:return[Vn.STLLoader]}ar.unreachable(t)}static moduleNamesFromExt(t){switch(t){case PJ.DRC:return[Vn.DRACOLoader];case PJ.FBX:return[Vn.FBXLoader];case PJ.GLTF:return[Vn.GLTFLoader];case PJ.GLB:return[Vn.GLTFLoader,Vn.DRACOLoader];case PJ.OBJ:return[Vn.OBJLoader];case PJ.PDB:return[Vn.PDBLoader];case PJ.PLY:return[Vn.PLYLoader];case PJ.STL:return[Vn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case OJ.AUTO:return this._loaderForExt();case OJ.DRC:return this.loader_for_drc(this._node);case OJ.FBX:return this.loader_for_fbx();case OJ.JSON:return;case OJ.GLTF:return this.loader_for_gltf();case OJ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case OJ.OBJ:return this.loader_for_obj();case OJ.PDB:return this.loader_for_pdb();case OJ.PLY:return this.loader_for_ply();case OJ.STL:return this.loader_for_stl()}ar.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case PJ.DRC:return this.loader_for_drc(this._node);case PJ.FBX:return this.loader_for_fbx();case PJ.GLTF:return this.loader_for_gltf();case PJ.GLB:return this.loader_for_glb(this._node);case PJ.OBJ:return this.loader_for_obj();case PJ.PDB:return this.loader_for_pdb();case PJ.PLY:return this.loader_for_ply();case PJ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=ai.modulesRegister.module(Vn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=ai.modulesRegister.module(Vn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=ai.modulesRegister.module(Vn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.DRACOPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${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 IJ.loader_for_drc(t)}static async loader_for_glb(t){const e=ai.modulesRegister.module(Vn.GLTFLoader),n=ai.modulesRegister.module(Vn.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=ai.libs.root(),r=ai.libs.DRACOGLTFPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${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 IJ.loader_for_glb(t)}loader_for_obj(){const t=ai.modulesRegister.module(Vn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=ai.modulesRegister.module(Vn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=ai.modulesRegister.module(Vn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=ai.modulesRegister.module(Vn.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)}))}}IJ._default_mat_mesh=new br.a,IJ._default_mat_point=new yr.a,IJ._default_mat_line=new wr.a,IJ.MAX_CONCURRENT_LOADS_COUNT=IJ._init_max_concurrent_loads_count(),IJ.CONCURRENT_LOADS_DELAY=IJ._init_concurrent_loads_delay(),IJ.in_progress_loads_count=0,IJ._queue=[];const FJ=`${Gg}/models/wolf.obj`;class DJ extends pG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new IJ({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)}}DJ.DEFAULT_PARAMS={url:FJ,format:OJ.AUTO};const kJ=DJ.DEFAULT_PARAMS;const BJ=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(kJ.url,{fileBrowse:{type:[Ls.GEOMETRY]}}),this.format=oa.STRING(kJ.format,{menuString:{entries:RJ.map((t=>({name:t,value:t})))}}),this.reload=oa.BUTTON(null,{callback:t=>{zJ.PARAM_CALLBACK_reload(t)}})}};class zJ extends gG{constructor(){super(...arguments),this.paramsConfig=BJ}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 IJ.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 DJ(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 UJ=new class extends aa{constructor(){super(...arguments),this.dist=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class GJ extends gG{constructor(){super(...arguments),this.paramsConfig=UJ}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.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,r={};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(\\\\\\\"-\\\\\\\");r[n]=r[n]||[],r[n].push(t)}const s=[];if(Object.keys(r).forEach((t=>{s.push(r[t][0])})),e.geometry.dispose(),s.length>0){const t=ps.geometryFromPoints(s,Nr(e.constructor));return t&&(e.geometry=t),e}}}class VJ{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,r=Math.floor(this._param_size.x/n),s=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==s||s==r-1?0==s?a.push([s+1+(t-1)*r,s+(t-1)*r,s+t*r]):a.push([s+t*r,s+(t-1)*r,s-1+t*r]):(a.push([s+t*r,s+(t-1)*r,s-1+t*r]),a.push([s+t*r,s+1+(t-1)*r,s+(t-1)*r])));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 HJ=new p.a(0,1,0);const jJ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2([1,1]),this.hexagonRadius=oa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=oa.VECTOR3([0,1,0]),this.pointsOnly=oa.BOOLEAN(0)}};class WJ extends gG{constructor(){super(...arguments),this.paramsConfig=jJ,this._core_transform=new Mz}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new VJ(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,HJ,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Sr.POINTS):this.setGeometry(t)}else this.setObjects([])}}var qJ;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(qJ||(qJ={}));const XJ=[qJ.ADD_PARENT,qJ.REMOVE_PARENT,qJ.ADD_CHILD];class YJ extends pG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=XJ[e.mode];switch(i){case qJ.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ar.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 In.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=oa.INTEGER(JJ.levels,{range:[0,5],visibleIf:[{mode:XJ.indexOf(qJ.ADD_PARENT)},{mode:XJ.indexOf(qJ.REMOVE_PARENT)}]}),this.objectMask=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}}),this.debugObjectMask=oa.BOOLEAN(0,{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}})}};class QJ extends gG{constructor(){super(...arguments),this.paramsConfig=ZJ}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(YJ.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=XJ[this.pv.mode];return $J.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new YJ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const KJ=new class extends aa{constructor(){super(...arguments),this.texture=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP}}),this.mult=oa.FLOAT(1)}};class tZ extends gG{constructor(){super(...arguments),this.paramsConfig=KJ}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==Ki.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:r,resx:s,resy:o}=i,a=r.length/(s*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,u=l.getAttribute(\\\\\\\"uv\\\\\\\"),h=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=u.array,p=h.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((s-1)*f),y=Math.floor((o-1)*(1-g)),x=y*s+v,b=r[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:Rf.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 eZ(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class nZ extends S.a{constructor(t,e,n,i,r){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const s=[],o=[],a=new Map;function l(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const o=t.clone().lerp(n,i/r),a=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?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 xZ=new class extends aa{constructor(){super(...arguments),this.length=oa.FLOAT(1,{range:[0,10]}),this.pointsCount=oa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,1,0])}};class bZ extends gG{constructor(){super(...arguments),this.paramsConfig=xZ}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 r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r)}const r=new S.a;r.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),r.setIndex(n),this.setGeometry(r,Sr.LINE_SEGMENTS)}}const wZ=new class extends aa{constructor(){super(...arguments),this.distance0=oa.FLOAT(1),this.distance1=oa.FLOAT(2),this.autoUpdate=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{TZ.PARAM_CALLBACK_update(t)}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class TZ extends gG{constructor(){super(...arguments),this.paramsConfig=wZ,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(Qi.FROM_NODE)}_create_LOD(){const t=new Mr;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: '${s.states.error.message()}')`)}else null===(r=this.states)||void 0===r||r.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&&!vs.isInGroup(n.group,t))return;const i=n.cloneMat?fs.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const r=t;this._old_mat_by_old_new_id.set(i.uuid,r.material),r.material=i,fs.apply_render_hook(t,i),fs.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})}}}}AZ.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new vi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},AZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const EZ=AZ.DEFAULT_PARAMS;const MZ=new class extends aa{constructor(){super(...arguments),this.group=oa.STRING(EZ.group),this.assignMat=oa.BOOLEAN(EZ.assignMat),this.material=oa.NODE_PATH(EZ.material.path(),{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=oa.BOOLEAN(EZ.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=oa.BOOLEAN(EZ.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=oa.BOOLEAN(EZ.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=oa.BOOLEAN(EZ.swapCurrentTex),this.texSrc0=oa.STRING(EZ.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=oa.STRING(EZ.texDest0,{visibleIf:{swapCurrentTex:1}})}};class SZ extends gG{constructor(){super(...arguments),this.paramsConfig=MZ}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(AZ.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 AZ(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CZ=n(88);class NZ{static sleep(t){return new Promise(((e,n)=>{setTimeout((()=>{e()}),t)}))}}const LZ=[Lg.VIDEO,Lg.WEB_CAM];const OZ=new class extends aa{constructor(){super(...arguments),this.webcam=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.COP,types:LZ}}),this.scale=oa.FLOAT(2,{range:[0,2],rangeLocked:[!0,!1]}),this.selfieMode=oa.BOOLEAN(0),this.minDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.maxDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.frames=oa.INTEGER(1,{range:[1,100],rangeLocked:[!0,!1]}),this.fps=oa.INTEGER(24,{range:[1,60],rangeLocked:[!0,!1]}),this.capture=oa.BUTTON(null,{callback:t=>{RZ.PARAM_CALLBACK_capture(t)}}),this.showAllMeshes=oa.BOOLEAN(1)}};class RZ extends gG{constructor(){super(...arguments),this.paramsConfig=OZ,this._faceMesh=this._createFaceMesh(),this._currentCapturedFrame=9999999,this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._faceMeshObjects=[]}static type(){return\\\\\\\"mediapipeFaceMesh\\\\\\\"}initializeNode(){this._forceTimeDependent()}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.scene(),\\\\\\\"facemesh_update_object\\\\\\\"),this._graph_node.addGraphInput(this.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this.setDirty.bind(this)))}async cook(){if(this.pv.showAllMeshes)this.setObjects(this._faceMeshObjects);else{const t=this.scene().frame()%this._faceMeshObjects.length,e=this._faceMeshObjects[t];e?this.setObject(e):this.setObjects([])}}_createFaceMesh(){const t=new CZ.FaceMesh({locateFile:t=>`https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4/${t}`});return t.onResults(this._onResults.bind(this)),t}async _getHTMLVideoElement(){const t=this.pv.webcam.nodeWithContext(Ki.COP);if(!t)return this.states.error.set(\\\\\\\"node is not a COP node\\\\\\\"),void this.cookController.endCook();if(!LZ.includes(t.type()))return this.states.error.set(`node type '${t.type()}' is not accepted by MediapipeFaceMesh (${LZ.join(\\\\\\\", \\\\\\\")})`),void this.cookController.endCook();const e=t;await e.compute();const n=e.HTMLVideoElement();if(n)return{videoElement:n};console.log(\\\\\\\"no video element found\\\\\\\")}_videoSnapshotCanvas(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.videoWidth,e.height=t.videoHeight;return e.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,e.width,e.height),e}static _canvasToTexture(t,e){return new Promise((n=>{const i=t.toDataURL(\\\\\\\"image/png\\\\\\\"),r=new Image;console.log(`start ${e}`),r.onload=()=>{const t=new J.a(r);t.name=`facemesh-texture-${e}`,t.encoding=w.ld,t.needsUpdate=!0,console.log(`done ${e}`),n({texture:t,image:r})},r.src=i}))}async _initActiveHTMLVideoElement(){const t=await this._getHTMLVideoElement();if(!t)return;const{videoElement:e}=t;for(;e.paused;)console.log(\\\\\\\"video is paused\\\\\\\"),await NZ.sleep(500);this._activeHTMLVideoElement=e}_captureAllowed(){return this._currentCapturedFrame{this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._captureSingleWebCamSnapshot(t)}))}async _captureSingleWebCamSnapshot(t){if(this._activeHTMLVideoElement)if(this._captureAllowed()){const e=this._videoSnapshotCanvas(this._activeHTMLVideoElement);this._webcamSnapshotCanvases.push(e),this._currentCapturedFrame++,console.log(\\\\\\\"capture\\\\\\\",this._currentCapturedFrame),setTimeout((()=>{this._captureSingleWebCamSnapshot(t)}),1e3/this.pv.fps)}else{console.log(\\\\\\\"converting snapshots to images and textures...\\\\\\\");let e=0;for(let t of this._webcamSnapshotCanvases){const n=await RZ._canvasToTexture(t,e);this._webcamSnapshots.push(n),e++}t()}else console.log(\\\\\\\"no video found\\\\\\\")}_initFaceMeshObjects(){this._faceMeshObjects=[];const t=this.pv.frames;for(let e=0;e{this._onSendToFaceMeshSingleResolve=e,this._faceMesh.send({image:t.image})}))}_onResults(t){if(!this._captureAllowed())return;console.log(this._currentCapturedFrame);const e=t.multiFaceLandmarks[0];if(!e)return console.error(`no landmark found (${this._currentCapturedFrame})`),void console.log(\\\\\\\"results\\\\\\\",t);const n=this._faceMeshObjects[this._currentCapturedFrame];let i=0;const r=n.geometry.getAttribute(\\\\\\\"position\\\\\\\"),s=n.geometry.getAttribute(\\\\\\\"uv\\\\\\\"),o=r.array,a=s.array,l=this.pv.scale;for(let t of e)o[3*i+0]=(1-t.x)*l,o[3*i+1]=(1-t.y)*l,o[3*i+2]=t.z*l,a[2*i+0]=t.x,a[2*i+1]=1-t.y,i++;r.needsUpdate=!0,s.needsUpdate=!0,this._updateMaterial(this._currentCapturedFrame),this._currentCapturedFrame++,this._onSendToFaceMeshSingleResolve&&this._onSendToFaceMeshSingleResolve()}_updateMaterial(t){const e=this._faceMeshObjects[t].material;if(!m.isArray(e)){const n=e,i=this._webcamSnapshots[t].texture;n.map=i,n.needsUpdate=!0}}_createFaceMeshObject(t){const e=new S.a,n=[],i=[];for(let t=0;t<468;t++)n.push(t),n.push(t),n.push(t),i.push(t),i.push(t);const r=[],s=CZ.FACEMESH_TESSELATION.length/3;for(let t=0;t{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._make_compact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_make_compact(t){const e=new Map,n=new Map,i=[];for(let r of t)r.traverse((t=>{if(t instanceof In.a)return;const r=t;if(r.geometry){const t=Nr(r.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,r.material),u.pushOnArrayAtEntry(n,t,r)}}}));const r=[];return i.forEach((t=>{var i,s;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 s=ps.mergeGeometries(n);if(s){const n=e.get(t),i=this.createObject(s,t,n);r.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===(s=this.states)||void 0===s||s.error.set(t.message)}}})),r}}PZ.DEFAULT_PARAMS={compact:!1},PZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const IZ=\\\\\\\"geometry to merge\\\\\\\",FZ=PZ.DEFAULT_PARAMS;const DZ=new class extends aa{constructor(){super(...arguments),this.compact=oa.BOOLEAN(FZ.compact),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{kZ.PARAM_CALLBACK_setInputsCount(t)}})}};class kZ extends gG{constructor(){super(...arguments),this.paramsConfig=DZ}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[IZ,IZ,IZ,IZ]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(PZ.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 PZ(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 BZ{constructor(t=Math){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.grad4=[[0,1,1,1],[0,1,1,-1],[0,1,-1,1],[0,1,-1,-1],[0,-1,1,1],[0,-1,1,-1],[0,-1,-1,1],[0,-1,-1,-1],[1,0,1,1],[1,0,1,-1],[1,0,-1,1],[1,0,-1,-1],[-1,0,1,1],[-1,0,1,-1],[-1,0,-1,1],[-1,0,-1,-1],[1,1,0,1],[1,1,0,-1],[1,-1,0,1],[1,-1,0,-1],[-1,1,0,1],[-1,1,0,-1],[-1,-1,0,1],[-1,-1,0,-1],[1,1,1,0],[1,1,-1,0],[1,-1,1,0],[1,-1,-1,0],[-1,1,1,0],[-1,1,-1,0],[-1,-1,1,0],[-1,-1,-1,0]],this.p=[];for(let e=0;e<256;e++)this.p[e]=Math.floor(256*t.random());this.perm=[];for(let t=0;t<512;t++)this.perm[t]=this.p[255&t];this.simplex=[[0,1,2,3],[0,1,3,2],[0,0,0,0],[0,2,3,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,3,0],[0,2,1,3],[0,0,0,0],[0,3,1,2],[0,3,2,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,3,2,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,0,3],[0,0,0,0],[1,3,0,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,3,0,1],[2,3,1,0],[1,0,2,3],[1,0,3,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,3,1],[0,0,0,0],[2,1,3,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,1,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,0,1,2],[3,0,2,1],[0,0,0,0],[3,1,2,0],[2,1,0,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,1,0,2],[0,0,0,0],[3,2,0,1],[3,2,1,0]]}dot(t,e,n){return t[0]*e+t[1]*n}dot3(t,e,n,i){return t[0]*e+t[1]*n+t[2]*i}dot4(t,e,n,i,r){return t[0]*e+t[1]*n+t[2]*i+t[3]*r}noise(t,e){let n,i,r;const s=(t+e)*(.5*(Math.sqrt(3)-1)),o=Math.floor(t+s),a=Math.floor(e+s),l=(3-Math.sqrt(3))/6,c=(o+a)*l,u=t-(o-c),h=e-(a-c);let d,p;u>h?(d=1,p=0):(d=0,p=1);const _=u-d+l,m=h-p+l,f=u-1+2*l,g=h-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-u*u-h*h;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],u,h));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let E=.5-f*f-g*g;return E<0?r=0:(E*=E,r=E*E*this.dot(this.grad3[w],f,g)),70*(n+i+r)}noise3d(t,e,n){let i,r,s,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),u=Math.floor(n+a),h=1/6,d=(l+c+u)*h,p=t-(l-d),_=e-(c-d),m=n-(u-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),E=s[A][0]>=3?1:0,M=s[A][1]>=3?1:0,S=s[A][2]>=3?1:0,C=s[A][3]>=3?1:0,N=s[A][0]>=2?1:0,L=s[A][1]>=2?1:0,O=s[A][2]>=2?1:0,R=s[A][3]>=2?1:0,P=s[A][0]>=1?1:0,I=s[A][1]>=1?1:0,F=s[A][2]>=1?1:0,D=s[A][3]>=1?1:0,k=x-E+l,B=b-M+l,z=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-R+2*l,W=x-P+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,rt=o[K+E+o[tt+M+o[et+S+o[nt+C]]]]%32,st=o[K+N+o[tt+L+o[et+O+o[nt+R]]]]%32,ot=o[K+P+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(r[it],x,b,w,T));let ct=.6-k*k-B*B-z*z-U*U;ct<0?u=0:(ct*=ct,u=ct*ct*this.dot4(r[rt],k,B,z,U));let ut=.6-G*G-V*V-H*H-j*j;ut<0?h=0:(ut*=ut,h=ut*ut*this.dot4(r[st],G,V,H,j));let ht=.6-W*W-q*q-X*X-Y*Y;ht<0?d=0:(ht*=ht,d=ht*ht*this.dot4(r[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(r[at],$,J,Z,Q)),27*(c+u+h+d+p)}}var zZ;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(zZ||(zZ={}));const UZ=[zZ.ADD,zZ.SET,zZ.MULT,zZ.SUBSTRACT,zZ.DIVIDE];const GZ=new class extends aa{constructor(){super(...arguments),this.amplitude=oa.FLOAT(1),this.tamplitudeAttrib=oa.BOOLEAN(0),this.amplitudeAttrib=oa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=oa.VECTOR3([1,1,1]),this.offset=oa.VECTOR3([0,0,0]),this.octaves=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10]}),this.seed=oa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=oa.BOOLEAN(0),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=oa.BOOLEAN(0),this.restP=oa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=oa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=oa.INTEGER(UZ.indexOf(zZ.ADD),{menu:{entries:UZ.map((t=>({name:t,value:UZ.indexOf(t)})))}}),this.computeNormals=oa.BOOLEAN(1)}};class VZ extends gG{constructor(){super(...arguments),this.paramsConfig=GZ,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([Qi.FROM_NODE])}setOperation(t){this.p.operation.set(UZ.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)!=Dr.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const r=this._get_simplex(),s=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=UZ[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,u=this.pv.tamplitudeAttrib;let h,f,g=new p.a;for(let t=0;t{r[3*t.index()+0]=e}));else{let t;for(let e=0;e{r[3*t.index()+1]=e}));else{let t;for(let e=0;e{r[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)}}JZ.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},JZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const ZZ=JZ.DEFAULT_PARAMS;const QZ=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(ZZ.applyToChildren,{separatorAfter:!0}),this.tname=oa.BOOLEAN(ZZ.tname),this.name=oa.STRING(ZZ.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=oa.BOOLEAN(ZZ.trenderOrder),this.renderOrder=oa.INTEGER(ZZ.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=oa.BOOLEAN(ZZ.tfrustumCulled),this.frustumCulled=oa.BOOLEAN(ZZ.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=oa.BOOLEAN(ZZ.tmatrixAutoUpdate),this.matrixAutoUpdate=oa.BOOLEAN(ZZ.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=oa.BOOLEAN(ZZ.tvisible),this.visible=oa.BOOLEAN(ZZ.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=oa.BOOLEAN(ZZ.tcastShadow),this.castShadow=oa.BOOLEAN(ZZ.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=oa.BOOLEAN(ZZ.treceiveShadow),this.receiveShadow=oa.BOOLEAN(ZZ.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class KZ extends gG{constructor(){super(...arguments),this.paramsConfig=QZ}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(JZ.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new JZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const tQ=new class extends aa{};class eQ extends gG{constructor(){super(...arguments),this.paramsConfig=tQ,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Qi.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 nQ 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),r=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const s=i.type(),o=`${s} ${r} = ${this.read_attribute(t,s,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 r=this._texture_allocations_controller.variable(n);if(!r)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=r.component(),n=r.allocation();if(n){const r=n.textureName(),s=new Af(t,Do.SAMPLER_2D,r);i.addDefinitions(t,[s]);return`texture2D( ${r}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Do.VEC2,nQ.UV_ATTRIB),i=new Ef(t,Do.VEC2,nQ.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${nQ.UV_VARYING} = ${nQ.UV_ATTRIB}`],xf.VERTEX)}}nQ.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",nQ.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",nQ.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class iQ{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new nQ(nQ.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${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,fs.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 ai.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new sQ(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 r=this._gpu_compute.init();null!==r&&(console.error(r),this.node.states.error.set(r))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(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[RR.TIME]={value:this.node.scene().time()},t.uniforms[RR.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 r=t.param_uniform_pairs;for(let t of r){const r=t[0],s=t[1],o=this.node.params.get(r),a=e[s];for(let t of n)t.uniforms[s]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[s])}))}}}}}_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[RR.TIME].value=this.node.scene().time(),t.material.uniforms[RR.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));r++);const s=nQ.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?cX:C.a;n.setAttribute(s,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 r=i.variables();if(!r)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const s=e.image.data;for(let t of r){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 r=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);s[r]=t}else e.attribValue(n).toArray(s,r);r+=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 r=[];for(let t of i)for(let e of t.points())r.push(e);return r}return[]}}class cQ{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 cQ(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const r=t.graph.nodeFromId(i);if(r){const t=r.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class uQ{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()==ir.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 uQ;for(let n of t){const t=cQ.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const hQ=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class dQ{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==UI.type())),n=t.filter((t=>t.type()!=UI.type())),i=n.map((t=>t.name())).sort(),r=new Map;for(let t of n)r.set(t.name(),t);for(let t of i){const n=r.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=dQ._sortNodes(t),e=dQ._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case UI.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new cQ(i.name(),Go[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,r=i.connected_input_node(),s=i.connected_input_connection_point();if(r&&s){const e=new cQ(i.attribute_name,Go[s.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case HP.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(hQ.includes(t)){const r=i.io.outputs.namedOutputConnectionPointsByName(t);if(r){const i=r.type(),s=new cQ(t,Go[i]);s.addGraphNodeId(e),n.push(s)}}}break}case gf.type():{const i=t,r=i.output_connection_point();if(r){const t=new cQ(i.attribute_name,Go[r.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)u.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 dQ;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=uQ.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 pQ 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=[],r=new F,s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]),r.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(r,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){ai.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 dQ.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 _Q=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.FLOAT(Ml.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=oa.BOOLEAN(1),this.maxTexturesSize=oa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=oa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=oa.INTEGER(0,{menu:{entries:oQ.map(((t,e)=>({value:e,name:t})))}}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{mQ.PARAM_CALLBACK_reset(t)}}),this.material=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1})}};class mQ extends gG{constructor(){super(...arguments),this.paramsConfig=_Q,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new pQ(this),this.globals_handler=new nQ(nQ.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new lQ(this),this.renderController=new iQ(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=Ki.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return Hn.GL_PARTICLES}_create_assembler_controller(){return ai.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(Qi.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 fQ extends pG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,r;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(r of(i=new ps(n),i.points())){const t=r.normal(),n=r.position().clone().add(t.multiplyScalar(e.amount));r.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}fQ.DEFAULT_PARAMS={amount:0};const gQ=fQ.DEFAULT_PARAMS;const vQ=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(gQ.amount,{range:[-1,1]})}};class yQ extends gG{constructor(){super(...arguments),this.paramsConfig=vQ}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){this._operation=this._operation||new fQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const xQ=new p.a(0,0,1),bQ=new p.a(0,0,1),wQ=new p.a(0,1,0);class TQ extends pG{constructor(){super(...arguments),this._core_transform=new Mz,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,xQ,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),r=this._create_plane(i,e);this._core_transform.rotate_geometry(r,bQ,wQ);const s=this._core_transform.translation_matrix(this._center);return r.applyMatrix4(s),this.createCoreGroupFromGeometry(r)}_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)}}TQ.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)},TQ.INPUT_CLONED_STATE=Qi.NEVER;const AQ=TQ.DEFAULT_PARAMS;const EQ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2(AQ.size),this.useSegmentsCount=oa.BOOLEAN(AQ.useSegmentsCount),this.stepSize=oa.FLOAT(AQ.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2(AQ.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=oa.VECTOR3(AQ.direction),this.center=oa.VECTOR3(AQ.center)}};class MQ extends gG{constructor(){super(...arguments),this.paramsConfig=EQ}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(TQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new TQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const SQ=\\\\\\\"position\\\\\\\";const CQ=new class extends aa{constructor(){super(...arguments),this.updateX=oa.BOOLEAN(0),this.x=oa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=oa.BOOLEAN(0),this.y=oa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=oa.BOOLEAN(0),this.z=oa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=oa.BOOLEAN(1)}};class NQ extends gG{constructor(){super(...arguments),this.paramsConfig=CQ,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(Qi.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{u[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=oa.VECTOR3(kQ.center.toArray()),this.longitude=oa.FLOAT(kQ.longitude,{range:[0,360]}),this.latitude=oa.FLOAT(kQ.latitude,{range:[-180,180]}),this.depth=oa.FLOAT(kQ.depth,{range:[0,10]})}};class zQ extends gG{constructor(){super(...arguments),this.paramsConfig=BQ}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(DQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new DQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class UQ{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,r=null;for(let s=0;s{s[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=>{s[t].push(e)}))})),o>0&&(r.push(o-1),r.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=s[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(r),o}static line_segment_to_geometries(t){var e;const n=[],i=new ps(t),r=i.attribNames(),s=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(s,i,r,e),n.push(t)}))}return n}}class GQ{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new ps(this.geometry0),e=new ps(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const r=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+h),p.push(t[1]),p.push(n[1]+h),p.push(n[0]+h)}));f.intersection(s.attribNames(),o.attribNames()).forEach((t=>{const e=s.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 VQ=new p.a(0,0,0),HQ=new p.a(1,1,1);const jQ=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.segmentsRadial=oa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=oa.BOOLEAN(0)}};class WQ extends gG{constructor(){super(...arguments),this.paramsConfig=jQ,this._core_transform=new Mz,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof Tr.a&&this._create_tube(t);const n=ps.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Sr.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new ps(n).points(),r=null===(e=n.getIndex())||void 0===e?void 0:e.array,s=UQ.accumulated_curve_point_indices(r);for(let t of s){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=wY.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,r=this._core_transform.matrix(e,VQ,HQ,1,Ez),s=n.clone();s.applyMatrix4(r),i.push(s)}for(let t=0;t0){const e=i[t],n=i[t-1],r=this._skin(n,e);this._geometries.push(r)}}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}const qQ=\\\\\\\"dist\\\\\\\";class XQ extends pG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new z$,this._raycaster=new uL,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,r;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(qQ)||t.addNumericVertexAttrib(qQ,1,-1));const s=t.points();for(let t of s)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),r=this._raycaster.intersectObjects(e.objects(),!0)[0],r){if(n.transformPoints&&t.setPosition(r.point),n.addDistAttribute){const e=this._pointPos.distanceTo(r.point);console.log(e),t.setAttribValue(qQ,e)}n.transferFaceNormals&&r.face&&t.setNormal(r.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}XQ.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},XQ.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.ALWAYS];const YQ=XQ.DEFAULT_PARAMS;const $Q=new class extends aa{constructor(){super(...arguments),this.useNormals=oa.BOOLEAN(YQ.useNormals),this.direction=oa.VECTOR3(YQ.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=oa.BOOLEAN(YQ.transformPoints),this.transferFaceNormals=oa.BOOLEAN(YQ.transferFaceNormals),this.addDistAttribute=oa.BOOLEAN(YQ.addDistAttribute)}};class JQ extends gG{constructor(){super(...arguments),this.paramsConfig=$Q}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.ALWAYS])}cook(t){this._operation=this._operation||new XQ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const ZQ={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},QQ=\\\\\\\"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}\\\\\\\",KQ=\\\\\\\"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}\\\\\\\",tK={minFilter:w.V,magFilter:w.V,format:w.ic};class eK extends k.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new X.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 K.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 Z(n,i,tK),Object(Ln.i)(n)&&Object(Ln.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new kU(new d.a(n,i)),this.material=new F({uniforms:I.clone(ZQ),fragmentShader:KQ,vertexShader:QQ}),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,r,s){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,u=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=u,t.setRenderTarget(l);var h=o.viewport;void 0!==h&&t.state.viewport(h),this.visible=!0}}}class nK extends pG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],r=await ai.renderersController.firstRenderer();if(!r)return this.createCoreGroupFromObjects(i);const s=n.objectsWithGeo();for(let t of s){const n=new eK(t.geometry,{clipBias:e.clipBias,renderer:r,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)}}nK.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},nK.INPUT_CLONED_STATE=Qi.NEVER;const iK=nK.DEFAULT_PARAMS;const rK=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(iK.active),this.clipBias=oa.FLOAT(iK.clipBias),this.color=oa.COLOR(iK.color.toArray()),this.opacity=oa.FLOAT(iK.opacity),this.pixelRatio=oa.INTEGER(iK.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=oa.BOOLEAN(iK.tblur),this.blur=oa.FLOAT(iK.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=oa.FLOAT(iK.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=oa.BOOLEAN(iK.tblur2,{visibleIf:{tblur:1}}),this.blur2=oa.FLOAT(iK.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=oa.FLOAT(iK.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class sK extends gG{constructor(){super(...arguments),this.paramsConfig=rK}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(nK.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new nK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oK=n(85);var aK;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(aK||(aK={}));const lK=[aK.POINTS_COUNT,aK.SEGMENT_LENGTH];var cK;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(cK||(cK={}));const uK=[cK.CENTRIPETAL,cK.CHORDAL,cK.CATMULLROM];const hK=new class extends aa{constructor(){super(...arguments),this.method=oa.INTEGER(lK.indexOf(aK.POINTS_COUNT),{menu:{entries:lK.map(((t,e)=>({name:t,value:e})))}}),this.curveType=oa.INTEGER(uK.indexOf(cK.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:uK.map(((t,e)=>({name:t,value:e})))}}),this.tension=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=oa.INTEGER(100,{visibleIf:{method:lK.indexOf(aK.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=oa.FLOAT(1,{visibleIf:{method:lK.indexOf(aK.SEGMENT_LENGTH)}})}};class dK extends gG{constructor(){super(...arguments),this.paramsConfig=hK}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=cs(o);return this.createObject(a,Sr.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=uK[this.pv.curveType],i=this.pv.tension,r=new oK.a(e,!1,n,i),s=this._get_points_from_curve(r);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=lK[this.pv.method];switch(e){case aK.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case aK.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)}ar.unreachable(e)}}class pK extends pG{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())}}}}pK.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const _K=pK.DEFAULT_PARAMS;const mK=new class extends aa{constructor(){super(...arguments),this.tposition=oa.BOOLEAN(_K.tposition),this.position=oa.STRING(_K.position,{visibleIf:{tposition:!0}}),this.restP=oa.STRING(_K.restP,{visibleIf:{tposition:!0}}),this.tnormal=oa.BOOLEAN(_K.tnormal),this.normal=oa.STRING(_K.normal,{visibleIf:{tnormal:!0}}),this.restN=oa.STRING(_K.restN,{visibleIf:{tnormal:!0}})}};class fK extends gG{constructor(){super(...arguments),this.paramsConfig=mK}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new pK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const gK=new p.a;function vK(t,e,n,i,r,s){const o=2*Math.PI*r/4,a=Math.max(s-2*r,0),l=Math.PI/4;gK.copy(e),gK[i]=0,gK.normalize();const c=.5*o/(o+a),u=1-gK.angleTo(t)/l;if(1===Math.sign(gK[n]))return u*c;return a/(o+a)+c+c*(1-u)}class yK extends N{constructor(t=1,e=1,n=1,i=2,r=.1){if(i=2*i+1,r=Math.min(t/2,e/2,n/2,r),super(1,1,1,i,i,i),1===i)return;const s=this.toNonIndexed();this.index=null,this.attributes.position=s.attributes.position,this.attributes.normal=s.attributes.normal,this.attributes.uv=s.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(r),c=this.attributes.position.array,u=this.attributes.normal.array,h=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,s=0;io.get(t.index())||-1));let l=0;for(let t of a)s+=o.get(t.index()),r[l]=s,l++;const c=[];let u=[];e.transferAttributes&&(u=n.attribNamesMatchingMask(e.attributesToTransfer));const h=new Map,d=new Map;for(let t of u)h.set(t,[]),d.set(t,n.attribSize(t));const p=new Iq,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rs.randFloat(_+t)*s;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Sr.POINTS);return this.createCoreGroupFromObjects([v])}}AK.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},AK.INPUT_CLONED_STATE=Qi.FROM_NODE;const EK=AK.DEFAULT_PARAMS;const MK=new class extends aa{constructor(){super(...arguments),this.pointsCount=oa.INTEGER(EK.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=oa.INTEGER(EK.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=oa.BOOLEAN(EK.transferAttributes),this.attributesToTransfer=oa.STRING(EK.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=oa.BOOLEAN(EK.addIdAttribute),this.addIdnAttribute=oa.BOOLEAN(EK.addIdnAttribute)}};class SK extends gG{constructor(){super(...arguments),this.paramsConfig=MK}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this._operation=this._operation||new AK(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CK;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(CK||(CK={}));const NK=[CK.MATRIX,CK.AXIS];var LK;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(LK||(LK={}));const OK=[LK.BBOX_CENTER,LK.BBOX_CENTER_OFFSET,LK.CUSTOM];class RK extends pG{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 X.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=NK[e.mode];switch(n){case CK.MATRIX:return this._applyMatrixShear(t,e);case CK.AXIS:return this._applyAxisShear(t,e)}ar.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 ps(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=OK[e.centerMode];switch(n){case LK.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case LK.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case LK.CUSTOM:return this._getAxisModeCenterCustom(e)}ar.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 PK;RK.DEFAULT_PARAMS={mode:NK.indexOf(CK.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:OK.indexOf(LK.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},RK.INPUT_CLONED_STATE=Qi.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(PK||(PK={}));const IK=RK.DEFAULT_PARAMS;const FK=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(IK.mode,{menu:{entries:NK.map(((t,e)=>({name:t,value:e})))}}),this.xy=oa.FLOAT(IK.xy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.xz=oa.FLOAT(IK.xz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yx=oa.FLOAT(IK.yx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yz=oa.FLOAT(IK.yz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zx=oa.FLOAT(IK.zx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zy=oa.FLOAT(IK.zy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.centerMode=oa.INTEGER(IK.centerMode,{visibleIf:{mode:NK.indexOf(CK.AXIS)},menu:{entries:OK.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=oa.VECTOR3(IK.centerOffset.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.BBOX_CENTER_OFFSET)}}),this.center=oa.VECTOR3(IK.center.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.CUSTOM)}}),this.planeAxis=oa.VECTOR3(IK.planeAxis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axis=oa.VECTOR3(IK.axis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axisAmount=oa.FLOAT(IK.axisAmount,{range:[-1,1],visibleIf:{mode:NK.indexOf(CK.AXIS)}})}};class DK extends gG{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return PK.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(RK.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set(NK.indexOf(t))}cook(t){this._operation=this._operation||new RK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const kK=new class extends aa{};class BK extends gG{constructor(){super(...arguments),this.paramsConfig=kK}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=UQ.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const r=e[n-1],s=this._skin(r,t);i.push(s)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),r=this._get_line_segments(n),s=f.sortBy([i,r],(t=>-t.length)),o=s[0],a=s[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,r=this._skin(e,i);l.push(r)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}var zK;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(zK||(zK={}));const UK=[zK.X,zK.Y,zK.Z];class GK extends pG{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 vs(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const r=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const s=UK[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),s){case zK.X:o=this._pointPos.x;break;case zK.Y:o=this._pointPos.y;break;case zK.Z:o=this._pointPos.z}this._positions[a]=o,u.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 h=f.uniq(l);for(let t of h){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(r.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=oa.BOOLEAN(VK.invert)}};class jK extends gG{constructor(){super(...arguments),this.paramsConfig=HK}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new GK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const WK=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.INTEGER(Ml.START_FRAME)}};class qK extends xG{constructor(){super(...arguments),this.paramsConfig=WK,this._last_simulated_frame=null,this.childrenDisplayController=new wG(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(Qi.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:JK[t]})))}}),this.radius=oa.FLOAT(KK.radius,{visibleIf:{type:JK.default}}),this.resolution=oa.VECTOR2(KK.resolution,{visibleIf:{type:JK.default}}),this.open=oa.BOOLEAN(KK.open,{visibleIf:{type:JK.default}}),this.phiStart=oa.FLOAT(KK.phiStart,{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.phiLength=oa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaStart=oa.FLOAT(KK.thetaStart,{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaLength=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.detail=oa.INTEGER(KK.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:JK.isocahedron}}),this.center=oa.VECTOR3(KK.center)}};class e0 extends gG{constructor(){super(...arguments),this.paramsConfig=t0}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(QK.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new QK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const n0=new class extends aa{constructor(){super(...arguments),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\")}};class i0 extends gG{constructor(){super(...arguments),this.paramsConfig=n0,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const r=t.object(),s=e.pointsFromGeometry(),o=s[0];if(o){if(o.attribSize(n)!=zr.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of s)t=e.indexedAttribValue(n),u.pushOnArrayAtEntry(i,t,e);else for(let e of s)t=e.attribValue(n),u.pushOnArrayAtEntry(i,t,e)}const a=Nr(r.constructor);i.forEach(((t,e)=>{const i=ps.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);vs.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const r0=new A.a,s0=new Q.a,o0=new p.a;class a0 extends $.a{constructor(){super(),this.uuid=Ln.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 U.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=s[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,r),_=c(_,3,s),_=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),s){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(u(e.normal)),a){const t=e.vertexNormals;n.push(u(t[0]),u(t[1]),u(t[2]))}if(l&&n.push(h(e.color)),p){const t=e.vertexColors;n.push(h(t[0]),h(t[1]),h(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=s),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new a0).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=[],r=t.morphTargets[n];for(let t=0,e=r.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}}a0.prototype.isGeometry=!0;class l0{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,r;const s=t.faces;for(i=0;i0,s=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){h=[];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,r,s){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(r),s[t].edges.push(o),s[e].edges.push(o)}function i(t,e,n,i,r){t.push(new c0(e,n,i,void 0,void 0,r))}function r(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function s(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}u0.prototype.smooth=function(o){var a,l,c,u,h,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,E,M,S,C,N,L,O,R,P,I,F,D,k,B=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(B)for(var z=0;z3&&(O=3/(8*_)),R=1-_*O,P=O,_<=2&&2==_&&(R=3/4,P=1/8),k=D.clone().multiplyScalar(R),w.set(0,0,0),z=0;z<_;z++)A=(I=F[z]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(P),k.add(w),b.push(k)}u=b.concat(x);var U,G,V,H,j,W,q,X=b.length;h=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{v0.PARAM_CALLBACK_reset(t)}})}};class v0 extends gG{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return er.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.input_graph_node(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var y0=n(82);class x0 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new y0.a(this.loadingManager),r=await this._urlToLoad();i.load(r,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new y0.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new In.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)}}w0.DEFAULT_PARAMS={url:b0,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const T0=w0.DEFAULT_PARAMS;const A0=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(T0.url,{fileBrowse:{type:[Ls.SVG]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{E0.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=oa.BOOLEAN(T0.drawFillShapes),this.fillShapesWireframe=oa.BOOLEAN(T0.fillShapesWireframe),this.drawStrokes=oa.BOOLEAN(T0.drawStrokes),this.strokesWireframe=oa.BOOLEAN(T0.strokesWireframe)}};class E0 extends gG{constructor(){super(...arguments),this.paramsConfig=A0}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Vn.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 w0(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 M0=\\\\\\\"geometry to switch to\\\\\\\";const S0=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class C0 extends gG{constructor(){super(...arguments),this.paramsConfig=S0}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[M0,M0,M0,M0]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.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 N0 extends nZ{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 L0=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.detail=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=oa.BOOLEAN(0),this.center=oa.VECTOR3([0,0,0])}};class O0 extends gG{constructor(){super(...arguments),this.paramsConfig=L0}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new N0(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,Sr.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class R0 extends YX{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 P0=n(48);class I0 extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials),s.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=r.parse(n);e&&e(i)}),n,i)}parse(t){return new F0(t)}}class F0{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),r=e/n.resolution,s=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,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 ai.modulesRegister.module(Vn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await ai.modulesRegister.module(Vn.SVGLoader);if(t)return t}}var B0;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(B0||(B0={}));const z0=[B0.MESH,B0.FLAT,B0.LINE,B0.STROKE],U0=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const G0=new class extends aa{constructor(){super(...arguments),this.font=oa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Ls.FONT]}}),this.text=oa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=oa.INTEGER(0,{menu:{entries:z0.map(((t,e)=>({name:t,value:e})))}}),this.size=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=oa.FLOAT(.1,{visibleIf:{type:z0.indexOf(B0.MESH)}}),this.segments=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:z0.indexOf(B0.MESH)}}),this.strokeWidth=oa.FLOAT(.02,{visibleIf:{type:z0.indexOf(B0.STROKE)}})}};class V0 extends gG{constructor(){super(...arguments),this.paramsConfig=G0,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(z0[this.pv.type]){case B0.MESH:return this._create_geometry_from_type_mesh(t);case B0.FLAT:return this._create_geometry_from_type_flat(t);case B0.LINE:return this._create_geometry_from_type_line(t);case B0.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 R0(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(U0)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new KX(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 r=0;r0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||r||(r=n[e.textureName]))}));if(r)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=r;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=r)}n.needsUpdate=!0}}));return n}}H0.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},H0.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const j0=H0.DEFAULT_PARAMS;const W0=new class extends aa{constructor(){super(...arguments),this.textureName=oa.STRING(j0.textureName)}};class q0 extends gG{constructor(){super(...arguments),this.paramsConfig=W0}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(H0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new H0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class X0 extends pG{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 r=i.map((t=>this._update_object(t,e)));return await Promise.all(r),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 ai.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)}}X0.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},X0.INPUT_CLONED_STATE=Qi.FROM_NODE;const Y0=X0.DEFAULT_PARAMS;const $0=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(Y0.applyToChildren,{separatorAfter:!0}),this.tencoding=oa.BOOLEAN(Y0.tencoding),this.encoding=oa.INTEGER(Y0.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=oa.BOOLEAN(Y0.tmapping),this.mapping=oa.INTEGER(Y0.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=oa.BOOLEAN(Y0.twrap),this.wrapS=oa.INTEGER(Y0.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=oa.INTEGER(Y0.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=oa.BOOLEAN(Y0.tanisotropy),this.useRendererMaxAnisotropy=oa.BOOLEAN(Y0.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=oa.INTEGER(Y0.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Y0.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(Y0.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class J0 extends gG{constructor(){super(...arguments),this.paramsConfig=$0}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(X0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new X0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Z0=new p.a(0,0,1);class Q0 extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=new eY(n,i,r,s);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,Z0,e.direction),this.createCoreGroupFromGeometry(o)}}Q0.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},Q0.INPUT_CLONED_STATE=Qi.FROM_NODE;const K0=Q0.DEFAULT_PARAMS;const t1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(K0.radius,{range:[0,1]}),this.radiusTube=oa.FLOAT(K0.radiusTube,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(K0.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=oa.INTEGER(K0.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=oa.VECTOR3(K0.direction),this.center=oa.VECTOR3(K0.center)}};class e1 extends gG{constructor(){super(...arguments),this.paramsConfig=t1}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new Q0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class n1 extends pG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=e.p,a=e.q,l=new nY(n,i,r,s,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}n1.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},n1.INPUT_CLONED_STATE=Qi.FROM_NODE;const i1=n1.DEFAULT_PARAMS;const r1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(i1.radius),this.radiusTube=oa.FLOAT(i1.radiusTube),this.segmentsRadial=oa.INTEGER(i1.segmentsRadial,{range:[1,128]}),this.segmentsTube=oa.INTEGER(i1.segmentsTube,{range:[1,32]}),this.p=oa.INTEGER(i1.p,{range:[1,10]}),this.q=oa.INTEGER(i1.q,{range:[1,10]}),this.center=oa.VECTOR3(i1.center)}};class s1 extends gG{constructor(){super(...arguments),this.paramsConfig=r1}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new n1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var o1;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(o1||(o1={}));const a1=[o1.SET_PARAMS,o1.UPDATE_MATRIX];class l1 extends pG{constructor(){super(...arguments),this._core_transform=new Mz,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=wz[e.applyOn];switch(n){case yz.GEOMETRIES:return this._update_geometries(t,e);case yz.OBJECTS:return this._update_objects(t,e)}ar.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=dG._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=a1[e.objectMode];switch(n){case o1.SET_PARAMS:return this._update_objects_params(t,e);case o1.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ar.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=Az[e.rotationOrder];this._r.copy(e.r).multiplyScalar(Ln.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,Az[t.rotationOrder])}}l1.DEFAULT_PARAMS={applyOn:wz.indexOf(yz.GEOMETRIES),objectMode:a1.indexOf(o1.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:Az.indexOf(Tz.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)},l1.INPUT_CLONED_STATE=Qi.FROM_NODE;const c1=l1.DEFAULT_PARAMS;const u1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(c1.applyOn,{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=oa.INTEGER(c1.objectMode,{visibleIf:{applyOn:wz.indexOf(yz.OBJECTS)},menu:{entries:a1.map(((t,e)=>({name:t,value:e})))}}),this.group=oa.STRING(c1.group,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}}),this.rotationOrder=oa.INTEGER(c1.rotationOrder,{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3(c1.t),this.r=oa.VECTOR3(c1.r),this.s=oa.VECTOR3(c1.s),this.scale=oa.FLOAT(c1.scale,{range:[0,10]}),this.pivot=oa.VECTOR3(c1.pivot,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}})}};class h1 extends gG{constructor(){super(...arguments),this.paramsConfig=u1}static type(){return PK.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(l1.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(wz.indexOf(t))}setObjectMode(t){this.p.objectMode.set(a1.indexOf(t))}cook(t){this._operation=this._operation||new l1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const d1=new class extends aa{constructor(){super(...arguments),this.useSecondInput=oa.BOOLEAN(1),this.reference=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.SOP},visibleIf:{useSecondInput:0}})}};class p1 extends gG{constructor(){super(...arguments),this.paramsConfig=d1}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([Qi.FROM_NODE,Qi.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 r=0;r({name:t,value:e})))}};function f1(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const g1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(wz.indexOf(yz.GEOMETRIES),{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.count=oa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(0)}),this.r0=oa.VECTOR3([0,0,0],{...f1(0)}),this.rotationOrder1=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(1)}),this.r1=oa.VECTOR3([0,0,0],{...f1(1)}),this.rotationOrder2=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(2)}),this.r2=oa.VECTOR3([0,0,0],{...f1(2)}),this.rotationOrder3=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(3)}),this.r3=oa.VECTOR3([0,0,0],{...f1(3)}),this.rotationOrder4=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(4)}),this.r4=oa.VECTOR3([0,0,0],{...f1(4)}),this.rotationOrder5=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(5)}),this.r5=oa.VECTOR3([0,0,0],{...f1(5)})}};class v1 extends gG{constructor(){super(...arguments),this.paramsConfig=g1,this._core_transform=new Mz,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([Qi.FROM_NODE,Qi.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[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=wz[this.pv.applyOn];switch(n){case yz.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case yz.OBJECTS:return this._apply_matrix_to_objects(t,e)}ar.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=[Hr.POSITION,Hr.NORMAL,Hr.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&Wr.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class w1 extends gG{constructor(){super(...arguments),this.paramsConfig=b1,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(Qi.FROM_NODE)}setMode(t){this.p.mode.set(x1.indexOf(t))}cook(t){const e=x1[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case y1.RESET_OBJECT:return this._reset_objects(e);case y1.CENTER_GEO:return this._center_geos(e,!1);case y1.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ar.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),Mz.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let r=i;const s=t[1];s&&(r=s.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 r=0,s=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,s=Math.max(s,e.y+e.h),r=Math.max(r,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?xi.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.input_graph_node(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const r=t;n=xi.findNode(i,r,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 co,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(ai.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return ks.create(this.param,e,t,n)}}class J1 extends $1{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 Z1 extends $1{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 Q1=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],K1=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class t2 extends $1{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 r=t[0],s=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(r)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,s,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(Q1.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?K1.indexOf(n)>=0?t[n]:-1:t}return-1}}class e2 extends $1{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],r=t[1];let s=null;try{s=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(s){const t=s.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(r){const t=n[r];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class n2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new co,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 r=t[0],s=this.getReferencedParam(r);if(s){s.isDirty()&&await s.compute();const t=s.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class i2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],r=t[2],s=this.node(),o=s?xi.findNode(s,n):null;let a;o&&o.type()==e$.type()&&(a=o.stamp_value(r)),null==a&&(a=i),e(a)}else e(0)}))}}class r2 extends $1{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 s2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class o2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class a2 extends $1{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 l2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${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 c2 extends $1{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 r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.objectsCount())}}else e(0)}))}}class u2 extends $1{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(sr.tailDigits(t))}else e(0)}else e(0)}))}}class h2 extends $1{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 d2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class p2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${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 _2 extends $1{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 r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.pointsCount())}}else e(0)}))}}class m2 extends $1{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 f2 extends $1{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class g2 extends $1{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 v2 extends $1{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 r=t[2]||1;return n&&(e=n.substr(i,r)),e}}class y2 extends $1{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 x2{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())),r=b.clone(i);if(0==this._validateNames(r))return;b.clone(this._created_spare_param_names).concat(r).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=hG.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(r.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},r=b.merge(n,i);let s=this._init_value_serialized_by_param_name.get(t.name());null==s&&(s=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:s,raw_input:o,options:r})}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()==Es.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 b2{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 x2(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 w2;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(w2||(w2={}));class T2{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 A2{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 E2{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)u.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)u.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class M2{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 E2(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 S2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\";\\\\\\\",[w2.BODY]:\\\\\\\";\\\\\\\"},C2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\"\\\\\\\",[w2.BODY]:\\\\\\\"\\\\t\\\\\\\"};class N2{static node_comment(t,e){let n=`// ${t.path()}`,i=C2[e];if(e==w2.BODY){let e=this.node_distance_to_material(t);t.type()==er.OUTPUT&&(e+=1),i=i.repeat(e)}return e==w2.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let r=C2[n];if(n==w2.BODY&&(r=r.repeat(this.node_distance_to_material(t))),e=`${r}${e}`,i){const t=e[e.length-1],i=S2[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==w2.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()!=er.INPUT&&t.type()!=er.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class L2{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new GI,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)u.pushOnArrayAtEntry(n,t,i)}const r=new Map;for(let t of i)r.set(t.graphNodeId(),!0);for(let e of t)r.get(e.graphNodeId())||(i.push(e),r.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 M2(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,w2.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,w2.DEFINE),this.addDefinitions(t,e,yf.VARYING,w2.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,w2.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,w2.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const r=[];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)r.push(e)}}if(r.length>0){const t=new vf(r),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const s=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),u.pushOnArrayAtEntry(s,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=s.get(e);if(n){const t=n[0];if(t){const e=N2.node_comment(t.node,i);u.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=N2.line_wrap(t.node,e.line,i);u.pushOnArrayAtEntry(a,i,n)}const r=N2.post_line_separator(i);u.pushOnArrayAtEntry(a,i,r)}}}))}}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 r=0;r0){const s=this._lines.get(e),o=N2.node_comment(t,n);if(u.pushOnArrayAtEntry(s,n,o),f.uniq(r).forEach((e=>{e=N2.line_wrap(t,e,n),u.pushOnArrayAtEntry(s,n,e)})),n!=w2.BODY||!i){const t=N2.post_line_separator(n);u.pushOnArrayAtEntry(s,n,t)}}}}class O2{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&&u.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 r=this._graph_id_by_depth.get(e);r&&r.forEach((e=>{var r;if(null===(r=this._graph_ids_by_shader_name.get(t))||void 0===r?void 0:r.get(e)){const r=this._graph.nodeFromId(e);this.add_nodes_with_children(r,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()==er.INPUT){const r=t.parent();if(r){const s=this.sorted_nodes_for_shader_name_for_parent(r,i);for(let r of s)r.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(r,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 r=this._graph_id_by_depth.get(n);r&&r.forEach((n=>{var r;if(!e||(null===(r=this._graph_ids_by_shader_name.get(e))||void 0===r?void 0:r.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const r=i[0];return t.context()==r.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&&(u.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),r=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(r.length>0)for(let e of r)u.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()==er.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 R2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),P2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),I2=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class F2 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 UI.type():case HI.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 HP.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 O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new L2(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 Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(F2.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"mvPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldNormal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"gl_Position\\\\\\\",Do.VEC4),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"cameraPosition\\\\\\\",Do.VEC3),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return F2.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 T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new T2(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 A2(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new A2(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return F2.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 R2.get(t)}insert_body_after(t){return P2.get(t)}lines_to_remove(t){return I2.get(t)}_replace_template(t,e){const n=this.builder_lines(e,w2.FUNCTION_DECLARATION),i=this.builder_lines(e,w2.DEFINE),r=this.builder_lines(e,w2.BODY);let s=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let u=!1,h=!1;for(let t of s){1==u&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),u=!1),1==h&&(r&&this._insert_lines(o,r),h=!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&&(u=!0),l&&t.indexOf(l)>=0&&(h=!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=z[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var D2,k2;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(D2||(D2={})),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\\\\\\\"}(k2||(k2={}));const B2=[k2.GL_FRAGCOORD,k2.GL_POINTCOORD];class z2 extends F2{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 r=i.createMaterial();r.name=e,t.customMaterials[e]=r}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 r=t.customMaterials[n];r&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(r),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 r=this._shaders_by_name.get(xf.VERTEX),s=this._shaders_by_name.get(xf.FRAGMENT);r&&s&&(t.vertexShader=r,t.fragmentShader=this.processFilterFragmentShader(s),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 r=t.io.inputs.named_input(n),s=t.variableForInput(n),o=this.variable_config(n);let a=null;if(r)a=uf.vector3(s);else if(o.default_from_attribute()){const r=t.io.inputs.namedInputConnectionPointsByName(n);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),r=o.if_condition();r&&e.addBodyLines(t,[`#if ${r}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const s=o.postLines();s&&e.addBodyLines(t,s),r&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,r=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(r)for(let n of r)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(),r=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),s=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${s} = ${r}`])}handle_globals_output_name(t){var e;switch(t.output_name){case k2.TIME:return void this.handleTime(t);case k2.RESOLUTION:return void this.handle_resolution(t);case k2.MV_POSITION:return void this.handle_mvPosition(t);case k2.GL_POSITION:return void this.handle_gl_Position(t);case k2.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case k2.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,Do.FLOAT,t.output_name);t.globals_shader_name&&u.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)u.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),u.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Do.VEC2,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],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 Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],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,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let r of l){const l=t.glVarName(r),c=e.current_shader_name,u={globals_node:t,shaders_collection_controller:e,output_name:r,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:s,body_lines_by_shader_name:a};this.handle_globals_output_name(u)}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&&B2.includes(t)||i.push(t);return i}}const U2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const G2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var V2=\\\\\\\"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 H2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),j2=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const W2=new Map([]);W2.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return U2.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})}}),W2.set(D2.DEPTH,class extends z2{templateShader(){const t=V.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return G2.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})}}),W2.set(D2.DEPTH_DOF,class extends z2{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:V2,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return H2.get(t)}insert_body_after(t){return j2.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class q2 extends z2{custom_assembler_class_by_custom_name(){return W2}}class X2 extends q2{templateShader(){const t=V.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 Y2 extends q2{templateShader(){const t=V.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 $2 extends q2{templateShader(){const t=V.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 J2=\\\\\\\"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 Z2 extends q2{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",Z2.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?V.physical:V.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;\\\\\\\"),Z2.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=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"metalness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"roughness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"emissive\\\\\\\",Do.VEC3,[1,1,1])),Z2.USE_SSS&&e.push(new Vo(\\\\\\\"SSSModel\\\\\\\",Do.SSS_MODEL,J2)),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){const t=F2.create_variable_configs();return t.push(new A2(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new A2(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new A2(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),Z2.USE_SSS&&t.push(new A2(\\\\\\\"SSSModel\\\\\\\",{default:J2,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),t}}Z2.USE_SSS=!0;class Q2 extends Z2{isPhysical(){return!0}}const K2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),t3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const e3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),n3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const i3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),r3=new Map;r3.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.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 K2.get(t)}insert_body_after(t){return t3.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})}}),r3.set(D2.DEPTH_DOF,class extends z2{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:V2,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return e3.get(t)}insert_body_after(t){return n3.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 s3 extends z2{custom_assembler_class_by_custom_name(){return r3}templateShader(){const t=V.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=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"gl_PointSize\\\\\\\",Do.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return F2.create_globals_node_output_connections().concat([new Vo(k2.GL_POINTCOORD,Do.VEC2)])}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return F2.create_variable_configs().concat([new A2(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return i3.get(t)}}const o3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),a3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const l3=new Map([]);l3.set(D2.DEPTH_DOF,class extends z2{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:V2,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return o3.get(t)}insert_body_after(t){return a3.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 c3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class u3 extends z2{templateShader(){const t=V.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\\\\\\\",l3),l3}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(u3.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return u3.create_globals_node_output_connections()}create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return c3.get(t)}}class h3 extends F2{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 r of this._root_nodes)switch(r.type()){case UI.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._leaf_nodes)switch(r.type()){case HP.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}setup_shader_names_and_variables(){var t;const e=new O2(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 dQ,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 Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new dQ}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,r){var s;if(n){const n=t.variableForInput(e),o=uf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=r.current_shader_name;if(e&&(null===(s=e.allocation())||void 0===s?void 0:s.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;r.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const r=n;this.add_export_body_line(t,n,i,r,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const r=t.gl_type(),s=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,r,s,e),a=t.glVarName(t.output_name),l=`${r} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(s),u=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==u){const n=this.textureAllocationsController().variable(s);if(n){const i=`gl_FragColor.${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,Do.FLOAT,e);n.addDefinitions(t,[i]);const r=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[r]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,e,n);if(o){const i=`${s} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class d3 extends F2{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 Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}create_shader_configs(){return[new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"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 r;\\\\\\\"color\\\\\\\"==i&&(r=`diffuseColor.xyz = ${uf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(r=`diffuseColor.a = ${uf.any(n)}`),r&&e.addBodyLines(t,[r])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],r=[];for(let e of t.io.outputs.used_output_names()){const s=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":r.push(new Af(t,Do.FLOAT,e)),i.push(`float ${s} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,s)}}e.addDefinitions(t,r,n),e.addBodyLines(t,i)}}const p3=new Map([]);class _3 extends z2{custom_assembler_class_by_custom_name(){return p3}}const m3=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),f3=new Map([[xf.FRAGMENT,[]]]);class g3 extends _3{templateShader(){return{vertexShader:jB,fragmentShader:WB,uniforms:I.clone(qB)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return fs.add_user_data_render_hook(e,$B.render_hook.bind($B)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"density\\\\\\\",Do.FLOAT,1)])}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"pos_normalized\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return g3.create_globals_node_output_connections()}insert_body_after(t){return m3.get(t)}lines_to_remove(t){return f3.get(t)}create_shader_configs(){return[new T2(xf.VERTEX,[],[]),new T2(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{}),new A2(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return g3.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map;let l,c;for(let r of t.io.outputs.used_output_names()){const h=t.glVarName(r),d=e.current_shader_name;switch(r){case\\\\\\\"time\\\\\\\":l=new Af(t,Do.FLOAT,r),d&&u.pushOnArrayAtEntry(o,d,l),c=`float ${h} = ${r}`;for(let t of s)u.pushOnArrayAtEntry(o,t,l),u.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class v3{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(B_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(um,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Em,Kl),t.registerNode(Mm,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(Mg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(Bg,ic),t.registerNode(Jg,ic),t.registerNode(ev,ic),t.registerNode(ov,rc),t.registerNode(lv,rc),t.registerNode(dv,rc),t.registerNode(_v,rc),t.registerNode(gv,nc),t.registerNode(xv,ic),t.registerNode(Tv,nc),t.registerNode(Mv,sc),t.registerNode(Cv,sc),t.registerNode(Sv,sc),t.registerNode(Nv,sc),t.registerNode(Lv,sc),t.registerNode(Ov,sc)}}.run(t),class{static run(t){t.registerNode(Fv,cc),t.registerNode(Bv,lc),t.registerNode(Uv,lc),t.registerNode(Hv,lc),t.registerNode(Kv,oc),t.registerNode(dy,oc),t.registerNode(hy,oc),t.registerNode(_y,lc),t.registerNode(yl,ac),t.registerNode(DN,oc),t.registerNode(ol,ac),t.registerNode(UN,lc),t.registerNode(VN,lc),t.registerNode(QN,oc),t.registerNode(qa,ac),t.registerNode(nL,cc),t.registerNode(oL,lc),t.registerNode(Za,ac),t.registerNode(NL,lc),t.registerNode(el,cc),t.registerNode(PL,cc),t.registerNode(jL,cc),t.registerNode(qL,lc),t.registerNode($L,lc),t.registerNode(bl,ac),t.registerNode(ZL,lc),t.registerNode(hl,ac),t.registerNode(tO,uc),t.registerNode(eO,uc),t.registerNode(nO,uc),t.registerNode(iO,uc),t.registerNode(rO,uc),t.registerNode(sO,uc)}}.run(t),class{static run(t){t.registerNode(jO,gc),t.registerNode(zR,vc),t.registerNode(WO,xc),t.registerNode(AR,gc),t.registerNode(jR,xc),t.registerNode(LR,fc),t.registerNode(qO,xc),t.registerNode(XO,xc),t.registerNode(gf,_c,{except:[`${Ki.COP}/builder`]}),t.registerNode(fO,dc),t.registerNode(YO,gc),t.registerNode(xR,gc),t.registerNode(YR,hc),t.registerNode(eP,fc),t.registerNode(nP,gc),t.registerNode(sP,_c),t.registerNode($O,xc),t.registerNode(lP,pc),t.registerNode(cP,gc),t.registerNode(JO,dc),t.registerNode(dP,pc),t.registerNode(hR,pc),t.registerNode(ER,gc),t.registerNode(dR,pc),t.registerNode(xP,gc),t.registerNode(ZO,gc),t.registerNode(QO,gc),t.registerNode(bR,pc),t.registerNode(TP,gc),t.registerNode(EP,gc),t.registerNode(SP,gc),t.registerNode(NP,gc),t.registerNode(lO,dc),t.registerNode(vO,dc),t.registerNode(xO,dc),t.registerNode(wO,dc),t.registerNode(KO,gc),t.registerNode(RP,hc),t.registerNode(GP,fc),t.registerNode(tR,gc),t.registerNode(HP,_c),t.registerNode(WP,hc),t.registerNode(XP,hc),t.registerNode(JP,fc),t.registerNode(QP,bc),t.registerNode(_O,dc),t.registerNode(hO,dc),t.registerNode(eR,gc),t.registerNode(sI,pc),t.registerNode(oI,pc),t.registerNode(lI,hc),t.registerNode(nR,gc),t.registerNode(iR,gc),t.registerNode(pR,gc),t.registerNode(uI,gc),t.registerNode(_R,gc),t.registerNode(mR,gc),t.registerNode(mI,gc),t.registerNode(dI,gc),t.registerNode(SR,gc),t.registerNode(vI,gc),t.registerNode(yI,gc),t.registerNode(BI,bc),t.registerNode(kI,pc),t.registerNode(rR,gc),t.registerNode(OR,fc),t.registerNode(UI,_c),t.registerNode(HI,_c),t.registerNode(fR,gc),t.registerNode(YI,yc),t.registerNode(QI,yc),t.registerNode(KI,yc),t.registerNode(tF,yc),t.registerNode(iF,_c),t.registerNode(oF,_c),t.registerNode(sR,dc),t.registerNode(gR,pc),t.registerNode(jI,pc),t.registerNode(lF,hc),t.registerNode(gF,pc),t.registerNode(yF,gc),t.registerNode(oR,gc),t.registerNode(aR,xc),t.registerNode(wR,gc),t.registerNode(bF,pc),t.registerNode(lR,gc),t.registerNode(XI,mc),t.registerNode(vR,pc),t.registerNode(kP,fc),t.registerNode(TF,fc,VF),t.registerNode(IP,fc,VF),t.registerNode(MR,gc),t.registerNode(EF,fc),t.registerNode(cR,xc),t.registerNode(SF,hc),t.registerNode(OF,_c),t.registerNode(IF,fc),t.registerNode(Lf,_c),t.registerNode(kF,_c),t.registerNode(NO,dc),t.registerNode(IO,dc),t.registerNode(LO,dc),t.registerNode(PO,dc),t.registerNode(FO,dc),t.registerNode(OO,dc),t.registerNode(RO,dc),t.registerNode(zF,pc),t.registerNode(GF,pc)}}.run(t),class{static run(t){t.registerNode(qF,wc),t.registerNode(YF,wc),t.registerNode(JF,wc),t.registerNode(iD,wc)}}.run(t),class{static run(t){t.registerNode(dD,Ac),t.registerNode(AD,Ac),t.registerNode(ek,Ec),t.registerNode(ak,Tc),t.registerNode(pk,Ec),t.registerNode(yk,Tc),t.registerNode(Ik,Ec),t.registerNode(Uk,Ec),t.registerNode(jk,Tc),t.registerNode(tB,Ec),t.registerNode(rB,Tc),t.registerNode(lB,Ec),t.registerNode(dB,Tc),t.registerNode(AB,Ec),t.registerNode(LB,Ec),t.registerNode(IB,Sc),t.registerNode(kB,Tc),t.registerNode(UB,Tc),t.registerNode(HB,Ec),t.registerNode(QB,Cc),t.registerNode(ez,Cc),t.registerNode(rz,Mc),t.registerNode(sz,Mc),t.registerNode(oz,Mc),t.registerNode(az,Mc),t.registerNode(lz,Mc),t.registerNode(cz,Mc)}}.run(t),class{static run(t){t.registerNode(gz,Pc),t.registerNode(Uz,Pc),t.registerNode(Zz,Pc),t.registerNode(rU,Pc),t.registerNode(uU,Pc),t.registerNode(vU,Pc),t.registerNode(CU,Lc),t.registerNode(PU,Fc),t.registerNode(HU,Nc),t.registerNode(YU,Rc),t.registerNode(ZU,Fc),t.registerNode(nG,Fc),t.registerNode(NG,Nc),t.registerNode(jG,Lc),t.registerNode(YG,Fc),t.registerNode(ZG,Nc),t.registerNode(lH,Oc),t.registerNode(dH,Oc),t.registerNode(mH,Oc),t.registerNode(yH,Ic),t.registerNode(xH,Ic),t.registerNode(bH,Ic),t.registerNode(wH,Ic),t.registerNode(TH,Ic),t.registerNode(AH,Ic)}}.run(t),class{static run(t){t.registerNode(RH,Qc),t.registerNode(DH,Qc),t.registerNode(zH,Zc),t.registerNode(VH,Zc),t.registerNode(WH,Kc),t.registerNode(XH,Kc),t.registerNode(JH,Kc),t.registerNode(QH,Kc),t.registerNode(aj,Qc),t.registerNode(rj,Qc),t.registerNode(hj,Qc),t.registerNode(_j,Kc),t.registerNode(gj,Zc),t.registerNode(yj,Jc),t.registerNode(bj,Kc),t.registerNode(Sj,Kc),t.registerNode(Nj,Kc),t.registerNode(Oj,Kc),t.registerNode(Ij,Qc),t.registerNode(kj,Qc),t.registerNode(zj,Kc),t.registerNode(Vj,Qc),t.registerNode(Wj,Zc),t.registerNode(Xj,Kc),t.registerNode(Zj,Jc),t.registerNode(eW,Qc),t.registerNode(iW,Jc),t.registerNode(oW,Qc),t.registerNode(cW,tu),t.registerNode(uW,tu),t.registerNode(hW,tu),t.registerNode(dW,tu),t.registerNode(pW,tu),t.registerNode(_W,tu)}}.run(t),class{static run(t){t.registerNode(bW,Dc),t.registerNode(PV,Bc),t.registerNode(wW,kc),t.registerNode(gW,kc),t.registerNode(TW,kc),t.registerNode(AW,kc),t.registerNode(EW,kc),t.registerNode(MW,kc)}}.run(t),class{static run(t){t.registerOperation(SW),t.registerOperation(GW),t.registerOperation($W),t.registerOperation(KW),t.registerOperation(iq),t.registerOperation(gq),t.registerOperation(hq),t.registerOperation(wq),t.registerOperation(eX),t.registerOperation(sX),t.registerOperation(yY),t.registerOperation(AY),t.registerOperation(s$),t.registerOperation(A$),t.registerOperation(DJ),t.registerOperation(YJ),t.registerOperation(rZ),t.registerOperation(lZ),t.registerOperation(_Z),t.registerOperation(PZ),t.registerOperation(AZ),t.registerOperation(WZ),t.registerOperation(JZ),t.registerOperation(fQ),t.registerOperation(TQ),t.registerOperation(LQ),t.registerOperation(DQ),t.registerOperation(XQ),t.registerOperation(nK),t.registerOperation(pK),t.registerOperation(xK),t.registerOperation(AK),t.registerOperation(RK),t.registerOperation(GK),t.registerOperation(QK),t.registerOperation(h0),t.registerOperation(w0),t.registerOperation(H0),t.registerOperation(X0),t.registerOperation(Q0),t.registerOperation(n1),t.registerOperation(l1),t.registerOperation(S1),t.registerOperation(I1),t.registerOperation(B1),t.registerNode(LW,Hc),t.registerNode(RW,Uc),t.registerNode(UW,Uc),t.registerNode(jW,Gc),t.registerNode(QW,Gc),t.registerNode(nq,Gc),t.registerNode(aq,Gc),t.registerNode(cq,Gc),t.registerNode(_q,Gc),t.registerNode(xq,Gc),t.registerNode(Mq,Gc),t.registerNode(Cq,Gc),t.registerNode(Lq,Gc),t.registerNode(Dq,Gc),t.registerNode(Bq,qc),t.registerNode(Uq,qc),t.registerNode(rX,qc),t.registerNode(lX,Yc),t.registerNode(dY,Wc),t.registerNode(vY,Yc),t.registerNode(bY,Yc),t.registerNode(SY,Yc),t.registerNode(IY,Yc),t.registerNode(UY,qc),t.registerNode(HY,Yc),t.registerNode(e$,qc),t.registerNode(l$,Yc),t.registerNode(p$,Hc),t.registerNode(b$,Hc),t.registerNode(S$,Wc),t.registerNode(N$,Wc),t.registerNode(j$,qc),t.registerNode(q$,qc),t.registerNode(CJ,zc),t.registerNode(LJ,qc),t.registerNode(zJ,Hc),t.registerNode(GJ,qc),t.registerNode(WJ,Yc),t.registerNode(tZ,qc),t.registerNode(QJ,Wc),t.registerNode(aZ,Yc),t.registerNode(hZ,$c),t.registerNode(pZ,$c),t.registerNode(gZ,qc),t.registerNode(yZ,qc),t.registerNode(bZ,Yc),t.registerNode(TZ,zc),t.registerNode(SZ,$c),t.registerNode(RZ,zc),t.registerNode(kZ,Wc),t.registerNode(VZ,Wc),t.registerNode(jZ,qc),t.registerNode(XZ,Wc),t.registerNode($Z,Hc),t.registerNode(KZ,qc),t.registerNode(eQ,zc,{userAllowed:!1}),t.registerNode(mQ,Vc),t.registerNode(yQ,qc),t.registerNode(MQ,Yc),t.registerNode(zQ,qc),t.registerNode(NQ,qc),t.registerNode(PQ,jc),t.registerNode(EG,zc),t.registerNode(WQ,qc),t.registerNode(JQ,qc),t.registerNode(sK,$c),t.registerNode(dK,qc),t.registerNode(fK,Gc),t.registerNode(TK,Hc),t.registerNode(SK,qc),t.registerNode(BK,qc),t.registerNode(DK,qc),t.registerNode(qK,zc),t.registerNode(YK,zc),t.registerNode(jK,qc),t.registerNode(e0,Yc),t.registerNode(i0,qc),t.registerNode(_0,qc),t.registerNode(f0,Wc),t.registerNode(v0,Wc),t.registerNode(yG,Wc),t.registerNode(E0,Hc),t.registerNode(C0,Wc),t.registerNode(O0,Yc),t.registerNode(V0,Yc),t.registerNode(q0,qc),t.registerNode(J0,qc),t.registerNode(e1,Yc),t.registerNode(s1,Yc),t.registerNode(h1,qc),t.registerNode(p1,qc),t.registerNode(v1,qc),t.registerNode(w1,qc),t.registerNode(E1,Yc),t.registerNode(N1,qc),t.registerNode(P1,qc),t.registerNode(k1,qc),t.registerNode(U1,qc),t.registerNode(H1,Xc),t.registerNode(j1,Xc),t.registerNode(W1,Xc),t.registerNode(q1,Xc),t.registerNode(X1,Xc),t.registerNode(Y1,Xc)}}.run(t)}}.run(ai),class{static run(t){t.registerCamera(lH),t.registerCamera(dH)}}.run(ai),class{static run(t){t.expressionsRegister.register(J1,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(Z1,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(t2,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(e2,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(n2,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(i2,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(r2,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(s2,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(o2,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(a2,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(l2,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(c2,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(u2,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(h2,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(d2,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(p2,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(_2,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(m2,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(f2,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(g2,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(v2,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(y2,\\\\\\\"windowSize\\\\\\\")}}.run(ai),class{static run(t){t.assemblersRegister.register(Hn.GL_MESH_BASIC,b2,X2),t.assemblersRegister.register(Hn.GL_MESH_LAMBERT,b2,Y2),t.assemblersRegister.register(Hn.GL_MESH_PHONG,b2,$2),t.assemblersRegister.register(Hn.GL_MESH_STANDARD,b2,Z2),t.assemblersRegister.register(Hn.GL_MESH_PHYSICAL,b2,Q2),t.assemblersRegister.register(Hn.GL_PARTICLES,b2,h3),t.assemblersRegister.register(Hn.GL_POINTS,b2,s3),t.assemblersRegister.register(Hn.GL_LINE,b2,u3),t.assemblersRegister.register(Hn.GL_TEXTURE,b2,d3),t.assemblersRegister.register(Hn.GL_VOLUME,b2,g3)}}.run(ai))}}v3._started=!1,v3.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$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":"2809283"}},"type":2,"external":true,"timestamp":1723910879016},{"data":{"url":"blob:https://ipfs.arkivo.art/b5dae44e-23b3-431b-8f84-30c79de93512","host":"","path":"https://ipfs.arkivo.art/b5dae44e-23b3-431b-8f84-30c79de93512","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":1723910879019},{"data":{"url":"blob:https://ipfs.arkivo.art/b5dae44e-23b3-431b-8f84-30c79de93512","body":"\"// src/polygonjs/PolyConfig.js\\nfunction configurePolygonjs(poly) {\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n 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