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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":1723910736347},{"data":{"url":"blob:https://ipfs.arkivo.art/e2febef6-687f-46ea-a6a1-f2eb5fd8442a","host":"","path":"https://ipfs.arkivo.art/e2febef6-687f-46ea-a6a1-f2eb5fd8442a","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":1723910736351},{"data":{"url":"blob:https://ipfs.arkivo.art/e2febef6-687f-46ea-a6a1-f2eb5fd8442a","body":"\"// src/polygonjs/PolyConfig.js\\nfunction configurePolygonjs(poly) {\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n 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","consoleMessages":[{"text":"Unrecognized Content-Security-Policy directive 'prefetch-src'.","level":"error","timestamp":1723910735605},{"text":"brightness Bm","level":"log","timestamp":1723910736622},{"text":"[.WebGL-0x109001c42a00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910747681},{"text":"[.WebGL-0x109001c42a00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910747683},{"text":"[.WebGL-0x109001c42a00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910747683},{"text":"[.WebGL-0x109001c42a00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723910747683}],"screenshotDelay":10000},"timestamp":1723910735097},"created_at":"2024-08-17T16:06:09.403+00:00","updated_at":"2024-08-17T16:06:09.403+00:00"}