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Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=r.set(i[0],i[1],i[2]).length();const a=r.set(i[4],i[5],i[6]).length(),l=r.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/s,h=1/a,u=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=h,o.elements[5]*=h,o.elements[6]*=h,o.elements[8]*=u,o.elements[9]*=u,o.elements[10]*=u,e.setFromRotationMatrix(o),n.x=s,n.y=a,n.z=l,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}s.prototype.isMatrix4=!0;const r=new i.a,o=new s,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,h=new i.a,u=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(3);const s={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},r={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class h{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=a(s,i,t+1/3),this.g=a(s,i,t),this.b=a(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=s[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new u.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}s.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),s=n(1),r=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=r.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=s.xb,this.side=s.H,this.vertexColors=!1,this.opacity=1,this.format=s.Ib,this.transparent=!1,this.blendSrc=s.Nc,this.blendDst=s.Db,this.blendEquation=s.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=s.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=s.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=s.R,this.stencilZFail=s.R,this.stencilZPass=s.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===s.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==s.xb&&(n.blending=this.blending),this.side!==s.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==s.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(28);class s{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,s){n.load(t,i,e,s)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),s=n(2),r=n(18),o=n(39),a=n(5),l=n(10),c=n(40),h=n(1),u=n(29),d=n(7);const p=new a.a,_=new o.a,m=new r.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,M=new s.a,E=new s.a,S=new s.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new u.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(s),!1===t.ray.intersectsSphere(m))return;if(p.copy(s).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,r,f,g,v,C);if(O){u&&(M.fromBufferAttribute(u,p),E.fromBufferAttribute(u,_),S.fromBufferAttribute(u,m),O.uv=c.a.getUV(C,f,g,v,M,E,S,new s.a)),d&&(M.fromBufferAttribute(d,p),E.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,M,E,S,new s.a));const t={a:p,b:_,c:m,normal:new 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l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,s)}intersectTriangle(t,e,n,i,s){l.subVectors(e,t),c.subVectors(n,t),h.crossVectors(l,c);let r,o=this.direction.dot(h);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}a.subVectors(this.origin,t);const u=r*this.direction.dot(c.crossVectors(a,c));if(u<0)return null;const d=r*this.direction.dot(l.cross(a));if(d<0)return null;if(u+d>o)return null;const p=-r*a.dot(h);return p<0?null:this.at(p/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const s=new i.a,r=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,h=new i.a,u=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),s.subVectors(t,e),i.cross(s);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){s.subVectors(i,e),r.subVectors(n,e),o.subVectors(t,e);const l=s.dot(s),c=s.dot(r),h=s.dot(o),u=r.dot(r),d=r.dot(o),p=l*u-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(u*h-c*d)*_,f=(l*d-c*h)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,s,r,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(s,a.x),l.addScaledVector(r,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return s.subVectors(n,e),r.subVectors(t,e),s.cross(r).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return s.subVectors(this.c,this.b),r.subVectors(this.a,this.b),.5*s.cross(r).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return _.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;l.subVectors(i,n),c.subVectors(s,n),u.subVectors(t,n);const a=l.dot(u),_=c.dot(u);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return r=a/(a-m),e.copy(n).addScaledVector(l,r);p.subVectors(t,s);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(s);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return h.subVectors(s,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(h,o);const w=1/(b+x+g);return r=x*w,o=g*w,e.copy(n).addScaledVector(l,r).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use 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i=t.ray.origin.distanceTo(d);it.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. 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l{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((t,e)=>{console.log(e,t,t.graphPredecessors(),t.graphSuccessors())}))}setScene(t){this._scene=t}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(t){const e=[];for(let n of t){const t=this.nodeFromId(n);t&&e.push(t)}return e}nodeFromId(t){return this._nodes_by_id.get(t)}hasNode(t){return null!=this._nodes_by_id.get(t.graphNodeId())}addNode(t){this._nodes_by_id.set(t.graphNodeId(),t),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(t.graphNodeId(),t)}removeNode(t){this._nodes_by_id.delete(t.graphNodeId()),this._successors.delete(t.graphNodeId()),this._predecessors.delete(t.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(t.graphNodeId())}nodesCount(){return this._nodesCount}connect(t,e,n=!0){const i=t.graphNodeId(),s=e.graphNodeId();if(this.hasNode(t)&&this.hasNode(e)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let e=!1;return n&&(e=this._hasPredecessor(i,s)),!e&&(this._createConnection(i,s),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${s}`),!1}disconnect(t,e){this._remove_connection(t.graphNodeId(),e.graphNodeId()),t.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(t){const e=this.predecessors(t);for(let n of e)this.disconnect(n,t)}disconnectSuccessors(t){const e=this.successors(t);for(let n of e)this.disconnect(t,n)}predecessorIds(t){const e=this._predecessors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}predecessors(t){const e=this.predecessorIds(t.graphNodeId());return this.nodesFromIds(e)}successorIds(t){const e=this._successors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}successors(t){const e=this.successorIds(t.graphNodeId())||[];return this.nodesFromIds(e)}allPredecessorIds(t){return this.allNextIds(t,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(t){return this.allNextIds(t,\\\\\\\"successorIds\\\\\\\")}allPredecessors(t){const e=this.allPredecessorIds(t);return this.nodesFromIds(e)}allSuccessors(t){const e=this.allSuccessorIds(t);return this.nodesFromIds(e)}_createConnection(t,e){let n=this._successors.get(t);if(n||(n=new Set,this._successors.set(t,n)),n.has(e))return;n.add(e);let i=this._predecessors.get(e);i||(i=new Set,this._predecessors.set(e,i)),i.add(t)}_remove_connection(t,e){let n=this._successors.get(t);n&&(n.delete(e),0==n.size&&this._successors.delete(t));let i=this._predecessors.get(e);i&&(i.delete(t),0==i.size&&this._predecessors.delete(e))}allNextIds(t,e){const n=new Map,i=[];let s=this[e](t.graphNodeId());for(;s.length>0;){const t=[];for(let n of s)for(let i of this[e](n))t.push(i);for(let t of s)n.set(t,!0);for(let e of t)s.push(e);s=t}return n.forEach(((t,e)=>{i.push(e)})),i}_hasPredecessor(t,e){const n=this.predecessorIds(t);if(n){if(n.includes(e))return!0;for(let t of n)return this._hasPredecessor(t,e)}return!1}}class c{constructor(t){this._node=t,this._cooks_count=0,this._total_cook_time=0,this._total_inputs_time=0,this._total_params_time=0}update_cook_data(t){this._cooks_count+=1,this._total_cook_time+=t.cookTime,this._total_inputs_time+=t.inputsTime,this._total_params_time+=t.paramsTime}total_time(){return this._total_cook_time+this._total_inputs_time+this._total_params_time}total_cook_time(){return this._total_cook_time}cook_time_per_iteration(){return this._cooks_count>0?this._total_cook_time/this._cooks_count:0}total_inputs_time(){return this._total_inputs_time}inputs_time_per_iteration(){return this._cooks_count>0?this._total_inputs_time/this._cooks_count:0}total_params_time2(){return this._total_params_time}params_time_per_iteration2(){return this._cooks_count>0?this._total_params_time/this._cooks_count:0}cooks_count(){return 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u.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return u.difference(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static toSet(t){const e=new Set;for(let n of t)e.add(n);return e}static isEqual(t,e){if(t.length!=e.length)return!1;const n=t.length;for(let i=0;i{s[r]=t,r++})),m.isString(s[0])?s.sort():s.sort(((t,e)=>t-e));const o=new Array(t.length);r=0;for(let t of s){const e=n.get(t);if(e)for(let t of e)o[r]=t,r++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),s=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),h.push(a/f),h.push(1-r/g),E+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}F.prototype.isShaderMaterial=!0;var D=n(6),B=n(14),z=\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\\\n\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\\\n\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\\\n\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t// y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim := ( 4 , 2 )\\\\n\\\\t\\\\t// # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",k=\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\";const U={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:z,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:k,transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\n\\\\t// modulation\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshnormal_vert:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",meshnormal_frag:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\nuniform float opacity;\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n#endif\\\\n}\\\\n\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};var G=n(11);const V={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new G.a},uv2Transform:{value:new G.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}}},H={basic:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.fog]),vertexShader:U.meshbasic_vert,fragmentShader:U.meshbasic_frag},lambert:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshlambert_vert,fragmentShader:U.meshlambert_frag},phong:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.fog,V.lights,{emissive:{value:new D.a(0)},specular:{value:new D.a(1118481)},shininess:{value:30}}]),vertexShader:U.meshphong_vert,fragmentShader:U.meshphong_frag},standard:{uniforms:R([V.common,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.roughnessmap,V.metalnessmap,V.fog,V.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag},toon:{uniforms:R([V.common,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.gradientmap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshtoon_vert,fragmentShader:U.meshtoon_frag},matcap:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,V.fog,{matcap:{value:null}}]),vertexShader:U.meshmatcap_vert,fragmentShader:U.meshmatcap_frag},points:{uniforms:R([V.points,V.fog]),vertexShader:U.points_vert,fragmentShader:U.points_frag},dashed:{uniforms:R([V.common,V.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:U.linedashed_vert,fragmentShader:U.linedashed_frag},depth:{uniforms:R([V.common,V.displacementmap]),vertexShader:U.depth_vert,fragmentShader:U.depth_frag},normal:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,{opacity:{value:1}}]),vertexShader:U.meshnormal_vert,fragmentShader:U.meshnormal_frag},sprite:{uniforms:R([V.sprite,V.fog]),vertexShader:U.sprite_vert,fragmentShader:U.sprite_frag},background:{uniforms:{uvTransform:{value:new G.a},t2D:{value:null}},vertexShader:U.background_vert,fragmentShader:U.background_frag},cube:{uniforms:R([V.envmap,{opacity:{value:1}}]),vertexShader:U.cube_vert,fragmentShader:U.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:U.equirect_vert,fragmentShader:U.equirect_frag},distanceRGBA:{uniforms:R([V.common,V.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:U.distanceRGBA_vert,fragmentShader:U.distanceRGBA_frag},shadow:{uniforms:R([V.lights,V.fog,{color:{value:new D.a(0)},opacity:{value:1}}]),vertexShader:U.shadow_vert,fragmentShader:U.shadow_frag}};function j(t,e,n,i,s){const r=new D.a(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===w.q)?(void 0===a&&(a=new B.a(new N(1,1,1),new F({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:P(H.cube.uniforms),vertexShader:H.cube.vertexShader,fragmentShader:H.cube.fragmentShader,side:w.i,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new B.a(new L(2,2),new F({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:P(H.background.uniforms),vertexShader:H.background.vertexShader,fragmentShader:H.background.fragmentShader,side:w.H,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function W(t,e,n,i){const s=t.getParameter(t.MAX_VERTEX_ATTRIBS),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.HIGH_FLOAT).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(t.VERTEX_SHADER,t.MEDIUM_FLOAT).precision>0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.MEDIUM_FLOAT).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS),u=t.getParameter(t.MAX_VERTEX_TEXTURE_IMAGE_UNITS),d=t.getParameter(t.MAX_TEXTURE_SIZE),p=t.getParameter(t.MAX_CUBE_MAP_TEXTURE_SIZE),_=t.getParameter(t.MAX_VERTEX_ATTRIBS),m=t.getParameter(t.MAX_VERTEX_UNIFORM_VECTORS),f=t.getParameter(t.MAX_VARYING_VECTORS),g=t.getParameter(t.MAX_FRAGMENT_UNIFORM_VECTORS),v=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(t.MAX_SAMPLES):0}}H.physical={uniforms:R([H.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new d.a(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new D.a(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new d.a},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new D.a(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new D.a(1,1,1)},specularTintMap:{value:null}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag};var Y=n(34);function $(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new Y.a,a=new G.a,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new N(5,5,5),s=new F({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:P(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});s.uniforms.tEquirect.value=e;const r=new B.a(i,s),o=e.minFilter;e.minFilter===w.Y&&(e.minFilter=w.V);return new nt(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}function rt(t){let e=new WeakMap;function n(t,e){return e===w.D?t.mapping=w.o:e===w.E&&(t.mapping=w.p),t}function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping;if(r===w.D||r===w.E){if(e.has(s)){return n(e.get(s).texture,s.mapping)}{const r=s.image;if(r&&r.height>0){const o=t.getRenderTarget(),a=new st(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}st.prototype.isWebGLCubeRenderTarget=!0;var ot=n(37);class at extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}at.prototype.isRawShaderMaterial=!0;var lt=n(29);const ct=Math.pow(2,8),ht=[.125,.215,.35,.446,.526,.582],ut=5+ht.length,dt=20,pt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},_t=new ot.a,{_lodPlanes:mt,_sizeLods:ft,_sigmas:gt}=Mt(),vt=new D.a;let yt=null;const xt=(1+Math.sqrt(5))/2,bt=1/xt,wt=[new p.a(1,1,1),new p.a(-1,1,1),new p.a(1,1,-1),new p.a(-1,1,-1),new p.a(0,xt,bt),new p.a(0,xt,-bt),new p.a(bt,0,xt),new p.a(-bt,0,xt),new p.a(xt,bt,0),new p.a(-xt,bt,0)];class Tt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new at({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}(dt),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){yt=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Nt(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Ct(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?ct:0,ct,ct),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=pt[w.U]:t.value=pt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Nt()):null==this._equirectShader&&(this._equirectShader=Ct());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new B.a(mt[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),St(e,0,0,3*ct,2*ct),n.setRenderTarget(e),n.render(s,_t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;edt&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,_t)}}function At(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function Mt(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=ht[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Q(3*ct,3*ct,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function St(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function Ct(){const t=new d.a(1,1);return new at({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return new at({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Pt(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===w.D||r===w.E,a=r===w.o||r===w.p;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const s=n.morphAttributes;for(const n in s){const i=s[n];for(let n=0,s=i.length;ne.maxTextureSize&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const p=new Float32Array(h*u*4*i),_=new zt(p,h,u,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const s=e*n;let r=Jt[s];if(void 0===r&&(r=new Float32Array(s),Jt[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function ne(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function on(t){return t.replace(rn,an)}function an(t,e){const n=U[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return on(n)}const ln=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,cn=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function hn(t){return t.replace(cn,dn).replace(ln,un)}function un(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),dn(t,e,n,i)}function dn(t,e,n,i){let s=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.skinning?\\\\\\\"#define USE_SKINNING\\\\\\\":\\\\\\\"\\\\\\\",n.useVertexTexture?\\\\\\\"#define BONE_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets?\\\\\\\"#define USE_MORPHTARGETS\\\\\\\":\\\\\\\"\\\\\\\",n.morphNormals&&!1===n.flatShading?\\\\\\\"#define USE_MORPHNORMALS\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\"+n.morphTargetsCount:\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.sizeAttenuation?\\\\\\\"#define USE_SIZEATTENUATION\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoat?\\\\\\\"#define USE_CLEARCOAT\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",n.toneMapping!==w.vb?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?U.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?tn(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===w.ic?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",U.encodings_pars_fragment,n.map?Qe(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Qe(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Qe(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Qe(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?Qe(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Qe(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Ke(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")),o=on(o),o=nn(o,n),o=sn(o,n),a=on(a),a=nn(a,n),a=sn(a,n),o=hn(o),a=hn(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+f+o,x=v+g+a,b=Ye(s,s.VERTEX_SHADER,y),T=Ye(s,s.FRAGMENT_SHADER,x);if(s.attachShader(m,b),s.attachShader(m,T),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(b).trim(),n=s.getShaderInfoLog(T).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,s.LINK_STATUS)){i=!1;const e=Ze(s,b,\\\\\\\"vertex\\\\\\\"),n=Ze(s,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,s.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let A,M;return s.deleteShader(b),s.deleteShader(T),this.getUniforms=function(){return void 0===A&&(A=new Xe(s,m)),A},this.getAttributes=function(){return void 0===M&&(M=function(t,e){const n={},i=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES);for(let s=0;s0,N=r.clearcoat>0;return{isWebGL2:l,shaderID:T,shaderName:r.type,vertexShader:M,fragmentShader:E,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==S?f(S.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===w.q||b.mapping===w.r),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:r.normalMapType===w.zb,tangentSpaceNormalMap:r.normalMapType===w.Uc,clearcoat:N,clearcoatMap:N&&!!r.clearcoatMap,clearcoatRoughnessMap:N&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:N&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:C,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&A>0,maxBones:A,useVertexTexture:h,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:w.vb,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:r.side===w.z,flipSided:r.side===w.i,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||gn),s.length>1&&s.sort(e||vn),r.length>1&&r.sort(e||vn)}}}function xn(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new yn(t),e.set(n,[s])):i>=e.get(n).length?(s=new yn(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function bn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let wn=0;function Tn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function An(t,e){const n=new bn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new p.a);const r=new p.a,o=new A.a,a=new A.a;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(Tn);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_FLOAT_1,s.rectAreaLTC2=V.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_HALF_1,s.rectAreaLTC2=V.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=wn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new Mn(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Sn.prototype.isMeshDepthMaterial=!0;class Cn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Cn.prototype.isMeshDistanceMaterial=!0;function Nn(t,e,n){let i=new T.a;const s=new d.a,r=new d.a,o=new _.a,a=new Sn({depthPacking:w.Hb}),l=new Cn,c={},h=n.maxTextureSize,u={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new B.a(f,p),v=this;function y(n,i){const s=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,m,g,null)}function x(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,h===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:u[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function b(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new Q(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),b(n,a,u.camera,c,this.type)}u.isPointLightShadow||this.type!==w.gd||y(u,a),u.needsUpdate=!1}v.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function Ln(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new _.a;let i=null;const s=new _.a(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(e){e?k(t.DEPTH_TEST):U(t.DEPTH_TEST)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case w.tb:t.depthFunc(t.NEVER);break;case w.g:t.depthFunc(t.ALWAYS);break;case w.S:t.depthFunc(t.LESS);break;case w.T:t.depthFunc(t.LEQUAL);break;case w.C:t.depthFunc(t.EQUAL);break;case w.L:t.depthFunc(t.GEQUAL);break;case w.K:t.depthFunc(t.GREATER);break;case w.yb:t.depthFunc(t.NOTEQUAL);break;default:t.depthFunc(t.LEQUAL)}else t.depthFunc(t.LEQUAL);i=e}},setLocked:function(t){e=t},setClear:function(e){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(n){e||(n?k(t.STENCIL_TEST):U(t.STENCIL_TEST))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,T=null,A=null,M=null,E=null;const S=t.getParameter(t.MAX_COMBINED_TEXTURE_IMAGE_UNITS);let C=!1,N=0;const L=t.getParameter(t.VERSION);-1!==L.indexOf(\\\\\\\"WebGL\\\\\\\")?(N=parseFloat(/^WebGL (\\\\d)/.exec(L)[1]),C=N>=1):-1!==L.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(N=parseFloat(/^OpenGL ES (\\\\d)/.exec(L)[1]),C=N>=2);let O=null,P={};const R=t.getParameter(t.SCISSOR_BOX),I=t.getParameter(t.VIEWPORT),F=(new _.a).fromArray(R),D=(new _.a).fromArray(I);function B(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(e,t.TEXTURE_MAG_FILTER,t.NEAREST);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?On.g:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to 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extends J.a{constructor(t,e){super();const n=this,i=t.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new tt.a;A.layers.enable(1),A.viewport=new _.a;const E=new tt.a;E.layers.enable(2),E.viewport=new _.a;const S=[A,E],C=new In;C.layers.enable(1),C.layers.enable(2);let N=null,L=null;function O(t){const e=T.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function P(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),h=null,f=null,g=null,v=null,d=null,u=null,c=null,s=null,z.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function R(t){const e=s.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const s=t.get(i).__maxMipLevel;void 0!==s&&(e.maxMipLevel.value=s)}let s,r;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap?s=n.clearcoatRoughnessMap:n.specularIntensityMap?s=n.specularIntensityMap:n.specularTintMap?s=n.specularTintMap:n.transmissionMap?s=n.transmissionMap:n.thicknessMap&&(s=n.thicknessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function Un(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(It.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,P=e.width,R=e.height,I=1,F=null,D=null;const B=new _.a(0,0,P,R),z=new _.a(0,0,P,R);let k=!1;const U=[],G=new T.a;let V=!1,H=!1,Y=null;const J=new A.a,Z=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ht,ut,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Mt=n;function Et(t,n){for(let i=0;i0&&function(t,e,n){if(null===Y){const t=!0===o&&!0===nt.isWebGL2;Y=new(t?Ht:Q)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget(Y),g.clear();const s=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=s,at.updateMultisampleRenderTarget(Y),at.updateRenderTargetMipmap(Y),g.setRenderTarget(i)}(s,e,n),i&&it.viewport(N.copy(i)),s.length>0&&Xt(s,e,n),r.length>0&&Xt(r,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?f[f.length-1]:null,m.pop(),u=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 0===ot.get(t).__webglFramebuffer&&at.setupRenderTarget(t);let i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(B).multiplyScalar(I).floor(),L.copy(z).multiplyScalar(I).floor(),O=k;if(it.bindFramebuffer(Mt.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Mt.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&Mt.readPixels(e,n,i,s,Tt.convert(a),Tt.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Mt.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),s=Math.floor(e.image.width*i),r=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Mt.RGB&&(o=Mt.RGB8),o===Mt.RGBA&&(o=Mt.RGBA8)),at.setTexture2D(e,0),Mt.copyTexImage2D(Mt.TEXTURE_2D,n,o,t.x,t.y,s,r,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,n.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?Mt.compressedTexSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Mt.generateMipmap(Mt.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let h;if(i.isDataTexture3D)at.setTexture3D(i,0),h=Mt.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),h=Mt.TEXTURE_2D_ARRAY}Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,i.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,i.unpackAlignment);const u=Mt.getParameter(Mt.UNPACK_ROW_LENGTH),d=Mt.getParameter(Mt.UNPACK_IMAGE_HEIGHT),p=Mt.getParameter(Mt.UNPACK_SKIP_PIXELS),_=Mt.getParameter(Mt.UNPACK_SKIP_ROWS),m=Mt.getParameter(Mt.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,f.width),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,f.height),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,t.min.x),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,t.min.y),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Mt.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,f.data)):Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f),Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,u),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,d),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,p),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,_),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,m),0===s&&i.generateMipmaps&&Mt.generateMipmap(h),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Gn={};var Vn,Hn,jn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Vn||(Vn={}));class Wn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Vn.WEBGL2))}createWebGLRenderer(t){const e=new Un(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Vn.WEBGL2:Vn.WEBGL;let i=t.getContext(n,Gn);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Vn.EXPERIMENTAL_WEBGL2:Vn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Gn)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Ht(t,e,n):new Q(t,e,n)}}class qn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class Xn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(t,e,n){const i=t.context(),s=t.type().toLowerCase();let r=this._node_register.get(i);r||(r=new Map,this._node_register.set(i,r));if(r.get(s))console.error(`node ${i}/${s} already registered`);else{if(r.set(s,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(s,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(s,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,s;null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(s=this._node_register_options.get(t))||void 0===s||s.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&null!=n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const s=n.type().toLowerCase(),r=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(s);if(r){const n=r.only,i=r.except,s=`${t}/${e}`;return n?n.includes(s):!i||!i.includes(s)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Yn{constructor(t){this.poly=t,this._operation_register=new Map}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=t.type().toLowerCase();if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return n.get(e.toLowerCase())}}class $n extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(jn||(jn={}));class Zn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Qn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Kn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var ti=n(86);class ei{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,li.playerMode()),li.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(ti.a)(t)}}class ni{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],s=this._map[n];return s?i?`${s}?${i}`:s:void 0}}var ii=n(91),si=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:s,unzippedData:r}=e,o=Object.keys(s);for(let t of o){const e=r[`assets/${s[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:li.blobs.createBlobUrl(n)};li.blobs.registerBlobUrl(i)}li.setPlayerMode(!0),li.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=li;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,r)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[si.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let s=document.getElementById(t.scriptElementId);const r=[];return r.push(`import {configurePolygonjs, configureScene} from '${i}';`),r.push(`configurePolygonjs(window.${t.Poly});`),r.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),r.push(`delete window.${t.loadSceneArgs};`),s||(s=document.createElement(\\\\\\\"script\\\\\\\"),s.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),s.text=r.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(s)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const s=new n(e),r=await s.scene();i&&i(r);const o=r.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);r.play(),t.scene=r,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ii.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(ti.a)(i)}}class oi{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class ai{constructor(){this.renderersController=new Wn,this.nodesRegister=new Xn(this),this.operationsRegister=new Yn(this),this.expressionsRegister=new $n,this.modulesRegister=new Jn,this.assemblersRegister=new Zn,this.pluginsRegister=new Qn(this),this.camerasRegister=new Kn(this),this.blobs=new ei,this.assetUrls=new ni,this.selfContainedScenesLoader=new ri,this.performance=new oi,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new ai;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new qn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const li=ai._instance_();class ci{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=li.performance.performanceManager(),i=n.now();e();const s=n.now()-i;console.log(`${t}: ${s}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=li.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const s=t[e];n.push(s),null==i[s]&&(i[s]=[]),i[s].push(e)}n.sort(((t,e)=>t-e));const s=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const r=[];for(let t of s){const n=i[t];for(let i of n){const n=e[i],s={duration:t,name:i,count:n,duration_per_iteration:t/n};r.push(s)}}return console.table(r),r}}class hi{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class di{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class pi{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var _i;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(_i||(_i={}));const mi=[_i.LOADED,_i.PLAY,_i.PAUSE,_i.TICK];class fi extends pi{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}}class gi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(_i.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolve_missing_references(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const vi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=bi.findNode(t,this._path)}clone(){const t=new yi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class xi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=bi.findParam(t,this._path)}clone(){const t=new xi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class bi{static split_parent_child(t){const e=t.split(bi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(bi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR).filter((t=>t.length>0)),s=i[0];let r=null;if(e[0]!==bi.SEPARATOR){switch(s){case bi.PARENT:null==n||n.add_path_element(s),r=t.parent();break;case bi.CURRENT:null==n||n.add_path_element(s),r=t;break;default:r=t.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const t=i.slice(1).join(bi.SEPARATOR);r=this.findNode(r,t,n)}return r}{const i=e.substr(1);r=this.findNode(t.root(),i,n)}return r}static findParam(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(bi.SEPARATOR),s=this.findNode(t,e,n);if(null!=s){const t=i[i.length-1],e=s.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let s=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(bi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)r[l]||a.push(t),l++;return`${s}${a.join(bi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),s=Math.min(n.length,i.length);let r=null;for(let t=0;t{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();if(e){if(this.scene.node(e))return!0;{const t=bi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}}class Ai{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=li.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Mi{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ai(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Si{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Ci{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ni extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Li extends Ci{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Oi extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Pi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Li(this.node)}hasDisplay(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ni(this.node)}hasBypass(){return!0}}}function Fi(t){return class extends t{constructor(){super(...arguments),this.optimize=new Oi(this.node)}hasOptimize(){return!0}}}class Di extends(Ri(Pi)){}class Bi extends(Ii(Pi)){}class zi extends(Ii(Ri(Pi))){}class ki extends(Fi(Ii(Pi))){}class Ui extends(Fi(Ii(Ri(Pi)))){}class Gi{constructor(t){this.node=t}}class Vi extends Gi{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Hi extends Gi{set(t){this._message!=t&&(t&&li.warn(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class ji{constructor(t){this.node=t,this.timeDependent=new Vi(this.node),this.error=new Hi(this.node)}}class Wi{constructor(t){this.node=t,this._graph_node=new Mi(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class qi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(Wi.base_name(this.node)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(t){const e=bi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==bi.CURRENT||t==bi.CURRENT_WITH_SLASH)return this.node;if(t==bi.PARENT||t==bi.PARENT_WITH_SLASH)return this.node.parent();const e=bi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return bi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const Xi=/[, ]/,Yi=/\\\\d+$/,$i=/^0+/,Ji=/,| /,Zi=/^-?\\\\d+\\\\.?\\\\d*$/;var Qi,Ki,ts,es,ns,is,ss,rs;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Qi||(Qi={}));class os{static isBoolean(t){return t==Qi.TRUE||t==Qi.FALSE}static toBoolean(t){return t==Qi.TRUE}static isNumber(t){return Zi.test(t)}static tailDigits(t){const e=t.match(Yi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Yi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const s=n.match($i);s&&(i=s[0]);const r=parseInt(n);0==r&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${r+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)os.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(Xi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let s=0;return n.forEach((t=>{i[s]=t,s++})),i}static indices(t){const e=t.split(Ji);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class as{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Ki||(Ki={}));class ls{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class cs{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=1:e;if(m.isString(e)){if(n){if(n instanceof yi)return n.set_path(e);if(n instanceof xi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new hs(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t{n[i]=t[e]}));for(let i=0;i=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class ps{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(ts||(ts={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(es||(es={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(ns||(ns={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(is||(is={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ss||(ss={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.PLAYER=\\\\\\\"playerControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rs||(rs={}));const _s=[rs.DEVICE_ORIENTATION,rs.MAP,rs.ORBIT,rs.FIRST_PERSON,rs.MOBILE_JOYSTICK];class ms{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var fs=n(69);class gs extends K.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}gs.prototype.isScene=!0;var vs=n(49),ys=n(52),xs=n(42),bs=n(55),ws=n(62),Ts=n(24),As=n(35);const Ms=new p.a,Es=new p.a;class Ss extends K.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t0){let n,i;for(n=1,i=e.length;n0){Ms.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ms);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ms.setFromMatrixPosition(t.matrixWorld),Es.setFromMatrixPosition(this.matrixWorld);const n=Ms.distanceTo(Es)/t.zoom;let i,s;for(e[0].object.visible=!0,i=1,s=e.length;i=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;in?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,s){return(t-e)/(n-e)*(s-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*sr}static radians_to_degrees(t){return t/sr}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),s=this.deg2rad(e.lat-t.lat),r=this.deg2rad(e.lng-t.lng),o=Math.sin(s/2)*Math.sin(s/2)+Math.cos(n)*Math.cos(i)*Math.sin(r/2)*Math.sin(r/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}rr.Easing=ir,rr.fract=t=>t-Math.floor(t),rr._vec={x:0,y:136574},rr._triangle_mid=new p.a,rr._triangle_mid_to_corner=new p.a;class or{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Qs(this._core_geometry,e[n+0]),new Qs(this._core_geometry,e[n+1]),new Qs(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new Ks.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}random_position(t){let e=[rr.randFloat(t),rr.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attrib_value_at_position(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),s=this._geometry.attributes[t].itemSize,r=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(s){case 1:a=0;for(let t of r)a+=t*i[l],l++;o=a;break;default:for(let t of r){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolated_value(t,e,n,i){const s=[e.a,e.b,e.c],r=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=s.map((t=>new p.a(r[3*t+0],r[3*t+1],r[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=s.map((t=>l[t]));break;case 2:c=s.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=s.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const h=s.map(((t,e)=>n.distanceTo(o[e]))),u=f.sum([h[0]*h[1],h[0]*h[2],h[1]*h[2]]),_=[h[1]*h[2]/u,h[0]*h[2]/u,h[0]*h[1]/u];let m;switch(a){case 1:m=f.sum(s.map(((t,e)=>_[e]*c[e])));break;default:var g=s.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class ar{from_points(t){t=this._filter_points(t);const e=new S.a,n=new _r(e),i=t[0];if(null!=i){const s=i.geometry(),r=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),r={};s.forEach(((t,e)=>r[t]=e)),n.userDataAttribs()[i]=s;const o=[];for(let e of t){const t=r[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,s=new Array(t.length*n);switch(n){case 1:for(let e=0;enew _r(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},s=[];for(let i of e){const e=i.points();for(let i of e){s.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const r=Object.keys(n);for(let e of s){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=r}const s=hr(t),r=new _r(s);return Object.keys(i).forEach((t=>{const e=i[t];r.setIndexedAttributeValues(t,e)})),s&&delete s.userData.mergedUserData,s}}class _r{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return _r.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=qs.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=qs.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=qs.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Bs.STRING:Bs.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return _r.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=os.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)os.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=qs.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let s=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew or(this,t)))}}var mr;_r._mesh_builder=new class extends ar{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],s=i.length;let r,o,a;for(let t=0;t{};class gr{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,s,r,o,a)=>{n(e,i,s,r,o,a,t)})}t.onBeforeRender=fr}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,s=n.customMaterials[i];s&&(t[i]=s,s.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e]=r.uniforms[e]||n)}}}const vr=\\\\\\\"name\\\\\\\";class yr extends Xs{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new _r(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=os.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),s=i[0];if(\\\\\\\"@\\\\\\\"==s[0]){const t=s.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,s=t.userData;s.attributes=s.attributes||{},s.attributes[e]=i}addAttribute(t,e){yr.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=qs.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==vr)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==vr?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return yr.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return yr.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(vr)}humanType(){return Hs.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?Bs.STRING:Bs.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:qs.attribSizeFromValue(e)}clone(){return yr.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return yr.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const s=n.get(e),r=e;if(r.geometry){const t=s.geometry;r.geometry=_r.clone(t);const e=r.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(r.material){r.material=s.material,gr.applyCustomMaterials(e,r.material);const t=r.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(s.userData));const o=s;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=s,h=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=h.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new _r(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class br{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=xr[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const wr=li.performance.performanceManager();class Tr{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=wr.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=wr.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=wr.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=wr.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=wr.now())}recordCookEnd(){this.active()&&(this._data.cookTime=wr.now()-this._cook_start,this._cooksCount+=1)}}class Ar{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new Tr(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),li.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?li.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(li.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let s;for(let r=0;rt.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Er,Sr;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Er||(Er={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Sr||(Sr={}));const Cr=\\\\\\\"dependentOnFoundNode\\\\\\\",Nr=\\\\\\\"visibleIf\\\\\\\";var Lr,Or;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Lr||(Lr={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Or||(Or={}));class Pr{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Cr in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:tObject.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Mi(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Sr.VISIBLE_UPDATED)}}}class Rr{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Ir{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Fr{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Dr{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&li.warn(this.param.path(),this._message),this.param.emitController.emit(Sr.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Br{constructor(t){this.param=t,this.timeDependent=new Fr(this.param),this.error=new Dr(this.param)}}class zr extends Mi{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.update_from_method_dependency_name_change()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const s=m.isNumber(e),r=new zr(t,e,i);if(n)r.set_resolved_graph_node(n);else if(!s){const t=e;r.set_unresolved_path(t)}return r}}const kr=[];class Ur extends Mi{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Pr(this),this._emit_controller=new Rr(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Pr(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rr(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ir(this)}get states(){return this._states=this._states||new Br(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof zr&&t.dispose();null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Er.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Sr.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=bi.relativePath(t,this.node);return e.length>0?`${e}${bi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return kr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Gr=n(94),Vr=n.n(Gr);Vr.a.addUnaryOp(\\\\\\\"@\\\\\\\");Vr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Hr{constructor(){}parse_expression(t){try{this.reset(),this.node=Vr()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Hr.string_value_elements(t),n=[];for(let t=0;t{e.push(Wr.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(Wr.assign_item_size_line(t)),e.push(Wr.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=Wr.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return Wr.var_attribute_size(t)}var_array(t){return Wr.var_array(t)}}const qr={math_random:\\\\\\\"random\\\\\\\"},Xr=Object.keys(ir),Yr={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),Object.keys(qr).forEach((t=>{const e=qr[t];Yr[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Yr[t]=`Core.Math.${t}`})),Xr.forEach((t=>{Yr[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Yr[t]=`Core.String.${t}`}));const $r={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},Jr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`}));const Zr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Qr extends jr{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new Wr,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller.reset(),t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_body()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}function_body(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rr,String:os};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=$r[n];if(i)return i(e);const s=`${e.join(\\\\\\\", \\\\\\\")}`,r=Yr[n];if(r)return`${r}(${s})`;const o=li.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],r=`return ${e[0]}`;let o,a=[];try{o=new Function(r),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${s}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;li.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,s=t.object,r=t.property;e=s.name,n=r.name;break}}if(e){if(e=qs.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Zr[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=Jr[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=li.expressionsRegister,s=i.getMethod(t);if(!s){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void li.warn(e)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const t=r.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Kr extends jr{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class to{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class eo{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Hr,this.function_generator=new Qr(this.param),this.dependencies_controller=new to(this.param)}parse_expression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Hr,this.reset(),this.param.type()==Er.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Kr(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class no{constructor(t){this.param=t}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new eo(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class io extends Ur{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Sr.VALUE_UPDATED),this.removeDirtyState()}}class so extends io{static type(){return Er.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&os.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(os.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=so.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends Ur{constructor(){super(...arguments),this._components_contructor=so}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;ee))}}processRawInput(){const t=this.scene().cooker;t.block();const e=this.components;for(let t of e)t.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let t=0;t linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(oo||(oo={}));oo.NONE,oo.GAMMA_TO_LINEAR,oo.LINEAR_TO_GAMMA,oo.SRGB_TO_LINEAR,oo.LINEAR_TO_SRGB;class ao{static set_hsv(t,e,n,i){t=Object(On.f)(t,1),e=Object(On.d)(e,0,1),n=Object(On.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const lo=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class co extends io{static type(){return Er.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&os.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(os.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=co.convert(t);return e?this.options.ensureInRange(e):e}}class ho{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(bi.SEPARATOR);const n=e[0];return n&&(bi.NON_LETTER_PREFIXES.includes(n)||(e=`${bi.SEPARATOR}${e}`)),e}}class uo extends Ur{constructor(){super(...arguments),this.decomposed_path=new ho}}var po;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(po||(po={}));class _o extends uo{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Er.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,s=this.options.paramSelectionOptions()?po.PARAM:po.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(s==po.PARAM?n=bi.findParam(this.node,t,this.decomposed_path):e=bi.findNode(this.node,t,this.decomposed_path));const r=s==po.PARAM?this._found_param:this._found_node,o=s==po.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==r?void 0:r.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),s==po.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var mo,fo=n(33),go=n(70);class vo{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new vo;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new vo(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(mo||(mo={}));class yo{constructor(t=mo.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(On.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(vo.from_json(n));return new yo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new yo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation)return!1;const e=t.points;if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.is_equal(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.is_equal_json(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!vo.are_equal_json(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.from_json(n):this._points.push(vo.from_json(n)),e+=1}}}const xo=1024;class bo extends Ur{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new fo.a(this._texture_data,xo,1,w.ic)}static type(){return Er.RAMP}defaultValueSerialized(){return this._default_value instanceof yo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof yo?t.clone():yo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof yo?this._raw_input.toJSON():yo.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof yo?e instanceof yo?t.is_equal(e):t.is_equal_json(e):e instanceof yo?e.is_equal_json(t):yo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof yo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof yo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=yo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Sr.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/xo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new go.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const t=this.value.points,e=f.sortBy(t,(t=>t.position)),n=new Float32Array(e.length),i=new Float32Array(e.length);let s=0;for(let t of e)n[s]=t.position,i[s]=t.value,s++;return bo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}bo.DEFAULT_VALUE=new yo(mo.LINEAR,[new vo(0,0),new vo(1,1)]),bo.DEFAULT_VALUE_JSON=bo.DEFAULT_VALUE.toJSON();class wo extends Ur{static type(){return Er.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Hr.string_value_elements(t)}}const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Mo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Er.BOOLEAN]:class extends io{static type(){return Er.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(os.isBoolean(t))return os.toBoolean(t);if(os.isNumber(t)){return parseFloat(t)>=1}}return null}},[Er.BUTTON]:class extends Ur{static type(){return Er.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Er.COLOR]:class extends ro{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Er.COLOR}componentNames(){return lo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=oo.NONE){switch(t){case oo.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case oo.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case oo.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case oo.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ls.unreachable(t)}this._value_serialized_dirty=!0}},[Er.FLOAT]:so,[Er.FOLDER]:class extends Ur{static type(){return Er.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Er.INTEGER]:co,[Er.OPERATOR_PATH]:_o,[Er.PARAM_PATH]:class extends uo{static type(){return Er.PARAM_PATH}initialize_param(){this._value=new xi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.NODE_PATH]:class extends uo{static type(){return Er.NODE_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.RAMP]:bo,[Er.STRING]:wo,[Er.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Er.VECTOR2}componentNames(){return To}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Er.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Er.VECTOR3}componentNames(){return Ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Er.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Er.VECTOR4}componentNames(){return Mo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class So{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Er.STRING:return this._handle_string_param(t);case Er.OPERATOR_PATH:return this._handle_operator_path_param(t);case Er.NODE_PATH:return this._handle_node_path_param(t);case Er.PARAM_PATH:return this._handle_param_path_param(t);case Er.FLOAT:case Er.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class Co{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new So}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Mi(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notify_params_updated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let s;this.node.params_init_value_overrides&&(s=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,s)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):li.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Er.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Er.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Er.FLOAT)}get_operator_path(t){return this.param_with_type(t,Er.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Er.BOOLEAN)}float(t){return this.value_with_type(t,Er.FLOAT)}integer(t){return this.value_with_type(t,Er.INTEGER)}string(t){return this.value_with_type(t,Er.STRING)}vector2(t){return this.value_with_type(t,Er.VECTOR2)}vector3(t){return this.value_with_type(t,Er.VECTOR3)}color(t){return this.value_with_type(t,Er.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(li.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},s){const r=i.spare||!1;!1!==this._param_create_mode||r||li.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&li.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Eo[t];if(null!=o){const a=this._params_by_name[e];a&&(r?a.type()!=t&&this._deleteParam(a.name()):li.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==s)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=s.raw_input)l.set(s.raw_input);else if(null!=s.simple_data)l.set(s.simple_data);else if(null!=s.complex_data){const t=s.complex_data.raw_input;t?l.set(t):l.set(n);const e=s.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void li.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?li.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class No{constructor(){}}class Lo{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=Lo._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}Lo._next_id=0;class Oo{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.setMinCount(0),this.setMaxCount(t.length),this._initGraphNodeInputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}_initGraphNodeInputs(){for(let t=0;t0)for(let e=0;e0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let t=0;tt.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const r=this._graph_node_inputs[i];if(null==r){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&s==l||(null!=o&&this._depends_on_inputs&&r.removeGraphInput(o),null!=e?r.addGraphInput(e)?(this._depends_on_inputs||r.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new Lo(e,this.node,s,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),r.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Oo(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Ro{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const s=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&s.push(n)}return s}return[]}}class Io{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class Fo{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class Do{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Bo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Bo||(Bo={}));const zo=[Bo.BOOL,Bo.INT,Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4],ko={[Bo.BOOL]:Er.BOOLEAN,[Bo.INT]:Er.INTEGER,[Bo.FLOAT]:Er.FLOAT,[Bo.VEC2]:Er.VECTOR2,[Bo.VEC3]:Er.VECTOR3,[Bo.VEC4]:Er.VECTOR4,[Bo.SAMPLER_2D]:Er.RAMP,[Bo.SSS_MODEL]:Er.STRING},Uo={[Er.BOOLEAN]:Bo.BOOL,[Er.COLOR]:Bo.VEC3,[Er.INTEGER]:Bo.INT,[Er.FLOAT]:Bo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:Bo.VEC2,[Er.VECTOR3]:Bo.VEC3,[Er.VECTOR4]:Bo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Go={[Bo.BOOL]:!1,[Bo.INT]:0,[Bo.FLOAT]:0,[Bo.VEC2]:[0,0],[Bo.VEC3]:[0,0,0],[Bo.VEC4]:[0,0,0,0],[Bo.SAMPLER_2D]:bo.DEFAULT_VALUE_JSON,[Bo.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Vo={[Bo.BOOL]:1,[Bo.INT]:1,[Bo.FLOAT]:1,[Bo.VEC2]:2,[Bo.VEC3]:3,[Bo.VEC4]:4,[Bo.SAMPLER_2D]:1,[Bo.SSS_MODEL]:1};class Ho extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Go[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return ko[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var jo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(jo||(jo={}));const Wo=[jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4],qo={[jo.BOOL]:Er.BOOLEAN,[jo.INT]:Er.INTEGER,[jo.FLOAT]:Er.FLOAT,[jo.VEC2]:Er.VECTOR2,[jo.VEC3]:Er.VECTOR3,[jo.VEC4]:Er.VECTOR4},Xo={[Er.BOOLEAN]:jo.BOOL,[Er.COLOR]:jo.VEC3,[Er.INTEGER]:jo.INT,[Er.FLOAT]:jo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:jo.VEC2,[Er.VECTOR3]:jo.VEC3,[Er.VECTOR4]:jo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Yo={[jo.BOOL]:!1,[jo.INT]:0,[jo.FLOAT]:0,[jo.VEC2]:[0,0],[jo.VEC3]:[0,0,0],[jo.VEC4]:[0,0,0,0]};jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4;class $o extends Do{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Yo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return qo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Jo;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}(Jo||(Jo={}));class Zo extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Er.FLOAT}are_types_matched(t,e){return e==Jo.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const Qo={[ts.ANIM]:void 0,[ts.COP]:void 0,[ts.EVENT]:Jo.BASE,[ts.GL]:Bo.FLOAT,[ts.JS]:jo.FLOAT,[ts.MANAGER]:void 0,[ts.MAT]:void 0,[ts.OBJ]:void 0,[ts.POST]:void 0,[ts.ROP]:void 0,[ts.SOP]:void 0};function Ko(t,e,n){switch(t){case ts.EVENT:return new Zo(e,n);case ts.GL:return new Ho(e,n);case ts.JS:return new $o(e,n);default:return}}class ta{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case ts.EVENT:return;case ts.GL:return Uo;case ts.JS:return Xo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=Ko(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let s=t.init_value;const r=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(s=null!=o?o:null!=r?r:t.init_value,m.isArray(t.init_value))if(m.isNumber(s)){const e=new Array(t.init_value.length);e.fill(s),s=e}else m.isArray(s)&&s.length==t.init_value.length&&null!=r&&(s=t.init_value);null!=s&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(s),raw_input:b.clone(s),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class ea{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new ta(this.node,this._context)}default_connection_type(){return Qo[this._context]}create_connection_point(t,e){return Ko(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creation_completed&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(ns.INPUT),n=this.node.nodesByType(ns.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;enull==t?void 0:t.type())),e=this.node.io.outputs.namedOutputConnectionPoints().map((t=>null==t?void 0:t.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=t.length)return!1;if(i.length!=e.length)return!1;for(let e=0;et.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class na{constructor(t){this.node=t,this._connections=new Io(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Po(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ea(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new Fo(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class ia{constructor(){}}class sa extends Mi{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new br(this),this.pv=new No,this.p=new ia,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new qi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new ds(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Si(this)}get states(){return this._states=this._states||new ji(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new ps(this)}get serializer(){return this._serializer=this._serializer||new Mr(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ar(this)}get io(){return this._io=this._io||new na(this)}get nameController(){return this._name_controller=this._name_controller||new Wi(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new Co(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var s;return null===(s=this._params_controller)||void 0===s?void 0:s.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class ra extends sa{static context(){return ts.MANAGER}}class oa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class aa{static BUTTON(t,e){return new oa(Er.BUTTON,t,e)}static BOOLEAN(t,e){return new oa(Er.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new oa(Er.COLOR,t,e)}static FLOAT(t,e){return new oa(Er.FLOAT,t,e)}static FOLDER(t=null,e){return new oa(Er.FOLDER,t,e)}static INTEGER(t,e){return new oa(Er.INTEGER,t,e)}static RAMP(t=bo.DEFAULT_VALUE,e){return new oa(Er.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new oa(Er.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new oa(Er.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new oa(Er.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new oa(Er.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new oa(Er.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new oa(Er.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new oa(Er.PARAM_PATH,t,e)}}class la{}class ca{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),s=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(os.matchMask(s,t))return i;const r=this.findObjectByMaskInObject(t,i,s);if(r)return r}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let s of e.children){const e=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;os.matchMask(r,t)&&n.push(s),this.objectsByMaskInObject(t,s,n,r)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ha={computeOnDirty:!1,callback:t=>{da.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=aa.BOOLEAN(1,ha)}}}ua(la);class da{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var pa;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(pa||(pa={}));const _a=[pa.NONE,pa.COLOR,pa.TEXTURE],ma={computeOnDirty:!1,callback:t=>{ga.update(t)}};function fa(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=aa.INTEGER(_a.indexOf(pa.NONE),{menu:{entries:_a.map(((t,e)=>({name:t,value:e})))},...ma}),this.bgColor=aa.COLOR([0,0,0],{visibleIf:{backgroundMode:_a.indexOf(pa.COLOR)},...ma}),this.bgTexture=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:_a.indexOf(pa.TEXTURE)},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...ma})}}}fa(la);class ga{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==_a.indexOf(pa.NONE))t.background=null;else if(e.backgroundMode==_a.indexOf(pa.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const va={computeOnDirty:!1,callback:t=>{xa.update(t)}};function ya(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=aa.BOOLEAN(0,va),this.environment=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...va})}}}ya(la);class xa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class ba{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new ba(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}ba.prototype.isFog=!0;class wa{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new wa(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}wa.prototype.isFogExp2=!0;const Ta={computeOnDirty:!1,callback:t=>{Sa.update(t)}};var Aa;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Aa||(Aa={}));const Ma=[Aa.LINEAR,Aa.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0,Ta),this.fogType=aa.INTEGER(Ma.indexOf(Aa.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Ma.map(((t,e)=>({name:t,value:e})))},...Ta}),this.fogColor=aa.COLOR([1,1,1],{visibleIf:{useFog:1},...Ta}),this.fogNear=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogFar=aa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogDensity=aa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.EXPONENTIAL)},...Ta})}}}Ea(la);class Sa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Ma.indexOf(Aa.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new ba(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new wa(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Ca={computeOnDirty:!1,callback:t=>{La.update(t)}};function Na(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=aa.BOOLEAN(0,Ca),this.overrideMaterial=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,...Ca})}}}Na(la);class La{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(ts.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class Oa extends(Na(ya(Ea(fa(ua(la)))))){}const Pa=new Oa;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Pa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.sceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this),this._children_controller_context=ts.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new gs;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const s=`_____${n.renderOrder}__${n.path()}`;e.push(s),t.set(s,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Ia{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();os.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class Fa{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new Mr(i);e[i.graphNodeId()]=s.toJSON(t);const r=i.params.all;for(let t of r)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Da;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(Da||(Da={}));const Ba=[Da.auxclick,Da.click,Da.contextmenu,Da.dblclick,Da.mousedown,Da.mouseenter,Da.mouseleave,Da.mousemove,Da.mouseover,Da.mouseout,Da.mouseup,Da.pointerlockchange,Da.pointerlockerror,Da.select,Da.wheel];class za extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}}class ka extends sa{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return ts.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,h.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var Ua;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(Ua||(Ua={}));const Ga=[Ua.CANVAS,Ua.DOCUMENT];class Va{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const s=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,s),i.set(n.type,{listener:s,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==Ua.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const Ha={visibleIf:{active:1},callback:t=>{Wa.PARAM_CALLBACK_updateRegister(t)}};class ja extends ka{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:Ga[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class Wa extends ja{acceptedEventTypes(){return[]}}const qa=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Xa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=aa.BOOLEAN(0,Ha),this.click=aa.BOOLEAN(0,Ha),this.contextmenu=aa.BOOLEAN(0,Ha),this.dblclick=aa.BOOLEAN(0,Ha),this.mousedown=aa.BOOLEAN(1,Ha),this.mouseenter=aa.BOOLEAN(0,Ha),this.mouseleave=aa.BOOLEAN(0,Ha),this.mousemove=aa.BOOLEAN(1,Ha),this.mouseover=aa.BOOLEAN(0,Ha),this.mouseout=aa.BOOLEAN(0,Ha),this.mouseup=aa.BOOLEAN(1,Ha),this.pointerlockchange=aa.BOOLEAN(0,Ha),this.pointerlockerror=aa.BOOLEAN(0,Ha),this.select=aa.BOOLEAN(0,Ha),this.wheel=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Xa extends ja{constructor(){super(...arguments),this.paramsConfig=qa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ba.map((t=>new Zo(t,Jo.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ya;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Ya||(Ya={}));const $a=[Ya.pointerdown,Ya.pointermove,Ya.pointerup];class Ja extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}}const Za=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=aa.BOOLEAN(1,Ha),this.pointermove=aa.BOOLEAN(0,Ha),this.pointerup=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Qa extends ja{constructor(){super(...arguments),this.paramsConfig=Za}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints($a.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ka,tl;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Ka||(Ka={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(tl||(tl={}));const el=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{nl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.sceneLoaded=aa.BOOLEAN(1,Ha),this.play=aa.BOOLEAN(1,Ha),this.pause=aa.BOOLEAN(1,Ha),this.tick=aa.BOOLEAN(1,{separatorAfter:!0,...Ha}),this.treachedTime=aa.BOOLEAN(0,{callback:t=>{nl.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=aa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=aa.INTEGER(1,{range:[0,100]}),this.setFrame=aa.BUTTON(null,{callback:t=>{nl.PARAM_CALLBACK_setFrame(t)}})}};class nl extends ja{constructor(){super(...arguments),this.paramsConfig=el}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Ka.SET_FRAME,Jo.BASE,this._onSetFrame.bind(this)),new Zo(_i.PLAY,Jo.BASE,this._play.bind(this)),new Zo(_i.PAUSE,Jo.BASE,this._pause.bind(this))]);const t=mi.map((t=>new Zo(t,Jo.BASE)));t.push(new Zo(tl.TIME_REACHED,Jo.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}_onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}_play(t){this.scene().play()}_pause(t){this.scene().pause()}_onFrameUpdate(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(tl.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Mi(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this._onFrameUpdate.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t._onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var il;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(il||(il={}));const sl=[il.keydown,il.keypress,il.keyup];class rl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}}const ol=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{al.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=aa.BOOLEAN(1,Ha),this.keypress=aa.BOOLEAN(0,Ha),this.keyup=aa.BOOLEAN(0,Ha),this.keyCodes=aa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Ha),this.ctrlKey=aa.BOOLEAN(0,Ha),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class al extends ja{constructor(){super(...arguments),this.paramsConfig=ol}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(sl.map((t=>new Zo(t,Jo.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var ll;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(ll||(ll={}));const cl=[ll.resize];class hl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}}const ul=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{dl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.resize=aa.BOOLEAN(1,Ha)}};class dl extends ja{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(cl.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var pl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(pl||(pl={}));const _l=[pl.dragover];class ml extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}}var fl;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(fl||(fl={}));const gl=[fl.touchstart,fl.touchmove,fl.touchend];class vl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}}const yl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{xl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=aa.BOOLEAN(1,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class xl extends ja{constructor(){super(...arguments),this.paramsConfig=yl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(_l.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const bl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{wl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=aa.BOOLEAN(1,Ha),this.touchmove=aa.BOOLEAN(0,Ha),this.touchend=aa.BOOLEAN(0,Ha)}};class wl extends ja{constructor(){super(...arguments),this.paramsConfig=bl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(gl.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class Tl{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case al.type():return this.keyboardEventsController;case Xa.type():return this.mouseEventsController;case xl.type():return this.dragEventsController;case Qa.type():return this.pointerEventsController;case nl.type():return this.sceneEventsController;case wl.type():return this.touchEventsController;case dl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(za)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(ml)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(Ja)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(fi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(hl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(vl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Al{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}set_reference_from_param(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),h.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}set_named_nodes_from_param(t){const e=t.decomposed_path.named_nodes();for(let n of e)h.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}reset_reference_from_param(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){h.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)h.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Er.OPERATOR_PATH,Er.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,s=[];for(let t of n)this._check_param(t,i,s);for(let t of s)i.set(t.node.graphNodeId(),t.node);const r=[];return i.forEach((t=>{r.push(t)})),r}_check_param(t,e,n){if(t instanceof _o){const i=t.found_node(),s=t.found_param();return i&&e.set(i.graphNodeId(),i),void(s&&n.push(s))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Ml;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Ml||(Ml={}));class El{constructor(t){this.scene=t,this._frame=0,this._time=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._delta=0,this._graph_node=new Mi(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(_i.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(_i.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(_i.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Ml.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta)}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(El.START_FRAME,!0)}incrementTimeIfPlaying(t){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime(t))}incrementTime(t){if(this._realtimeState){this._delta=t;const e=this._time+this._delta;this.setTime(e)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return El.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Cl{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Nl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,li.renderersController.setRequireWebGL2())}}class Ll{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Mi(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Ol{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Pl{constructor(){this._cameras_controller=new r(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new Ti(this),this._expressions_controller=new ui,this._nodes_controller=new Ia(this),this._objects_controller=new ca(this),this._references_controller=new Al(this),this._time_controller=new El(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=os.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Ol}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new hi(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new Tl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new di(this)}get loadingController(){return this._loading_controller=this._loading_controller||new gi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new ci}get viewersRegister(){return this._viewers_register=this._viewers_register||new Cl(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new Fa(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Rl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let s of t)i=e[s],i&&(n[s]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let s;for(let r of i)if(s=e[r],null!=s){const e=s.options,i=s.overriden_options;if(e||i){const o=s;e&&e.spare==t?null!=o.raw_input&&(n[r]={complex_data:o}):i&&(n[r]={complex_data:o})}else{const t=s;(i||null!=t)&&(n[r]={simple_data:t})}}}return n}}const Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,s;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:r}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of r)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let r of this._optimized_root_node_names){const o=e[r],a=this._node.createNode(Il);if(a){a.setName(r),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(s=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,s;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const r=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of r)(null===(s=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===s?void 0:s.active())||o++;return this.is_optimized_root_node_generic({outputs_count:r.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let s of n)if(s!=e){const n=t[s].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(s)}}return i}_create_operation_container(t,e,n,i){const s=Rl.non_spare_params_data_value(n.params),r=Dl.operation_type(n),o=this._node.create_operation_container(r,i,s);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return Dl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class Bl{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:s}=Dl.child_names_by_optimized_state(e),r=[];for(let n of s){const i=e[n],s=i.type.toLowerCase(),o=Rl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(s,o);t&&(t.setName(n),r.push(t))}catch(e){console.error(`error importing node: cannot create with type ${s}`,e);const i=os.camelCase(s);try{const t=this._node.createNode(i,o);t&&(t.setName(n),r.push(t))}catch(e){const a=`${s}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),r.push(t))}catch(e){const n=`failed to create node with type '${s}', '${i}' or '${a}'`;t.report.addWarning(n),li.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==ts.SOP){const t=Object.keys(e);let s;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const t=r.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(s)){const t=`node '${`${this._node.path()}/${s}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of r){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else li.warn(`possible import error for node ${n.name()}`)}for(let t of r){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const zl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class kl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;if(null!=e){const t=this._node.io.inputs.minCount();this._node.io.inputs.setCount(t,e)}this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(li.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const s=e.comment;s&&n.setComment(s),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new Bl(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,s,r,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(s=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(l));const c=a.optimize;null!=c&&(null===(o=null===(r=this._node.flags)||void 0===r?void 0:r.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n0,s=n.toAdd&&n.toAdd.length>0;if(i||s){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Wl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of zl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Ul extends Rl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Rl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Rl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends kl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new kl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof wo?new Vl(t):t instanceof bo?new Hl(t):new Rl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Pl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const s=e.realtimeState;null!=s&&t.timeController.setRealtimeState(s),t.setFrame(e.frame||El.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Yl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),s=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;s.push(i)}const r=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();r.push(`${n}/ui.json?t=${t}`)}for(let t of s)r.push(t);let o=0;const a=r.length,l=r.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),h=[];for(let t of c)h.push(await t.json());const u={root:h[0],properties:h[1]};let d=2;t.editorMode&&(u.ui=h[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=h[t+d];p[e]=n}return this.assemble(u,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{r.matrixAutoUpdate=!0},c.onComplete=()=>{r.matrixAutoUpdate=t,r.matrixAutoUpdate||r.updateMatrix()}}if(o instanceof ah.a&&this._target_value instanceof ah.a){const t={value:0},e=o,n=(new ah.a).copy(o),i=this._target_value;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._target_value[t],i);a&&this._startTimeline(e,n,c,a)}}}_sceneGraphProps(t,e){const n=e.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=t[e];let i=null;const s=[];return m.isNumber(n)?(i=t,s.push(e)):(i=n,this._target_value instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,ah.a),{target_property:n,to_target:i,property_names:s}}{const e=t[n.shift()];if(e){const t=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(e,t)}}}_populateWithNode(t,e,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",t]);const i=t.p[this._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):li.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Er.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Er.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Er.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Er.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Er.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamVector2(t,e,n){if(!(this._target_value instanceof d.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),i.w=this.withOp(t.value.w,this._target_value.w,o),this._startTimeline(e,n,i,s)}withOp(t,e,n){switch(n){case N_.SET:return e;case N_.ADD:return t+e;case N_.SUBSTRACT:return t-e}ls.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||oh.NONE;n&&(e.ease=n);const i=t.delay();null!=i&&(e.delay=i);const s=t.repeatParams();return s&&(e.repeat=s.count,e.repeatDelay=s.delay,e.yoyo=s.yoyo),e}_startTimeline(t,e,n,i){const s=t.position(),r=s?s.toParameter():void 0;e.to(i,n,r)}}function dh(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function ph(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var _h,mh,fh,gh,vh,yh,xh,bh,wh,Th,Ah,Mh,Eh,Sh={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Ch={duration:.5,overwrite:!1,delay:0},Nh=1e8,Lh=1e-8,Oh=2*Math.PI,Ph=Oh/4,Rh=0,Ih=Math.sqrt,Fh=Math.cos,Dh=Math.sin,Bh=function(t){return\\\\\\\"string\\\\\\\"==typeof t},zh=function(t){return\\\\\\\"function\\\\\\\"==typeof 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N_,L_=pp.registerPlugin(C_)||pp;L_.core.Tween;!function(t){t.SET=\\\\\\\"set\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\"}(N_||(N_={}));const O_=[N_.SET,N_.ADD,N_.SUBSTRACT];class P_{constructor(){this._timeline_builders=[],this._duration=1,this._operation=N_.SET,this._delay=0,this._debug=!1}setDebug(t){this._debug=t}_printDebug(t){this._debug&&console.log(t)}addTimelineBuilder(t){this._timeline_builders.push(t),t.setParent(this)}timelineBuilders(){return this._timeline_builders}setParent(t){this._parent=t}parent(){return this._parent}setTarget(t){this._target=t;for(let e of this._timeline_builders)e.setTarget(t)}target(){return this._target}setDuration(t){if(t>=0){this._duration=t;for(let e of this._timeline_builders)e.setDuration(t)}}duration(){return this._duration}setEasing(t){this._easing=t;for(let e of this._timeline_builders)e.setEasing(t)}easing(){return this._easing}setOperation(t){this._operation=t;for(let e of this._timeline_builders)e.setOperation(t)}operation(){return this._operation}setRepeatParams(t){this._repeat_params=t;for(let e of this._timeline_builders)e.setRepeatParams(t)}repeatParams(){return this._repeat_params}setDelay(t){this._delay=t;for(let e of this._timeline_builders)e.setDelay(t)}delay(){return this._delay}setPosition(t){this._position=t}position(){return this._position}setUpdateCallback(t){this._update_callback=t}updateCallback(){return this._update_callback}clone(){const t=new P_;if(t.setDuration(this._duration),t.setOperation(this._operation),t.setDelay(this._delay),this._target&&t.setTarget(this._target.clone()),this._easing&&t.setEasing(this._easing),this._delay&&t.setDelay(this._delay),this._update_callback&&t.setUpdateCallback(this._update_callback.clone()),this._repeat_params&&t.setRepeatParams({count:this._repeat_params.count,delay:this._repeat_params.delay,yoyo:this._repeat_params.yoyo}),this._property){const e=this._property.name();e&&t.setPropertyName(e);const n=this._property.targetValue();null!=n&&t.setPropertyValue(n)}this._position&&t.setPosition(this._position.clone());for(let e of this._timeline_builders){const n=e.clone();t.addTimelineBuilder(n)}return t}setPropertyName(t){this.property().setName(t)}property(){return this._property=this._property||new uh}propertyName(){return this.property().name()}setPropertyValue(t){this.property().setTargetValue(t)}populate(t){var e;this._printDebug([\\\\\\\"populate\\\\\\\",this,t]);for(let n of this._timeline_builders){const i=L_.timeline();n.setDebug(this._debug),n.populate(i);const s=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,s)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const R_=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends ih{constructor(){super(...arguments),this.paramsConfig=R_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new P_;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new P_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const B_=new class extends la{constructor(){super(...arguments),this.duration=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class z_ extends ih{constructor(){super(...arguments),this.paramsConfig=B_}static type(){return\\\\\\\"duration\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.duration])}))}))}cook(t){const e=t[0]||new P_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const k_=new class extends la{constructor(){super(...arguments),this.name=aa.INTEGER(lh.indexOf(oh.POWER4),{menu:{entries:lh.map(((t,e)=>({name:t,value:e})))}}),this.inOut=aa.INTEGER(hh.indexOf(ch.OUT),{menu:{entries:hh.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends ih{constructor(){super(...arguments),this.paramsConfig=k_}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.inOut],(()=>this.easing_full_name()))}))}))}easing_full_name(){const t=lh[this.pv.name];if(t==oh.NONE)return t;return`${t}.${hh[this.pv.inOut]}`}cook(t){const e=t[0]||new P_,n=this.easing_full_name();e.setEasing(n),this.setTimelineBuilder(e)}}var G_;!function(t){t.RELATIVE=\\\\\\\"relative\\\\\\\",t.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(G_||(G_={}));const V_=[G_.RELATIVE,G_.ABSOLUTE];var H_;!function(t){t.START=\\\\\\\"start\\\\\\\",t.END=\\\\\\\"end\\\\\\\"}(H_||(H_={}));const j_=[H_.START,H_.END];class W_{constructor(){this._mode=G_.RELATIVE,this._relativeTo=H_.END,this._offset=0}clone(){const t=new W_;return t.setMode(this._mode),t.setRelativeTo(this._relativeTo),t.setOffset(this._offset),t}setMode(t){this._mode=t}mode(){return this._mode}setRelativeTo(t){this._relativeTo=t}relativeTo(){return this._relativeTo}setOffset(t){this._offset=t}offset(){return this._offset}toParameter(){switch(this._mode){case G_.RELATIVE:return this._relative_position_param();case G_.ABSOLUTE:return this._absolutePositionParam()}ls.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ls.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0,{range:[-1,1]}),this.overridePositions=aa.BOOLEAN(0),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends ih{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new P_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ls.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends la{constructor(){super(...arguments),this.play=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=aa.BOOLEAN(0)}};class Z_ extends ih{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0]||new P_;this.setTimelineBuilder(e)}async play(){return new Promise((async t=>{const e=await this.compute();e&&(this._timeline_builder=e.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline({onComplete:t}),this.pv.debug&&console.log(`play from '${this.path()}'`),this._timeline_builder.setDebug(this.pv.debug),this._timeline_builder.populate(this._timeline)))}))}async pause(){this._timeline&&this._timeline.pause()}static PARAM_CALLBACK_play(t){t.play()}static PARAM_CALLBACK_pause(t){t.pause()}}const Q_=new class extends la{constructor(){super(...arguments),this.operation=aa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends ih{constructor(){super(...arguments),this.paramsConfig=Q_}static type(){return\\\\\\\"operation\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>O_[this.pv.operation]))}))}))}cook(t){const e=t[0]||new P_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=aa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0)}};class em extends ih{constructor(){super(...arguments),this.paramsConfig=tm}static type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new P_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"position\\\\\\\")}};class im extends ih{constructor(){super(...arguments),this.paramsConfig=nm}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new P_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var sm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(sm||(sm={}));const rm=[sm.CUSTOM,sm.FROM_SCENE_GRAPH,sm.FROM_NODE],om=rm.indexOf(sm.CUSTOM),am=rm.indexOf(sm.FROM_SCENE_GRAPH),lm=rm.indexOf(sm.FROM_NODE);const cm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(om,{menu:{entries:rm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=aa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=aa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{hm.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=aa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=aa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=aa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=aa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=aa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=aa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class hm extends ih{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new P_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(rm.indexOf(t))}async _prepare_timeline_builder(t){const e=rm[this.pv.mode];switch(e){case sm.CUSTOM:return this._prepare_timebuilder_custom(t);case sm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case sm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ls.unreachable(e)}_prepare_timebuilder_custom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepare_timebuilder_from_scene_graph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof ah.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const s=i.value;s&&(m.isNumber(s)||m.isVector(s))&&t.setPropertyValue(s)}static PARAM_CALLBACK_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const um=new class extends la{constructor(){super(...arguments),this.unlimited=aa.BOOLEAN(0),this.count=aa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=aa.FLOAT(0),this.yoyo=aa.BOOLEAN(0)}};class dm extends ih{constructor(){super(...arguments),this.paramsConfig=um}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new P_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends ih{constructor(){super(...arguments),this.paramsConfig=pm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=aa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends ih{constructor(){super(...arguments),this.paramsConfig=bm}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ls.unreachable(t)}))}))}))}cook(t){const e=t[0]||new P_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ls.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ls.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new P_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends sa{static context(){return ts.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Mi(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Pm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Pm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Pm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Rm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Im{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Fm=new ot.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class Bm{constructor(t){this._mesh=new B.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class zm extends Im{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new Bm(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class km extends Im{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),s=t.state;let r,o;s.buffers.color.setMask(!1),s.buffers.depth.setMask(!1),s.buffers.color.setLocked(!0),s.buffers.depth.setLocked(!0),this.inverse?(r=0,o=1):(r=1,o=0),s.buffers.stencil.setTest(!0),s.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),s.buffers.stencil.setFunc(i.ALWAYS,r,4294967295),s.buffers.stencil.setClear(o),s.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),s.buffers.color.setLocked(!1),s.buffers.depth.setLocked(!1),s.buffers.stencil.setLocked(!1),s.buffers.stencil.setFunc(i.EQUAL,1,4294967295),s.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),s.buffers.stencil.setLocked(!0)}}class Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Q(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===Rm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===zm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new zm(Rm),this.clock=new Om}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e({name:Object.keys(t)[0],value:Object.values(t)[0]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends la{constructor(){super(...arguments),this.prependRenderPass=aa.BOOLEAN(1),this.useRenderTarget=aa.BOOLEAN(1),this.tmagFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=aa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=aa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new Gm(e,t)}else n=new Gm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=li.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Hm(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Km extends Am{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(44);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var sf=new Uint16Array(32),rf=0;rf<32;rf++)sf[rf]=28898;const of=new fo.a(sf,32,1,w.gb,w.M);class af extends sa{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ki(this)}static context(){return ts.COP}static displayedInputNames(){return nf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class hf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${os.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=os.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),m.isNumber(i)&&(i=os.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return os.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:os.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return os.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:os.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const uf=/\\\\/+/g;class df extends sa{static context(){return ts.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==ts.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(uf,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return hf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return hf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const pf=new class extends la{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const ff=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=aa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=aa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ss.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return gf.INPUT_NAME}get output_name(){return gf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(gf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Bo.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Mf extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Ef={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,s;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(!r)return;const o=t.glVarName(e),a=r.type(),l=new Mf(t,a,o);n.addDefinitions(t,[l]);const c=null===(s=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!c)return;const h=c.shader_config(n.current_shader_name);if(!h)return;const u=h.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of u)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==u.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Ef[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var s,r;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Mf(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const u=new Map;u.set(xf.FRAGMENT,[]),h.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(r=null===(s=t.material_node)||void 0===s?void 0:s.assemblerController)||void 0===r?void 0:r.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)h.pushOnArrayAtEntry(c,t,l),h.pushOnArrayAtEntry(u,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),u.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Cf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const Nf=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Cf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Mf(this,e,n),s=`${n} = ${hf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[s],xf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Pf{static overlay(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.max(t.width,e.width),s.height=Math.max(t.height,e.height);let r=s.getContext(\\\\\\\"2d\\\\\\\");r.drawImage(t,0,0,t.width,t.height),r.drawImage(e,0,0,e.width,e.height);const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=t,s.height=e;let r=s.getContext(\\\\\\\"2d\\\\\\\");r.beginPath(),r.rect(0,0,t,e),r.fillStyle=\\\\\\\"white\\\\\\\",r.fill();const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const s=Math.min(t.width,t.height),r=t.width/t.height;i.width=s,i.height=s;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=r>1,l=a?(t.width-s)/2:(t.height-s)/2;a?o.drawImage(t,l,0,s,s,0,0,s,s):o.drawImage(t,0,l,s,s,0,0,s,s);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),h=new Image;h.onload=()=>{e(h)},h.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var Rf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Rf||(Rf={}));class If{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Pf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,s=this._data_texture.image.data,r=4*n;for(let t=0;t0:s.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(s.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":s.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":s.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":s.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":s.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":s.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":s.uniforms[e].value=(new G.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":s.uniforms[e].value=(new A.a).fromArray(i.value);break;default:s.uniforms[e].value=i.value}}if(void 0!==t.defines&&(s.defines=t.defines),void 0!==t.vertexShader&&(s.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(s.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)s.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(s.flatShading=1===t.shading),void 0!==t.size&&(s.size=t.size),void 0!==t.sizeAttenuation&&(s.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(s.map=n(t.map)),void 0!==t.matcap&&(s.matcap=n(t.matcap)),void 0!==t.alphaMap&&(s.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(s.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(s.bumpScale=t.bumpScale),void 0!==t.normalMap&&(s.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(s.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),s.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(s.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(s.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(s.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(s.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(s.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(s.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(s.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(s.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(s.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(s.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(s.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(s.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(s.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(s.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(s.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(s.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(s.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(s.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(s.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(s.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(s.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(s.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(s.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(s.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(s.thicknessMap=n(t.thicknessMap)),s}setTextures(t){return this.textures=t,this}}class Xf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new qf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new G.a;for(let t=0;t{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(ts.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class Jf extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,tg.handle_dependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{$f.callback(n,i)}})}}}class Zf{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const Qf=[256,256];const Kf=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2(Qf),this.useCameraRenderer=aa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new B.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new gs,this._texture_camera=new tf.a,this._children_controller_context=ts.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return jn.GL_TEXTURE}_create_assembler_controller(){const t=li.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Sf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Of.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),s=t.assembler.uniforms();i&&s&&(this._fragment_shader=i,this._uniforms=s),tg.handle_dependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handle_dependencies(t,e,n){const i=t.scene(),s=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(s,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(s))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],sg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],rg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=aa.BOOLEAN(0,{...lg}),this.encoding=aa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=aa.BOOLEAN(0,{...cg}),this.mapping=aa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=aa.BOOLEAN(0,{...hg}),this.wrapS=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...hg}),this.wrapT=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...hg}),this.tminFilter=aa.BOOLEAN(0,{...ug}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...ug}),this.tmagFilter=aa.BOOLEAN(0,{...ug}),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...ug}),this.tanisotropy=aa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=aa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=aa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=aa.BOOLEAN(0,{...pg}),this.flipY=aa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=aa.BOOLEAN(0,{..._g}),this.offset=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=aa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=aa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=aa.BOOLEAN(0,{...yg}),this.tformat=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=aa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=aa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(la)){}new bg;class wg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Ff(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const s=Object.keys(i)[0];i[s]==e[n]&&t.push(`${n}: ${s}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=aa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=aa.BUTTON(null,{cook:!1,callback:t=>{Eg.PARAM_CALLBACK_update(t)}})}}}(la))){}const Mg=new Ag;class Eg extends af{constructor(){super(...arguments),this.paramsConfig=Mg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Tg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Sg=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=aa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),s=255*i[0],r=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t({name:t,value:e})))}})}};class Ig extends af{constructor(){super(...arguments),this.paramsConfig=Rg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(ts.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Pg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const Bg=new class extends la{constructor(){super(...arguments),this.useCameraRenderer=aa.BOOLEAN(1),this.mode=aa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class zg extends af{constructor(){super(...arguments),this.paramsConfig=Bg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Ff(this);const e=await this._renderer_controller.renderer();if(e){const n=new Tt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class kg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==r?r:w.V,this.magFilter=void 0!==s?s:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}kg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(28);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=li.assetUrls.remapedUrl(t);if(e)return e;const n=li.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await li.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return li.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,s=n.fullUrl,r=t.node;e.push(li.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:s,node:r}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}jg.loadingManager=Hg;const Wg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\",\\\\\\\"ogg\\\\\\\"];var qg;!function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(qg||(qg={}));const Xg=[qg.JPEG,qg.JPG,qg.PNG,qg.EXR,qg.BASIS,qg.HDR];function Yg(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");return e[e.length-1]}class $g extends jg{constructor(t,e,n,i,s){super(t,i,n),this._param=e,this._node=n,this._scene=i,this._forceVideo=!1,this._forceImage=!1,this._forceVideo=(null==s?void 0:s.forceVideo)||this._forceVideo,this._forceImage=(null==s?void 0:s.forceImage)||this._forceImage}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=bi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),s=await this._urlToLoad();if(this._forceVideo||Wg.includes(i)){e(await this._loadVideo(s))}else if(this._forceImage||Xg.includes(i))try{e(await this._loadImage(s,t))}catch(t){n()}}))}_loadImage(t,e){return new Promise((async(n,i)=>{const s=this.extension();this.loader_for_ext(s,e).then((async e=>{e?($g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed(),e.load(t,(e=>{$g.decrementInProgressLoadsCount();const i=$g._onTextureLoadedCallback;i&&i(t,e),n(e)}),void 0,(t=>{$g.decrementInProgressLoadsCount(),li.warn(\\\\\\\"error\\\\\\\",t),i()}))):i()}))}))}_loadVideo(t){return new Promise((async(e,n)=>{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const n=new kg(i);$g.decrementInProgressLoadsCount();const s=$g._onTextureLoadedCallback;s&&s(t,n),e(n)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=jg.extension(t);let o=$g.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||$g._default_video_source_type(t),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(s);let a=t;a=\\\\\\\"mp4\\\\\\\"==r?$g.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):$g.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=$g.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||$g._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static module_names(t){switch(t){case qg.EXR:return[Hn.EXRLoader];case qg.HDR:return[Hn.RGBELoader];case qg.BASIS:return[Hn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case qg.EXR:return await this._exr_loader(e);case qg.HDR:return await this._hdr_loader(e);case qg.BASIS:return await $g._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await li.modulesRegister.module(Hn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await li.modulesRegister.module(Hn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await li.modulesRegister.module(Hn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.BASISPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const r=await li.renderersController.waitForRenderer();return r?n.detectSupport(r):li.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}static _default_video_source_type(t){return`video/${jg.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.isChrome()?0:10}static incrementInProgressLoadsCount(){this.in_progress_loads_count++}static decrementInProgressLoadsCount(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async waitForMaxConcurrentLoadsQueueFreed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$g.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,$g.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,$g.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},$g.MAX_CONCURRENT_LOADS_COUNT=$g._init_max_concurrent_loads_count(),$g.CONCURRENT_LOADS_DELAY=$g._init_concurrent_loads_delay(),$g.in_progress_loads_count=0,$g._queue=[];var Jg=n(114);class Zg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{Kg.PARAM_CALLBACK_reload(t)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{Kg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=aa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Kg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(la))){}const Qg=new Zg;class Kg extends af{constructor(){super(...arguments),this.paramsConfig=Qg,this.textureParamsController=new wg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(\\\\\\\"gif\\\\\\\"!=Yg(this.pv.url).toLowerCase())this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const t=await fetch(this.pv.url),e=await t.arrayBuffer(),n=await Object(Jg.parseGIF)(e),i=!0;this._parsedFrames=await Object(Jg.decompressFrames)(n,i);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),$g.decrementInProgressLoadsCount(),this._gifCanvasElement){const t=new Tg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}function tv(t){return class extends t{constructor(){super(...arguments),this.checkFileType=aa.BOOLEAN(!0)}}}class ev extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{iv.PARAM_CALLBACK_reload(t,e)}})}}}(la)))){}const nv=new ev;class iv extends af{constructor(){super(...arguments),this.paramsConfig=nv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Xg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}var e}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new $g(t,n,this,this.scene(),{forceImage:!this.pv.checkFileType});try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var sv=n(32);const rv=.005;class ov{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new gs,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:rv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new ah.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Q(this.res,this.res,{type:n}),this.progressiveLightMap2=new Q(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const s=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,s)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(s-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const r={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=r.previousShadowMap,i.uniforms.pixelOffset=r.pixelOffset,n.uniforms.previousShadowMap=r.previousShadowMap,n.uniforms.pixelOffset=r.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:rv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:rv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const av=new class extends la{constructor(){super(...arguments),this.update=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=aa.BOOLEAN(1),this.lightMapRes=aa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=aa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=aa.FLOAT(rv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=aa.BOOLEAN(1),this.blurAmount=aa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=aa.FLOAT(1,{range:[0,10]}),this.objectsMask=aa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=aa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class lv extends af{constructor(){super(...arguments),this.paramsConfig=av,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await li.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new ov(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof sv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof sv.a||!n.has(e)&&e instanceof B.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const cv=new la;class hv extends af{constructor(){super(...arguments),this.paramsConfig=cv}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const uv=[is.ORTHOGRAPHIC,is.PERSPECTIVE];class dv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:uv}}),this.resolution=aa.VECTOR2([1024,1024]),this.useCameraRenderer=aa.BOOLEAN(1),this.render=aa.BUTTON(null,{callback:t=>{_v.PARAM_CALLBACK_render(t)}})}}}(la))){}const pv=new dv;class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(ts.OBJ),this._camera_node&&uv.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const mv=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class fv extends af{constructor(){super(...arguments),this.paramsConfig=mv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class gv extends(xg(la)){}const vv=new gv;class yv extends af{constructor(){super(...arguments),this.paramsConfig=vv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class xv extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_VIDEO]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{wv.PARAM_CALLBACK_reload(t,e)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=aa.FLOAT(0,{cook:!1}),this.setVideoTime=aa.BUTTON(null,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_time(t)}})}}}(la)))){}const bv=new xv;class wv extends af{constructor(){super(...arguments),this.paramsConfig=bv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Wg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&wg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}var e}dispose(){var t;super.dispose(),this._video&&(null===(t=this._video.parentElement)||void 0===t||t.removeChild(this._video))}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new $g(t,n,this,this.scene(),{forceVideo:!this.pv.checkFileType});try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class Tv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=aa.VECTOR2([1024,1024])}}}(la))){}const Av=new Tv;class Mv extends af{constructor(){super(...arguments),this.paramsConfig=Av,this.textureParamsController=new wg(this)}static type(){return Lg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new kg(this._video),n=t[0];if(n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Ev extends sa{static context(){return ts.COP}cook(){this.cookController.endCook()}}class Sv extends Ev{}class Cv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Pv extends Ev{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Rv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Iv,Fv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Iv||(Iv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Fv||(Fv={}));const Dv=new class extends la{constructor(){super(...arguments),this.animation=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.ANIM},dependentOnFoundNode:!1}),this.play=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_pause(t)}})}};class Bv extends ka{constructor(){super(...arguments),this.paramsConfig=Dv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Iv.START,Jo.BASE,this._play.bind(this)),new Zo(Iv.STOP,Jo.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Fv.START,Jo.BASE),new Zo(Fv.COMPLETE,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(ts.ANIM);if(!n)return;const i=await n.compute();i&&(this._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Fv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Fv.COMPLETE,t)}}const zv=\\\\\\\"event\\\\\\\";const kv=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(1),this.inputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Uv extends ka{constructor(){super(...arguments),this.paramsConfig=kv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>zv)),this.io.connection_points.set_expected_output_types_function((()=>[Jo.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill(Jo.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(zv,t)}}const Gv=new class extends la{constructor(){super(...arguments),this.blocking=aa.BOOLEAN(1)}};class Vv extends ka{constructor(){super(...arguments),this.paramsConfig=Gv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Vv.OUTPUT,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Vv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Hv;Vv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Hv||(Hv={}));const jv=new class extends la{constructor(){super(...arguments),this.dispatch=aa.BUTTON(null,{callback:t=>{Wv.PARAM_CALLBACK_execute(t)}})}};class Wv extends ka{constructor(){super(...arguments),this.paramsConfig=jv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hv.OUT,Jo.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Hv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class qv extends ka{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const s=await this.createControlsInstance(t,i),r=this._controls_by_viewer.get(e);r&&r.dispose(),this._controls_by_viewer.set(e,s);const o=li.performance.performanceManager().now();return s.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setupControls(s),s}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Xv=n(27);const Yv=new p.a(0,0,1),$v=new Xv.a,Jv=new ah.a,Zv=new ah.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Qv={type:\\\\\\\"change\\\\\\\"};class Kv extends J.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new ah.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},s=function(){e.screenOrientation=window.orientation||0};this.connect=function(){s(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?On.e(t.alpha)+e.alphaOffset:0,s=t.beta?On.e(t.beta):0,r=t.gamma?On.e(t.gamma):0,o=e.screenOrientation?On.e(e.screenOrientation):0;!function(t,e,n,i,s){$v.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler($v),t.multiply(Zv),t.multiply(Jv.setFromAxisAngle(Yv,-s))}(e.object.quaternion,i,s,r,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Qv))}},this.dispose=function(){e.disconnect()},this.connect()}}const ty=new class extends la{constructor(){super(...arguments),this.enabled=aa.BOOLEAN(1)}};class ey extends qv{constructor(){super(...arguments),this.paramsConfig=ty,this._controls_by_element_id=new Map}static type(){return rs.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async createControlsInstance(t,e){const n=new Kv(t);return this._controls_by_element_id.set(e.id,n),n}setupControls(t){t.enabled=this.pv.enabled}updateRequired(){return!0}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ny{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(On.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const iy={type:\\\\\\\"change\\\\\\\"},sy={type:\\\\\\\"start\\\\\\\"},ry={type:\\\\\\\"end\\\\\\\"};class oy extends J.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(iy),n.update(),s=i.NONE},this.update=function(){const e=new p.a,u=(new ah.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=u.clone().invert(),_=new p.a,m=new ah.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(u),o.setFromVector3(e),n.autoRotate&&s===i.NONE&&E(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;tMath.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(h||_.distanceToSquared(n.object.position)>r||8*(1-m.dot(n.object.quaternion))>r)&&(n.dispatchEvent(iy),_.copy(n.object.position),m.copy(n.object.quaternion),h=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let s=i.NONE;const r=1e-6,o=new ny,a=new ny;let l=1;const c=new p.a;let h=!1;const u=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function M(){return Math.pow(.95,n.zoomSpeed)}function E(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const s=n.domElement;if(n.object.isPerspectiveCamera){const r=n.object.position;t.copy(r).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/s.clientHeight,n.object.matrix),N(2*i*o/s.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/s.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/s.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){u.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)u.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);u.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function B(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function z(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,u).multiplyScalar(n.rotateSpeed);const e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_)}function k(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,s=t.pageY-e.y,r=Math.sqrt(i*i+s*s);x.set(0,r),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),s=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),s=i.TOUCH_PAN;break;default:s=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&B(),n.enablePan&&D(),s=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&B(),n.enableRotate&&F(),s=i.TOUCH_DOLLY_ROTATE;break;default:s=i.NONE}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),s=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),s=i.PAN}else{if(!1===n.enableRotate)return;R(t),s=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;R(t),s=i.ROTATE}else{if(!1===n.enablePan)return;I(t),s=i.PAN}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),s){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;z(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;k(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&k(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&z(t)}(t),n.update();break;default:s=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(s){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,u).multiplyScalar(n.rotateSpeed);var e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(M()):b.y<0&&P(M()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(ry),s=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||s!==i.NONE&&s!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(sy),function(t){t.deltaY<0?P(M()):t.deltaY>0&&O(M()),n.update()}(t),n.dispatchEvent(ry))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:E(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:E(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e{py.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=aa.BOOLEAN(0),this.keysMode=aa.INTEGER(uy.indexOf(hy.PAN),{visibleIf:{enableKeys:1},menu:{entries:uy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=aa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.PAN)}}),this.keysRotateSpeedVertical=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}}),this.keysRotateSpeedHorizontal=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}})}};class py extends qv{constructor(){super(...arguments),this.paramsConfig=dy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rs.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(ly,Jo.BASE),new Zo(cy,Jo.BASE),new Zo(\\\\\\\"end\\\\\\\",Jo.BASE)])}async createControlsInstance(t,e){const n=new oy(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(ly,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(cy,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setupControls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=uy[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}updateRequired(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class _y extends py{static type(){return rs.MAP}async create_controls_instance(t,e){const n=new ay(t,e);return this._bind_listeners_to_controls_instance(n),n}}const my=new class extends la{constructor(){super(...arguments),this.delay=aa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class fy extends ka{constructor(){super(...arguments),this.paramsConfig=my}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(\\\\\\\"out\\\\\\\",Jo.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const gy={type:\\\\\\\"change\\\\\\\"},vy={type:\\\\\\\"lock\\\\\\\"},yy={type:\\\\\\\"unlock\\\\\\\"},xy=Math.PI/2,by=new p.a,wy=new ny;class Ty extends J.a{constructor(t,e,n){super(),this.camera=t,this.domElement=e,this.player=n,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.rotateSpeed=1,this.euler=new Xv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._azimuthalAngle=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e*this.rotateSpeed,this.euler.x-=.002*n*this.rotateSpeed,this.euler.x=Math.max(xy-this.maxPolarAngle,Math.min(xy-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this._computeAzimuthalAngle(),this.dispatchEvent(gy)}}_computeAzimuthalAngle(){this.camera.updateMatrixWorld(),by.set(0,0,1),this.camera.localToWorld(by),by.sub(this.camera.position),wy.setFromVector3(by),this._azimuthalAngle=wy.theta}onPointerlockChange(){this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(vy),this.isLocked=!0):(this.dispatchEvent(yy),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}}var Ay=n(16);const My=new p.a,Ey=new p.a;class Sy{constructor(t=new p.a,e=new p.a){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){My.subVectors(t,this.start),Ey.subVectors(this.end,this.start);const n=Ey.dot(Ey);let i=Ey.dot(My)/n;return e&&(i=On.d(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}const Cy=new p.a;function Ny(t,e,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),l=Math.PI/4;Cy.copy(e),Cy[i]=0,Cy.normalize();const c=.5*o/(o+a),h=1-Cy.angleTo(t)/l;if(1===Math.sign(Cy[n]))return h*c;return a/(o+a)+c+c*(1-h)}class Ly extends N{constructor(t=1,e=1,n=1,i=2,s=.1){if(i=2*i+1,s=Math.min(t/2,e/2,n/2,s),super(1,1,1,i,i,i),1===i)return;const r=this.toNonIndexed();this.index=null,this.attributes.position=r.attributes.position,this.attributes.normal=r.attributes.normal,this.attributes.uv=r.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(s),c=this.attributes.position.array,h=this.attributes.normal.array,u=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,r=0;ithis.object.position.y<-25,this.object=t.object,this.object.matrixAutoUpdate=!0,this.collider=t.collider,t.meshName&&(this._mesh=new B.a,this._mesh.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),this._mesh.name=t.meshName,this._mesh.receiveShadow=!0,this._mesh.castShadow=!0)}setCollider(t){this.collider=t}setCapsule(t){this.capsuleInfo.radius=t.radius,this.capsuleInfo.segment.end.y=-t.height,this._mesh&&(this._mesh.geometry=Oy(t))}setUsePlayerMesh(t){t?(this._mesh=this._mesh||this._createMesh(),this.object.add(this._mesh)):this._mesh&&this.object.remove(this._mesh)}_createMesh(){const t=new B.a;return t.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),t.name=this._meshName||\\\\\\\"defaultPlayerMeshName\\\\\\\",t.receiveShadow=!0,t.castShadow=!0,t}setMaterial(t){this._mesh&&(this._mesh.material=t)}reset(){this.stop(),this.object.position.copy(this.startPosition),ky.copy(this.startRotation).multiplyScalar(On.a),this.object.rotation.setFromVector3(ky)}stop(){this._pressed.forward=!1,this._pressed.backward=!1,this._pressed.left=!1,this._pressed.right=!1,this._running=!1}setResetRequiredCallback(t){this._resetRequiredCallback=t}setAzimuthalAngle(t){this._azimuthalAngle=t}update(t){const e=Math.min(t,.1);for(let t=0;tt.intersectsBox(Dy),intersectsTriangle:t=>{const e=Iy,n=Fy,s=t.closestPointToSegment(zy,e,n);if(sMath.abs(t*this._velocity.y*.25);const o=Math.max(0,r.length()-1e-5);r.normalize().multiplyScalar(o),this.object.position.add(r),this._onGround?this._velocity.set(0,0,0):(r.normalize(),this._velocity.addScaledVector(r,-r.dot(this._velocity))),this._resetRequiredCallback()&&this.reset()}setForward(t){this._pressed.forward=t}setBackward(t){this._pressed.backward=t}setLeft(t){this._pressed.left=t}setRight(t){this._pressed.right=t}jump(){this._onGround&&this.jumpAllowed&&(this._velocity.y=this.jumpStrength)}setRun(t){t?this._onGround&&this.runAllowed&&(this._running=!0):this._running=!1}}function Gy(t){t.preventDefault()}class Vy{constructor(t){this.player=t,this._bounds={keydown:this._onKeyDown.bind(this),keyup:this._onKeyUp.bind(this)}}_onKeyDown(t){if(!t.ctrlKey)switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!0),Gy(t);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!0),Gy(t);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!0),Gy(t);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!0),Gy(t);break;case\\\\\\\"Space\\\\\\\":this.player.jump(),Gy(t);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!0),Gy(t)}}_onKeyUp(t){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!1);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!1),Gy(t)}}addEvents(){document.addEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}removeEvents(){document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}}const Hy=\\\\\\\"lock\\\\\\\",jy=\\\\\\\"change\\\\\\\",Wy=\\\\\\\"unlock\\\\\\\";function qy(){return{cook:!1,callback:t=>{Yy.PARAM_CALLBACK_updatePlayerParams(t)}}}const Xy=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.lock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_lockControls(t)}}),this.unlock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_unlockControls(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...qy()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...qy()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...qy()}),this.gravity=aa.VECTOR3([0,-30,0],{...qy()}),this.translateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.rotateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.jumpAllowed=aa.BOOLEAN(!0,{...qy()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...qy()}),this.runAllowed=aa.BOOLEAN(!0,{...qy()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...qy()}),this.startRotation=aa.VECTOR3([0,0,0],{...qy()}),this.reset=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]})}};class Yy extends qv{constructor(){super(...arguments),this.paramsConfig=Xy,this._controls_by_element_id=new Map}static type(){return rs.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Hy,Jo.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hy,Jo.BASE),new Zo(jy,Jo.BASE),new Zo(Wy,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new Ty(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.startRotation.copy(this.pv.startRotation),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(Hy,(()=>{this.dispatchEventToOutput(Hy,{})})),t.addEventListener(jy,(()=>{this.dispatchEventToOutput(jy,{})})),t.addEventListener(Wy,(()=>{this.dispatchEventToOutput(Wy,{})}))}updateRequired(){return!0}setupControls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.rotateSpeed=this.pv.rotateSpeed}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}unlockControls(){var t,e;const n=this._firstControls();n&&(n.unlock(),null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),null===(e=this._player)||void 0===e||e.stop())}lockControls(){const t=this._firstControls();t&&(this._player&&(this._corePlayerKeyEvents=this._corePlayerKeyEvents||new Vy(this._player),this._corePlayerKeyEvents.addEvents()),t.lock())}_firstControls(){let t;return this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t}static PARAM_CALLBACK_lockControls(t){t.lockControls()}static PARAM_CALLBACK_unlockControls(t){t.unlockControls()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var $y,Jy;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}($y||($y={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(Jy||(Jy={}));const Zy=new class extends la{constructor(){super(...arguments),this.maxCount=aa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=aa.BUTTON(null,{callback:t=>{Qy.PARAM_CALLBACK_reset(t)}})}};class Qy extends ka{constructor(){super(...arguments),this.paramsConfig=Zy,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo($y.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this)),new Zo($y.RESET,Jo.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Jy.OUT,Jo.BASE),new Zo(Jy.LAST,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class nx{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}}const ix=100,sx=301,rx=302,ox=303,ax=304,lx=306,cx=307,hx=1e3,ux=1001,dx=1002,px=1003,_x=1004,mx=1005,fx=1006,gx=1007,vx=1008,yx=1009,xx=1012,bx=1014,wx=1015,Tx=1016,Ax=1020,Mx=1022,Ex=1023,Sx=1026,Cx=1027,Nx=2300,Lx=2301,Ox=2302,Px=2400,Rx=2401,Ix=2402,Fx=2500,Dx=3e3,Bx=3001,zx=3007,kx=3002,Ux=7680,Gx=35044,Vx=35048,Hx=\\\\\\\"300 es\\\\\\\";class jx{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e>8&255]+Yx[t>>16&255]+Yx[t>>24&255]+\\\\\\\"-\\\\\\\"+Yx[255&e]+Yx[e>>8&255]+\\\\\\\"-\\\\\\\"+Yx[e>>16&15|64]+Yx[e>>24&255]+\\\\\\\"-\\\\\\\"+Yx[63&n|128]+Yx[n>>8&255]+\\\\\\\"-\\\\\\\"+Yx[n>>16&255]+Yx[n>>24&255]+Yx[255&i]+Yx[i>>8&255]+Yx[i>>16&255]+Yx[i>>24&255]).toUpperCase()}function Zx(t,e,n){return Math.max(e,Math.min(n,t))}function Qx(t,e){return(t%e+e)%e}function Kx(t,e,n){return(1-n)*t+n*e}function tb(t){return 0==(t&t-1)&&0!==t}function eb(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function nb(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var ib=Object.freeze({__proto__:null,DEG2RAD:qx,RAD2DEG:Xx,generateUUID:Jx,clamp:Zx,euclideanModulo:Qx,mapLinear:function(t,e,n,i,s){return i+(t-e)*(s-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:Kx,damp:function(t,e,n,i){return Kx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(Qx(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(Wx=t%2147483647),Wx=16807*Wx%2147483647,(Wx-1)/2147483646},degToRad:function(t){return t*qx},radToDeg:function(t){return t*Xx},isPowerOfTwo:tb,ceilPowerOfTwo:eb,floorPowerOfTwo:nb,setQuaternionFromProperEuler:function(t,e,n,i,s){const r=Math.cos,o=Math.sin,a=r(n/2),l=o(n/2),c=r((e+i)/2),h=o((e+i)/2),u=r((e-i)/2),d=o((e-i)/2),p=r((i-e)/2),_=o((i-e)/2);switch(s){case\\\\\\\"XYX\\\\\\\":t.set(a*h,l*u,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*h,l*u,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*u,l*d,a*h,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*h,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*h,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*h,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+s)}}});class sb{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),s=this.x-t.x,r=this.y-t.y;return this.x=s*n-r*i+t.x,this.y=s*i+r*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}sb.prototype.isVector2=!0;class rb{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function ob(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;ne&&(e=t[n]);return e}rb.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function ab(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let lb;class cb{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===lb&&(lb=ab(\\\\\\\"canvas\\\\\\\")),lb.width=t.width,lb.height=t.height;const n=lb.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=lb}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let hb=0;class ub extends jx{constructor(t=ub.DEFAULT_IMAGE,e=ub.DEFAULT_MAPPING,n=1001,i=1001,s=1006,r=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:hb++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=s,this.minFilter=r,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new sb(0,0),this.repeat=new sb(1,1),this.center=new sb(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new rb,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=Jx()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t1)switch(this.wrapS){case hx:t.x=t.x-Math.floor(t.x);break;case ux:t.x=t.x<0?0:1;break;case dx:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case hx:t.y=t.y-Math.floor(t.y);break;case ux:t.y=t.y<0?0:1;break;case dx:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function db(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?cb.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}ub.DEFAULT_IMAGE=void 0,ub.DEFAULT_MAPPING=300,ub.prototype.isTexture=!0;class pb{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(Zx(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isQuaternion=!0;class gb{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(yb.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(yb.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[3]*n+s[6]*i,this.y=s[1]*e+s[4]*n+s[7]*i,this.z=s[2]*e+s[5]*n+s[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=t.elements,r=1/(s[3]*e+s[7]*n+s[11]*i+s[15]);return this.x=(s[0]*e+s[4]*n+s[8]*i+s[12])*r,this.y=(s[1]*e+s[5]*n+s[9]*i+s[13])*r,this.z=(s[2]*e+s[6]*n+s[10]*i+s[14])*r,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,s=t.x,r=t.y,o=t.z,a=t.w,l=a*e+r*i-o*n,c=a*n+o*e-s*i,h=a*i+s*n-r*e,u=-s*e-r*n-o*i;return this.x=l*a+u*-s+c*-o-h*-r,this.y=c*a+u*-r+h*-s-l*-o,this.z=h*a+u*-o+l*-r-c*-s,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i,this.y=s[1]*e+s[5]*n+s[9]*i,this.z=s[2]*e+s[6]*n+s[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return vb.copy(this).projectOnVector(t),this.sub(vb)}reflect(t){return this.sub(vb.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(Zx(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}gb.prototype.isVector3=!0;const vb=new gb,yb=new fb;class xb{constructor(t=new gb(1/0,1/0,1/0),e=new gb(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.length;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.count;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,wb),wb.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Lb),Ob.subVectors(this.max,Lb),Ab.subVectors(t.a,Lb),Mb.subVectors(t.b,Lb),Eb.subVectors(t.c,Lb),Sb.subVectors(Mb,Ab),Cb.subVectors(Eb,Mb),Nb.subVectors(Ab,Eb);let e=[0,-Sb.z,Sb.y,0,-Cb.z,Cb.y,0,-Nb.z,Nb.y,Sb.z,0,-Sb.x,Cb.z,0,-Cb.x,Nb.z,0,-Nb.x,-Sb.y,Sb.x,0,-Cb.y,Cb.x,0,-Nb.y,Nb.x,0];return!!Ib(e,Ab,Mb,Eb,Ob)&&(e=[1,0,0,0,1,0,0,0,1],!!Ib(e,Ab,Mb,Eb,Ob)&&(Pb.crossVectors(Sb,Cb),e=[Pb.x,Pb.y,Pb.z],Ib(e,Ab,Mb,Eb,Ob)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return wb.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(wb).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(bb[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),bb[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),bb[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),bb[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),bb[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),bb[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),bb[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),bb[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(bb)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}xb.prototype.isBox3=!0;const bb=[new gb,new gb,new gb,new gb,new gb,new gb,new gb,new gb],wb=new gb,Tb=new xb,Ab=new gb,Mb=new gb,Eb=new gb,Sb=new gb,Cb=new gb,Nb=new gb,Lb=new gb,Ob=new gb,Pb=new gb,Rb=new gb;function Ib(t,e,n,i,s){for(let r=0,o=t.length-3;r<=o;r+=3){Rb.fromArray(t,r);const o=s.x*Math.abs(Rb.x)+s.y*Math.abs(Rb.y)+s.z*Math.abs(Rb.z),a=e.dot(Rb),l=n.dot(Rb),c=i.dot(Rb);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Fb=new xb,Db=new gb,Bb=new gb,zb=new gb;class kb{constructor(t=new gb,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Fb.setFromPoints(t).getCenter(n);let i=0;for(let e=0,s=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){zb.subVectors(t,this.center);const e=zb.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(zb.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Bb.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Db.copy(t.center).add(Bb)),this.expandByPoint(Db.copy(t.center).sub(Bb)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Ub=new gb,Gb=new gb,Vb=new gb,Hb=new gb,jb=new gb,Wb=new gb,qb=new gb;class Xb{constructor(t=new gb,e=new gb(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Ub)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Ub.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Ub.copy(this.direction).multiplyScalar(e).add(this.origin),Ub.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Gb.copy(t).add(e).multiplyScalar(.5),Vb.copy(e).sub(t).normalize(),Hb.copy(this.origin).sub(Gb);const s=.5*t.distanceTo(e),r=-this.direction.dot(Vb),o=Hb.dot(this.direction),a=-Hb.dot(Vb),l=Hb.lengthSq(),c=Math.abs(1-r*r);let h,u,d,p;if(c>0)if(h=r*a-o,u=r*o-a,p=s*c,h>=0)if(u>=-p)if(u<=p){const t=1/c;h*=t,u*=t,d=h*(h+r*u+2*o)+u*(r*h+u+2*a)+l}else u=s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u=-s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u<=-p?(h=Math.max(0,-(-r*s+o)),u=h>0?-s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l):u<=p?(h=0,u=Math.min(Math.max(-s,-a),s),d=u*(u+2*a)+l):(h=Math.max(0,-(r*s+o)),u=h>0?s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l);else u=r>0?-s:s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(h).add(this.origin),i&&i.copy(Vb).multiplyScalar(u).add(Gb),d}intersectSphere(t,e){Ub.subVectors(t.center,this.origin);const n=Ub.dot(this.direction),i=Ub.dot(Ub)-n*n,s=t.radius*t.radius;if(i>s)return null;const r=Math.sqrt(s-i),o=n-r,a=n+r;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Ub)}intersectTriangle(t,e,n,i,s){jb.subVectors(e,t),Wb.subVectors(n,t),qb.crossVectors(jb,Wb);let r,o=this.direction.dot(qb);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}Hb.subVectors(this.origin,t);const a=r*this.direction.dot(Wb.crossVectors(Hb,Wb));if(a<0)return null;const l=r*this.direction.dot(jb.cross(Hb));if(l<0)return null;if(a+l>o)return null;const c=-r*Hb.dot(qb);return c<0?null:this.at(c/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class Yb{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new Yb).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/$b.setFromMatrixColumn(t,0).length(),s=1/$b.setFromMatrixColumn(t,1).length(),r=1/$b.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*s,e[5]=n[5]*s,e[6]=n[6]*s,e[7]=0,e[8]=n[8]*r,e[9]=n[9]*r,e[10]=n[10]*r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,s=t.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(s),h=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(Zb,t,Qb)}lookAt(t,e,n){const i=this.elements;return ew.subVectors(t,e),0===ew.lengthSq()&&(ew.z=1),ew.normalize(),Kb.crossVectors(n,ew),0===Kb.lengthSq()&&(1===Math.abs(n.z)?ew.x+=1e-4:ew.z+=1e-4,ew.normalize(),Kb.crossVectors(n,ew)),Kb.normalize(),tw.crossVectors(ew,Kb),i[0]=Kb.x,i[4]=tw.x,i[8]=ew.x,i[1]=Kb.y,i[5]=tw.y,i[9]=ew.y,i[2]=Kb.z,i[6]=tw.z,i[10]=ew.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=$b.set(i[0],i[1],i[2]).length();const r=$b.set(i[4],i[5],i[6]).length(),o=$b.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],Jb.copy(this);const a=1/s,l=1/r,c=1/o;return Jb.elements[0]*=a,Jb.elements[1]*=a,Jb.elements[2]*=a,Jb.elements[4]*=l,Jb.elements[5]*=l,Jb.elements[6]*=l,Jb.elements[8]*=c,Jb.elements[9]*=c,Jb.elements[10]*=c,e.setFromRotationMatrix(Jb),n.x=s,n.y=r,n.z=o,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}Yb.prototype.isMatrix4=!0;const $b=new gb,Jb=new Yb,Zb=new gb(0,0,0),Qb=new gb(1,1,1),Kb=new gb,tw=new gb,ew=new gb,nw=new Yb,iw=new fb;class sw{constructor(t=0,e=0,n=0,i=sw.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,s=i[0],r=i[4],o=i[8],a=i[1],l=i[5],c=i[9],h=i[2],u=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Zx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(u,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Zx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-h,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Zx(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(-h,d),this._z=Math.atan2(-r,l)):(this._y=0,this._z=Math.atan2(a,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Zx(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(u,d),this._z=Math.atan2(a,s)):(this._x=0,this._z=Math.atan2(-r,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Zx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-h,s)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Zx(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(u,l),this._y=Math.atan2(o,s)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return nw.makeRotationFromQuaternion(t),this.setFromRotationMatrix(nw,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return iw.setFromEuler(this),this.setFromQuaternion(iw,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new gb(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}sw.prototype.isEuler=!0,sw.DefaultOrder=\\\\\\\"XYZ\\\\\\\",sw.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class rw{constructor(){this.mask=1}set(t){this.mask=1<1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,s){xw.subVectors(i,e),bw.subVectors(n,e),ww.subVectors(t,e);const r=xw.dot(xw),o=xw.dot(bw),a=xw.dot(ww),l=bw.dot(bw),c=bw.dot(ww),h=r*l-o*o;if(0===h)return s.set(-2,-1,-1);const u=1/h,d=(l*a-o*c)*u,p=(r*c-o*a)*u;return s.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Tw),Tw.x>=0&&Tw.y>=0&&Tw.x+Tw.y<=1}static getUV(t,e,n,i,s,r,o,a){return this.getBarycoord(t,e,n,i,Tw),a.set(0,0),a.addScaledVector(s,Tw.x),a.addScaledVector(r,Tw.y),a.addScaledVector(o,Tw.z),a}static isFrontFacing(t,e,n,i){return xw.subVectors(n,e),bw.subVectors(t,e),xw.cross(bw).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return xw.subVectors(this.c,this.b),bw.subVectors(this.a,this.b),.5*xw.cross(bw).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Lw.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Lw.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return Lw.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return Lw.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Lw.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;Aw.subVectors(i,n),Mw.subVectors(s,n),Sw.subVectors(t,n);const a=Aw.dot(Sw),l=Mw.dot(Sw);if(a<=0&&l<=0)return e.copy(n);Cw.subVectors(t,i);const c=Aw.dot(Cw),h=Mw.dot(Cw);if(c>=0&&h<=c)return e.copy(i);const u=a*h-c*l;if(u<=0&&a>=0&&c<=0)return r=a/(a-c),e.copy(n).addScaledVector(Aw,r);Nw.subVectors(t,s);const d=Aw.dot(Nw),p=Mw.dot(Nw);if(p>=0&&d<=p)return e.copy(s);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(Mw,o);const m=c*p-d*h;if(m<=0&&h-c>=0&&d-p>=0)return Ew.subVectors(s,i),o=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Ew,o);const f=1/(m+_+u);return r=_*f,o=u*f,e.copy(n).addScaledVector(Aw,r).addScaledVector(Mw,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Ow=0;class Pw extends jx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Ow++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=Ex,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=ix,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Ux,this.stencilZFail=Ux,this.stencilZPass=Ux,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==Ex&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}Pw.prototype.isMaterial=!0;const Rw={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},Iw={h:0,s:0,l:0},Fw={h:0,s:0,l:0};function Dw(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Bw(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function zw(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class kw{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=Qx(t,1),e=Zx(e,0,1),n=Zx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=Dw(s,i,t+1/3),this.g=Dw(s,i,t),this.b=Dw(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Rw[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Bw(t.r),this.g=Bw(t.g),this.b=Bw(t.b),this}copyLinearToSRGB(t){return this.r=zw(t.r),this.g=zw(t.g),this.b=zw(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?Ww:jw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new rb).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return Yw.makeRotationFromQuaternion(t),this.applyMatrix4(Yw),this}rotateX(t){return Yw.makeRotationX(t),this.applyMatrix4(Yw),this}rotateY(t){return Yw.makeRotationY(t),this.applyMatrix4(Yw),this}rotateZ(t){return Yw.makeRotationZ(t),this.applyMatrix4(Yw),this}translate(t,e,n){return Yw.makeTranslation(t,e,n),this.applyMatrix4(Yw),this}scale(t,e,n){return Yw.makeScale(t,e,n),this.applyMatrix4(Yw),this}lookAt(t){return $w.lookAt(t),$w.updateMatrix(),this.applyMatrix4($w.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Jw).negate(),this.translate(Jw.x,Jw.y,Jw.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),iT.copy(n.boundingSphere),iT.applyMatrix4(s),!1===t.ray.intersectsSphere(iT))return;if(eT.copy(s).invert(),nT.copy(t.ray).applyMatrix4(eT),null!==n.boundingBox&&!1===nT.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:gT.clone(),object:t}}(t,e,n,i,sT,rT,oT,fT);if(p){a&&(pT.fromBufferAttribute(a,c),_T.fromBufferAttribute(a,h),mT.fromBufferAttribute(a,u),p.uv=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb)),l&&(pT.fromBufferAttribute(l,c),_T.fromBufferAttribute(l,h),mT.fromBufferAttribute(l,u),p.uv2=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb));const t={a:c,b:h,c:u,normal:new gb,materialIndex:0};Lw.getNormal(sT,rT,oT,t.normal),p.face=t}return p}vT.prototype.isMesh=!0;class xT extends tT{constructor(t=1,e=1,n=1,i=1,s=1,r=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:s,depthSegments:r};const o=this;i=Math.floor(i),s=Math.floor(s),r=Math.floor(r);const a=[],l=[],c=[],h=[];let u=0,d=0;function p(t,e,n,i,s,r,p,_,m,f,g){const v=r/m,y=p/f,x=r/2,b=p/2,w=_/2,T=m+1,A=f+1;let M=0,E=0;const S=new gb;for(let r=0;r0?1:-1,c.push(S.x,S.y,S.z),h.push(a/m),h.push(1-r/f),M+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}AT.prototype.isShaderMaterial=!0;class MT extends yw{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new Yb,this.projectionMatrix=new Yb,this.projectionMatrixInverse=new Yb}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}MT.prototype.isCamera=!0;class ET extends MT{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*Xx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Xx*Math.atan(Math.tan(.5*qx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,s,r){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*qx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,s=-.5*i;const r=this.view;if(null!==this.view&&this.view.enabled){const t=r.fullWidth,o=r.fullHeight;s+=r.offsetX*i/t,e-=r.offsetY*n/o,i*=r.width/t,n*=r.height/o}const o=this.filmOffset;0!==o&&(s+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}ET.prototype.isPerspectiveCamera=!0;const ST=90;class CT extends yw{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new ET(ST,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new gb(1,0,0)),this.add(i);const s=new ET(ST,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new gb(-1,0,0)),this.add(s);const r=new ET(ST,1,t,e);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new gb(0,1,0)),this.add(r);const o=new ET(ST,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new gb(0,-1,0)),this.add(o);const a=new ET(ST,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new gb(0,0,1)),this.add(a);const l=new ET(ST,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new gb(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,s,r,o,a,l]=this.children,c=t.xr.enabled,h=t.getRenderTarget();t.xr.enabled=!1;const u=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,s),t.setRenderTarget(n,2),t.render(e,r),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=u,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(h),t.xr.enabled=c}}class NT extends ub{constructor(t,e,n,i,s,r,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:sx,n,i,s,r,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}NT.prototype.isCubeTexture=!0;class LT extends _b{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new NT(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:fx,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=Ex,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new xT(5,5,5),s=new AT({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:bT(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});s.uniforms.tEquirect.value=e;const r=new vT(i,s),o=e.minFilter;e.minFilter===vx&&(e.minFilter=fx);return new CT(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}LT.prototype.isWebGLCubeRenderTarget=!0;const OT=new gb,PT=new gb,RT=new rb;class IT{constructor(t=new gb(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=OT.subVectors(n,e).cross(PT.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(OT),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const s=-(t.start.dot(this.normal)+this.constant)/i;return s<0||s>1?null:e.copy(n).multiplyScalar(s).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||RT.getNormalMatrix(t),i=this.coplanarPoint(OT).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}IT.prototype.isPlane=!0;const FT=new kb,DT=new gb;class BT{constructor(t=new IT,e=new IT,n=new IT,i=new IT,s=new IT,r=new IT){this.planes=[t,e,n,i,s,r]}set(t,e,n,i,s,r){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],s=n[1],r=n[2],o=n[3],a=n[4],l=n[5],c=n[6],h=n[7],u=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,h-a,_-u,v-m).normalize(),e[1].setComponents(o+i,h+a,_+u,v+m).normalize(),e[2].setComponents(o+s,h+l,_+d,v+f).normalize(),e[3].setComponents(o-s,h-l,_-d,v-f).normalize(),e[4].setComponents(o-r,h-c,_-p,v-g).normalize(),e[5].setComponents(o+r,h+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),FT.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSprite(t){return FT.center.set(0,0,0),FT.radius=.7071067811865476,FT.applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)0?t.max.x:t.min.x,DT.y=i.normal.y>0?t.max.y:t.min.y,DT.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(DT)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function zT(){let t=null,e=!1,n=null,i=null;function s(e,r){n(e,r),i=t.requestAnimationFrame(s)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(s),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function kT(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,s){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n}\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n}\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\treturn sheenTint * ( D * V );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float vFogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\treturn irradiance;\\\\n}\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\t#endif\\\\n}\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n#ifdef IOR\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n};\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\treturn fab;\\\\n}\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n}\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normal_pars_fragment:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_pars_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",output_fragment:\\\\\\\"#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\t}\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include \\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",depth_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main () {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshnormal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\"},VT={common:{diffuse:{value:new kw(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new rb},uv2Transform:{value:new rb},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new sb(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new kw(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new kw(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}},sprite:{diffuse:{value:new kw(16777215)},opacity:{value:1},center:{value:new sb(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}}},HT={basic:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.fog]),vertexShader:GT.meshbasic_vert,fragmentShader:GT.meshbasic_frag},lambert:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshlambert_vert,fragmentShader:GT.meshlambert_frag},phong:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},specular:{value:new kw(1118481)},shininess:{value:30}}]),vertexShader:GT.meshphong_vert,fragmentShader:GT.meshphong_frag},standard:{uniforms:wT([VT.common,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.roughnessmap,VT.metalnessmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag},toon:{uniforms:wT([VT.common,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.gradientmap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshtoon_vert,fragmentShader:GT.meshtoon_frag},matcap:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,{matcap:{value:null}}]),vertexShader:GT.meshmatcap_vert,fragmentShader:GT.meshmatcap_frag},points:{uniforms:wT([VT.points,VT.fog]),vertexShader:GT.points_vert,fragmentShader:GT.points_frag},dashed:{uniforms:wT([VT.common,VT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:GT.linedashed_vert,fragmentShader:GT.linedashed_frag},depth:{uniforms:wT([VT.common,VT.displacementmap]),vertexShader:GT.depth_vert,fragmentShader:GT.depth_frag},normal:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,{opacity:{value:1}}]),vertexShader:GT.meshnormal_vert,fragmentShader:GT.meshnormal_frag},sprite:{uniforms:wT([VT.sprite,VT.fog]),vertexShader:GT.sprite_vert,fragmentShader:GT.sprite_frag},background:{uniforms:{uvTransform:{value:new rb},t2D:{value:null}},vertexShader:GT.background_vert,fragmentShader:GT.background_frag},cube:{uniforms:wT([VT.envmap,{opacity:{value:1}}]),vertexShader:GT.cube_vert,fragmentShader:GT.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:GT.equirect_vert,fragmentShader:GT.equirect_frag},distanceRGBA:{uniforms:wT([VT.common,VT.displacementmap,{referencePosition:{value:new gb},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:GT.distanceRGBA_vert,fragmentShader:GT.distanceRGBA_frag},shadow:{uniforms:wT([VT.lights,VT.fog,{color:{value:new kw(0)},opacity:{value:1}}]),vertexShader:GT.shadow_vert,fragmentShader:GT.shadow_frag}};function jT(t,e,n,i,s){const r=new kw(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===lx)?(void 0===a&&(a=new vT(new xT(1,1,1),new AT({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:bT(HT.cube.uniforms),vertexShader:HT.cube.vertexShader,fragmentShader:HT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new vT(new UT(2,2),new AT({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:bT(HT.background.uniforms),vertexShader:HT.background.vertexShader,fragmentShader:HT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function WT(t,e,n,i){const s=t.getParameter(34921),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(34930),u=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(36183):0}}function YT(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new IT,a=new rb,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length0){const o=t.getRenderTarget(),a=new LT(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}HT.physical={uniforms:wT([HT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new sb(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new kw(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new sb},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new kw(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new kw(1,1,1)},specularTintMap:{value:null}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag};class JT extends MT{constructor(t=-1,e=1,n=1,i=-1,s=.1,r=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=s,this.far=r,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,s,r){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let s=n-t,r=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;s+=t*this.view.offsetX,r=s+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(s,r,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}JT.prototype.isOrthographicCamera=!0;class ZT extends AT{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ZT.prototype.isRawShaderMaterial=!0;const QT=Math.pow(2,8),KT=[.125,.215,.35,.446,.526,.582],tA=5+KT.length,eA=20,nA={[Dx]:0,[Bx]:1,[kx]:2,3004:3,3005:4,3006:5,[zx]:6},iA=new JT,{_lodPlanes:sA,_sizeLods:rA,_sigmas:oA}=_A(),aA=new kw;let lA=null;const cA=(1+Math.sqrt(5))/2,hA=1/cA,uA=[new gb(1,1,1),new gb(-1,1,1),new gb(1,1,-1),new gb(-1,1,-1),new gb(0,cA,hA),new gb(0,cA,-hA),new gb(hA,0,cA),new gb(-hA,0,cA),new gb(cA,hA,0),new gb(-cA,hA,0)];class dA{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new gb(0,1,0);return new ZT({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(eA),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){lA=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=vA(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=gA(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?QT:0,QT,QT),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===Ex&&e.type===yx&&e.encoding===Bx?t.value=nA[3e3]:t.value=nA[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=vA()):null==this._equirectShader&&(this._equirectShader=gA());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new vT(sA[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),fA(e,0,0,3*QT,2*QT),n.setRenderTarget(e),n.render(s,iA)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eeA&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,iA)}}function pA(t){return void 0!==t&&t.type===yx&&(t.encoding===Dx||t.encoding===Bx||t.encoding===zx)}function _A(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=KT[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new tT;y.setAttribute(\\\\\\\"position\\\\\\\",new Hw(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new Hw(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new Hw(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function mA(t){const e=new _b(3*QT,3*QT,t);return e.texture.mapping=lx,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function fA(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function gA(){const t=new sb(1,1);return new ZT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function vA(){return new ZT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function yA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function xA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function bA(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const 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XM(t,e){const n=GT[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return qM(n)}const YM=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,$M=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function JM(t){return t.replace($M,QM).replace(YM,ZM)}function ZM(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. 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i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[KM(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define 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y=v+g+a,x=FM(s,35633,v+f+o),b=FM(s,35632,y);if(s.attachShader(m,x),s.attachShader(m,b),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(x).trim(),n=s.getShaderInfoLog(b).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,35714)){i=!1;const e=zM(s,x,\\\\\\\"vertex\\\\\\\"),n=zM(s,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info 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s=0;s0,C=r.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:r.type,vertexShader:A,fragmentShader:M,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==E?f(E.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===lx||b.mapping===cx),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:1===r.normalMapType,tangentSpaceNormalMap:0===r.normalMapType,clearcoat:C,clearcoatMap:C&&!!r.clearcoatMap,clearcoatRoughnessMap:C&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:S,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:h,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:2===r.side,flipSided:1===r.side,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||iE),s.length>1&&s.sort(e||sE),r.length>1&&r.sort(e||sE)}}}function oE(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new rE(t),e.set(n,[s])):i>=e.get(n).length?(s=new rE(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function aE(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new gb,color:new kw};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new gb,direction:new gb,color:new kw,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new gb,color:new kw,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new gb,skyColor:new kw,groundColor:new kw};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new kw,position:new gb,halfWidth:new gb,halfHeight:new gb}}return t[e.id]=n,n}}}let lE=0;function cE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function hE(t,e){const n=new aE,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new gb);const r=new gb,o=new Yb,a=new Yb;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(cE);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_FLOAT_1,s.rectAreaLTC2=VT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_HALF_1,s.rectAreaLTC2=VT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=lE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new uE(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class pE extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}pE.prototype.isMeshDepthMaterial=!0;class _E extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new gb,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}_E.prototype.isMeshDistanceMaterial=!0;function mE(t,e,n){let i=new BT;const s=new sb,r=new sb,o=new pb,a=new pE({depthPacking:3201}),l=new _E,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new AT({uniforms:{shadow_pass:{value:null},resolution:{value:new sb},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include \\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const _=new tT;_.setAttribute(\\\\\\\"position\\\\\\\",new Hw(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new vT(_,d),f=this;function g(n,i){const s=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,d.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,p,m,null)}function v(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===h?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:u[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function y(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&3===this.type){const t={minFilter:fx,magFilter:fx,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new _b(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:px,magFilter:px,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),y(n,a,u.camera,c,this.type)}u.isPointLightShadow||3!==this.type||g(u,a),u.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function fE(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new pb;let i=null;const s=new pb(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(t){t?B(2929):z(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?B(2960):z(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,_=null,m=null,f=null,g=null,v=null,y=!1,x=null,b=null,w=null,T=null,A=null;const M=t.getParameter(35661);let E=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),E=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),E=S>=2);let N=null,L={};const O=t.getParameter(3088),P=t.getParameter(2978),R=(new pb).fromArray(O),I=(new pb).fromArray(P);function F(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?nb:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+r+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return 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0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.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s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function ME(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=ab(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 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i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=P;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&ut.readPixels(e,n,i,s,ct.convert(a),ct.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. 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s=e.image.width,r=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ut.pixelStorei(37440,n.flipY),ut.pixelStorei(37441,n.premultiplyAlpha),ut.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ut.texSubImage2D(3553,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?ut.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ut.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ut.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let h;if(i.isDataTexture3D)X.setTexture3D(i,0),h=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),h=35866}ut.pixelStorei(37440,i.flipY),ut.pixelStorei(37441,i.premultiplyAlpha),ut.pixelStorei(3317,i.unpackAlignment);const u=ut.getParameter(3314),d=ut.getParameter(32878),p=ut.getParameter(3316),_=ut.getParameter(3315),f=ut.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ut.pixelStorei(3314,g.width),ut.pixelStorei(32878,g.height),ut.pixelStorei(3316,t.min.x),ut.pixelStorei(3315,t.min.y),ut.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),ut.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,g.data)):ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g),ut.pixelStorei(3314,u),ut.pixelStorei(32878,d),ut.pixelStorei(3316,p),ut.pixelStorei(3315,_),ut.pixelStorei(32877,f),0===s&&i.generateMipmaps&&ut.generateMipmap(h),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ht.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends ME{}).prototype.isWebGL1Renderer=!0;class EE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.density=e}clone(){return new EE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}EE.prototype.isFogExp2=!0;class SE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.near=e,this.far=n}clone(){return new SE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}SE.prototype.isFog=!0;class CE extends yw{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}CE.prototype.isScene=!0;class NE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Gx,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=Jx()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,s=this.stride;it.far||e.push({distance:a,point:IE.clone(),uv:Lw.getUV(IE,UE,GE,VE,HE,jE,WE,new sb),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function XE(t,e,n,i,s,r){BE.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(zE.x=r*BE.x-s*BE.y,zE.y=s*BE.x+r*BE.y):zE.copy(BE),t.copy(e),t.x+=zE.x,t.y+=zE.y,t.applyMatrix4(kE)}qE.prototype.isSprite=!0;const YE=new gb,$E=new pb,JE=new pb,ZE=new gb,QE=new Yb;class KE extends vT{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new Yb,this.bindMatrixInverse=new Yb}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new pb,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;u.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(u);dt.far||e.push({distance:d,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;na)continue;u.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(u);it.far||e.push({distance:i,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}_S.prototype.isLine=!0;const mS=new gb,fS=new gb;class gS extends _S{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function MS(t,e,n,i,s,r,o){const a=bS.distanceSqToPoint(t);if(as.far)return;r.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}AS.prototype.isPoints=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:Mx,this.minFilter=void 0!==r?r:fx,this.magFilter=void 0!==s?s:fx,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class ES extends ub{constructor(t,e,n,i,s,r,o,a,l,c,h,u){super(null,r,o,a,l,c,i,s,h,u),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}ES.prototype.isCompressedTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l,c){if((c=void 0!==c?c:Sx)!==Sx&&c!==Cx)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===Sx&&(n=xx),void 0===n&&c===Cx&&(n=Ax),super(null,i,s,r,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:px,this.minFilter=void 0!==a?a:px,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new gb,new gb,new gb,new Lw;class SS{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),s=0;e.push(0);for(let r=1;r<=t;r++)n=this.getPoint(r/t),s+=n.distanceTo(i),e.push(s),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const s=n.length;let r;r=e||t*n[s-1];let o,a=0,l=s-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-r,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===r)return i/(s-1);const c=n[i];return(i+(r-c)/(n[i+1]-c))/(s-1)}getTangent(t,e){const n=1e-4;let i=t-n,s=t+n;i<0&&(i=0),s>1&&(s=1);const r=this.getPoint(i),o=this.getPoint(s),a=e||(r.isVector2?new sb:new gb);return a.copy(o).sub(r).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new gb,i=[],s=[],r=[],o=new gb,a=new Yb;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new gb)}s[0]=new gb,r[0]=new gb;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),h=Math.abs(i[0].y),u=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),h<=l&&(l=h,n.set(0,1,0)),u<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),s[0].crossVectors(i[0],o),r[0].crossVectors(i[0],s[0]);for(let e=1;e<=t;e++){if(s[e]=s[e-1].clone(),r[e]=r[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(Zx(i[e-1].dot(i[e]),-1,1));s[e].applyMatrix4(a.makeRotationAxis(o,t))}r[e].crossVectors(i[e],s[e])}if(!0===e){let e=Math.acos(Zx(s[0].dot(s[t]),-1,1));e/=t,i[0].dot(o.crossVectors(s[0],s[t]))>0&&(e=-e);for(let n=1;n<=t;n++)s[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),r[n].crossVectors(i[n],s[n])}return{tangents:i,normals:s,binormals:r}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class CS extends SS{constructor(t=0,e=0,n=1,i=1,s=0,r=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=s,this.aEndAngle=r,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new sb,i=2*Math.PI;let s=this.aEndAngle-this.aStartAngle;const r=Math.abs(s)i;)s-=i;s0?0:(Math.floor(Math.abs(l)/s)+1)*s:0===c&&l===s-1&&(l=s-2,c=1),this.closed||l>0?o=i[(l-1)%s]:(OS.subVectors(i[0],i[1]).add(i[0]),o=OS);const 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hC(t.prev,t,t.next)<0?hC(t,e,t.next)>=0&&hC(t,t.prev,e)>=0:hC(t,e,t.prev)<0||hC(t,t.next,e)<0}function fC(t,e){const n=new yC(t.i,t.x,t.y),i=new yC(e.i,e.x,e.y),s=t.next,r=e.prev;return t.next=e,e.prev=t,n.next=s,s.prev=n,i.next=n,n.prev=i,r.next=i,i.prev=r,i}function gC(t,e,n,i){const s=new yC(t,e,n);return i?(s.next=i.next,s.prev=i,i.next.prev=s,i.next=s):(s.prev=s,s.next=s),s}function vC(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function yC(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class xC{static area(t){const e=t.length;let n=0;for(let i=e-1,s=0;s2&&t[e-1].equals(t[0])&&t.pop()}function wC(t,e){for(let n=0;nNumber.EPSILON){const u=Math.sqrt(h),d=Math.sqrt(l*l+c*c),p=e.x-a/u,_=e.y+o/u,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,s=_+a*m-t.y;const f=i*i+s*s;if(f<=2)return new sb(i,s);r=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new sb(i/r,s/r)}const O=[];for(let t=0,e=E.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+d;for(let t=0,e=E.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*p;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}CC.prototype.isMeshPhysicalMaterial=!0;class NC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new kw(16777215),this.specular=new kw(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}NC.prototype.isMeshPhongMaterial=!0;class LC extends Pw{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}LC.prototype.isMeshToonMaterial=!0;class OC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}OC.prototype.isMeshNormalMaterial=!0;class PC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}PC.prototype.isMeshLambertMaterial=!0;class RC extends Pw{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new kw(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}RC.prototype.isMeshMatcapMaterial=!0;class IC extends lS{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}IC.prototype.isLineDashedMaterial=!0;const FC={arraySlice:function(t,e,n){return FC.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,s=new t.constructor(i);for(let r=0,o=0;o!==i;++r){const i=n[r]*e;for(let n=0;n!==e;++n)s[o++]=t[i+n]}return s},flattenJSON:function(t,e,n,i){let s=1,r=t[0];for(;void 0!==r&&void 0===r[i];)r=t[s++];if(void 0===r)return;let o=r[i];if(void 0!==o)if(Array.isArray(o))do{o=r[i],void 0!==o&&(e.push(r.time),n.push.apply(n,o)),r=t[s++]}while(void 0!==r);else if(void 0!==o.toArray)do{o=r[i],void 0!==o&&(e.push(r.time),o.toArray(n,n.length)),r=t[s++]}while(void 0!==r);else do{o=r[i],void 0!==o&&(e.push(r.time),n.push(o)),r=t[s++]}while(void 0!==r)},subclip:function(t,e,n,i,s=30){const r=t.clone();r.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;nr.tracks[t].times[0]&&(a=r.tracks[t].times[0]);for(let t=0;t=i.times[u]){const t=u*l+a,e=t+l-a;d=FC.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(r),d=FC.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===s){(new fb).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;te;)--r;if(++r,0!==s||r!==i){s>=r&&(r=Math.max(r,1),s=r-1);const t=this.getValueSize();this.times=FC.arraySlice(n,s,r),this.values=FC.arraySlice(this.values,s*t,r*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,s=n.length;0===s&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let r=null;for(let e=0;e!==s;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==r&&r>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,r),t=!1;break}r=i}if(void 0!==i&&FC.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=FC.arraySlice(this.times),e=FC.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Ox,s=t.length-1;let r=1;for(let o=1;o0){t[r]=t[s];for(let t=s*n,i=r*n,o=0;o!==n;++o)e[i+o]=e[t+o];++r}return r!==t.length?(this.times=FC.arraySlice(t,0,r),this.values=FC.arraySlice(e,0,r*n)):(this.times=t,this.values=e),this}clone(){const t=FC.arraySlice(this.times,0),e=FC.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}UC.prototype.TimeBufferType=Float32Array,UC.prototype.ValueBufferType=Float32Array,UC.prototype.DefaultInterpolation=Lx;class GC extends UC{}GC.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",GC.prototype.ValueBufferType=Array,GC.prototype.DefaultInterpolation=Nx,GC.prototype.InterpolantFactoryMethodLinear=void 0,GC.prototype.InterpolantFactoryMethodSmooth=void 0;class VC extends UC{}VC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class HC extends UC{}HC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class jC extends DC{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)fb.slerpFlat(s,0,r,l-o,r,l,a);return s}}class WC extends UC{InterpolantFactoryMethodLinear(t){return new jC(this.times,this.values,this.getValueSize(),t)}}WC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",WC.prototype.DefaultInterpolation=Lx,WC.prototype.InterpolantFactoryMethodSmooth=void 0;class qC extends UC{}qC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",qC.prototype.ValueBufferType=Array,qC.prototype.DefaultInterpolation=Nx,qC.prototype.InterpolantFactoryMethodLinear=void 0,qC.prototype.InterpolantFactoryMethodSmooth=void 0;class XC extends UC{}XC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class YC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=Jx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,s=n.length;t!==s;++t)e.push($C(n[t]).scale(i));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(UC.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const s=e.length,r=[];for(let t=0;t1){const t=r[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const r=[];for(const t in i)r.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return r}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,s){if(0!==n.length){const r=[],o=[];FC.flattenJSON(n,r,o,i),0!==r.length&&s.push(new t(e,r,o))}},i=[],s=t.name||\\\\\\\"default\\\\\\\",r=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){fb.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;fb.multiplyQuaternionsFlat(t,r,t,e,t,n),fb.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}const ON=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",PN=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),RN=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",IN=\\\\\\\"[^\\\\\\\"+ON.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",FN=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",RN),DN=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",IN),BN=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",RN),zN=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",RN),kN=new RegExp(\\\\\\\"^\\\\\\\"+FN+DN+BN+zN+\\\\\\\"$\\\\\\\"),UN=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class GN{constructor(t,e,n){this.path=e,this.parsedPath=n||GN.parseTrackName(e),this.node=GN.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new GN.Composite(t,e,n):new GN(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(PN,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=kN.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==UN.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case Fx:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=2202===n;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(2200===n){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Rx,i.endingEnd=Rx):(i.endingStart=t?this.zeroSlopeAtStart?Rx:Px:Ix,i.endingEnd=e?this.zeroSlopeAtEnd?Rx:Px:Ix)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}(class extends jx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new LN(GN.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;tthis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return jN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}WN.prototype.isBox2=!0;const qN=new gb,XN=new gb;class YN{constructor(t=new gb,e=new gb){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){qN.subVectors(t,this.start),XN.subVectors(this.end,this.start);const n=XN.dot(XN);let i=XN.dot(qN)/n;return e&&(i=Zx(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends yw{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const $N=new gb,JN=new Yb,ZN=new Yb;function QN(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n8&&t<16&&e[n].split(t+1),0!==i&&this.subTrees.push(e[n])}return this}build(){return this.calcBox(),this.split(0),this}getRayTriangles(t,e){for(let n=0;n0)for(let t=0;t0&&i>0||n<-t.radius&&i<-t.radius)return!1;const s=Math.abs(n/(Math.abs(n)+Math.abs(i))),r=sL.copy(t.start).lerp(t.end,s);if(e.containsPoint(r))return{normal:oL.normal.clone(),point:r.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=aL.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e0)for(let t=0;t0)for(let t=0;ta&&(i=o.clone().add(t.origin),s=a,n=e[r])}}return s<1e100&&{distance:s,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new dL(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}setJumpParams(t){}setGravity(t){}setPlayerMass(t){}}const _L={rotateSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI}},mL={type:\\\\\\\"change\\\\\\\"},fL=new p.a,gL=new ny;class vL extends pL{constructor(t,e,n){super(),this._camera=t,this.domElement=e,this.player=n,this.translationData={direction:new p.a},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this)},this._startCameraRotation=new Xv.a,this._rotationSpeed=_L.rotateSpeed,this._rotationRange={min:_L.rotationRange.min,max:_L.rotationRange.max},this._azimuthalAngle=0,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),s=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${s}px`,t.style.left=`${s}px`,t}_addEvents(){var t;nx.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;nx.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this._translateDomElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=rr.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this._computeAzimuthalAngle(),this.dispatchEvent(mL)}_computeAzimuthalAngle(){this._camera.updateMatrixWorld(),fL.set(0,0,1),this._camera.localToWorld(fL),fL.sub(this._camera.position),gL.setFromVector3(fL),this._azimuthalAngle=gL.theta}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);if(!this._getTouch(t,this._translateDomElement))return;this._translateDomElementRect=this._translateDomElement.getBoundingClientRect();const e=this._translateDomElementRect.left+.5*this._translateDomElementRect.width,n=this._translateDomElementRect.top+.5*this._translateDomElementRect.height;this._translationStartPosition.set(e,n)}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationDelta.x/this._translateDomElementRect.width*.5,this.translationData.direction.z=-this._translationDelta.y/this._translateDomElementRect.height*.5,this._updatePlayerTranslate(),this.dispatchEvent(mL))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.z=0,this._updatePlayerTranslate()}_updatePlayerTranslate(){if(!this.player)return;const t=this.translationData.direction;this.player.setForward(!1),this.player.setBackward(!1),this.player.setLeft(!1),this.player.setRight(!1);const e=Math.abs(t.x),n=Math.abs(t.z),i=n-e;function s(e){t.z>0&&e.setForward(!0),t.z<0&&e.setBackward(!0)}function r(e){t.x>0&&e.setRight(!0),t.x<0&&e.setLeft(!0)}i>0?(s(this.player),i<.5*n&&r(this.player)):(r(this.player),i<.5*e&&s(this.player))}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}_getTouch(t,e){for(let n=0;n{AL.PARAM_CALLBACK_updatePlayerParams(t)}}}const TL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...wL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...wL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...wL()}),this.gravity=aa.VECTOR3([0,-30,0],{...wL()}),this.translateSpeed=aa.FLOAT(1),this.rotateSpeed=aa.FLOAT(_L.rotateSpeed),this.updateCollider=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...wL()}),this.startRotation=aa.VECTOR3([0,0,0],{...wL()}),this.reset=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(\\\\\\\"-$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]})}};class AL extends qv{constructor(){super(...arguments),this.paramsConfig=TL,this._controls_by_element_id=new Map}static type(){return rs.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(yL,Jo.BASE),new Zo(xL,Jo.BASE),new Zo(bL,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new vL(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=!1,this._player.runAllowed=!1,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(yL,(()=>{this.dispatchEventToOutput(yL,{})})),t.addEventListener(xL,(()=>{this.dispatchEventToOutput(xL,{})})),t.addEventListener(bL,(()=>{this.dispatchEventToOutput(bL,{})}))}updateRequired(){return!0}setupControls(t){t.setRotationSpeed(this.pv.rotateSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle})}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var ML;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(ML||(ML={}));const EL=[ML.ALL_TOGETHER,ML.BATCH];const SL=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{CL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=aa.BOOLEAN(0),this.cookMode=aa.INTEGER(EL.indexOf(ML.ALL_TOGETHER),{menu:{entries:EL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=aa.INTEGER(1,{visibleIf:{cookMode:EL.indexOf(ML.BATCH)},separatorAfter:!0}),this.updateResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_print_resolve(t)}})}};class CL extends ka{constructor(){super(...arguments),this.paramsConfig=SL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(CL.INPUT_TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(CL.OUTPUT_FIRST_NODE,Jo.BASE),new Zo(CL.OUTPUT_EACH_NODE,Jo.BASE),new Zo(CL.OUTPUT_ALL_NODES,Jo.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=EL[this.pv.cookMode];switch(t){case ML.ALL_TOGETHER:return this._cook_nodes_all_together();case ML.BATCH:return this._cook_nodes_batch()}ls.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(CL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var NL,LL;CL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",CL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",CL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",CL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(NL||(NL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(LL||(LL={}));const OL=new class extends la{};class PL extends ka{constructor(){super(...arguments),this.paramsConfig=OL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(NL.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(LL.OUT,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(LL.OUT,t)}}const RL=\\\\\\\"init\\\\\\\",IL=\\\\\\\"dispose\\\\\\\",FL=\\\\\\\"reset\\\\\\\";function DL(){return{callback:t=>{GL.PARAM_CALLBACK_updatePlayerParams(t)}}}const BL=new p.a,zL=new p.a,kL=new ny;const UL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.playerObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{types:[is.PERSPECTIVE,is.ORTHOGRAPHIC],context:ts.OBJ}}),this.initPlayer=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_initPlayer(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...DL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...DL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...DL()}),this.gravity=aa.VECTOR3([0,-30,0],{...DL()}),this.speed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.jumpAllowed=aa.BOOLEAN(!0,{...DL()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...DL()}),this.runAllowed=aa.BOOLEAN(!0,{...DL()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_updateCollider(t)}}),this.mesh=aa.FOLDER(),this.useMesh=aa.BOOLEAN(!0,{callback:t=>{GL.PARAM_CALLBACK_updatePlayerMesh(t)}}),this.material=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},callback:t=>{GL.PARAM_CALLBACK_updatePlayerMaterial(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,5,0],{...DL()}),this.reset=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_resetPlayer(t)}})}};class GL extends ka{constructor(){super(...arguments),this.paramsConfig=UL}static type(){return rs.PLAYER}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(RL,Jo.BASE,this._initPlayer.bind(this)),new Zo(IL,Jo.BASE,this._disposePlayer.bind(this)),new Zo(FL,Jo.BASE,this._resetPlayer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(RL,Jo.BASE),new Zo(IL,Jo.BASE),new Zo(FL,Jo.BASE)])}async _initPlayer(){if(this._player=this._player||await this._createPlayer(),!this._player)return void this.states.error.set(\\\\\\\"could not create player\\\\\\\");this._updatePlayerMesh(),this._updatePlayerMaterial(),this._updatePlayerParams(),this._corePlayerKeyEvents=new Vy(this._player),this._corePlayerKeyEvents.addEvents(),this._player.reset();const t=this._player;this.scene().registerOnBeforeTick(this._callbackName(),(e=>{t.setAzimuthalAngle(this._getAzimuthalAngle()),t.update(e)})),this.dispatchEventToOutput(RL,{})}_callbackName(){return`event/PlayerControls-${this.graphNodeId()}`}_disposePlayer(){var t;this._player&&(null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),this.scene().unRegisterOnBeforeTick(this._callbackName())),this.dispatchEventToOutput(IL,{})}_resetPlayer(){this._player&&this._player.reset(),this.dispatchEventToOutput(FL,{})}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.speed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}_updatePlayerMesh(){this._player&&this._player.setUsePlayerMesh(this.pv.useMesh)}async _updatePlayerMaterial(){if(!this._player)return;const t=this.pv.material.nodeWithContext(ts.MAT);if(!t)return void this.states.error.set(\\\\\\\"material node not found\\\\\\\");const e=(await t.compute()).material();this._player.setMaterial(e)}async _createPlayer(){const t=this.pv.playerObject.nodeWithContext(ts.OBJ);if(!t)return void this.states.error.set(\\\\\\\"player node not found\\\\\\\");const e=this.pv.camera.nodeWithContext(ts.OBJ);if(!e)return void this.states.error.set(\\\\\\\"invalid camera node\\\\\\\");this._cameraObject=e.object;const n=t.object,i=await this._getCollider();if(!i)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:n,collider:i,meshName:this.path()})}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_getAzimuthalAngle(){if(!this._cameraObject||!this._player)return 0;const t=this._cameraObject.position,e=this._player.object.position;return BL.copy(t),zL.copy(e),BL.sub(zL),kL.setFromVector3(BL),kL.theta}static PARAM_CALLBACK_initPlayer(t){t._initPlayer()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_updatePlayerMaterial(t){t._updatePlayerMaterial()}static PARAM_CALLBACK_updatePlayerMesh(t){t._updatePlayerMesh()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var VL,HL=n(39),jL=n(36);class WL{constructor(t,e,n=0,i=1/0){this.ray=new HL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new jL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return XL(t,this,n,e),n.sort(qL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,s=t.length;i{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=JL[this._node.pv.intersectWith];switch(e){case $L.GEOMETRY:return this._intersect_with_geometry(t);case $L.PLANE:return this._intersect_with_plane(t)}ls.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=zs[this._node.pv.geoAttributeType],n=QL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Bs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Bs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ls.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Ls(t.object.constructor)){case Cs.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Cs.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const s=i.getAttribute(e);if(s){switch(n){case Bs.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(s,t.face.a),this._uvB.fromBufferAttribute(s,t.face.b),this._uvC.fromBufferAttribute(s,t.face.c),t.uv=Ks.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Bs.STRING:{const t=new _r(i).points()[0];return t?t.stringAttribValue(e):void 0}}ls.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Bs.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Bs.STRING:{const n=new _r(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ls.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(li.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Er.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Er.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(ts.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=lO[this._node.pv.targetType];switch(t){case aO.NODE:return this._update_target_from_node();case aO.SCENE_GRAPH:return this._update_target_from_scene_graph()}ls.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(ts.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}QL._vA=new p.a,QL._vB=new p.a,QL._vC=new p.a,QL._uvA=new d.a,QL._uvB=new d.a,QL._uvC=new d.a,QL._hitUV=new d.a;class KL{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,s=i.renderController;if(s){if(this._render_target=this._render_target||new Q(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,r=s.resolved_scene||i.scene().threejsScene(),o=s.renderer(n);this._modify_scene_and_renderer(r,o),o.setRenderTarget(this._render_target),o.clear(),o.render(r,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(r,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==ts.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const tO=1e3/60;var eO;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(eO||(eO={}));const nO=[eO.CPU,eO.GPU];function iO(t={}){return t.mode=nO.indexOf(eO.CPU),{visibleIf:t}}function sO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.GEOMETRY),{visibleIf:t}}function rO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.PLANE),{visibleIf:t}}function oO(t={}){return t.mode=nO.indexOf(eO.GPU),{visibleIf:t}}var aO;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(aO||(aO={}));const lO=[aO.SCENE_GRAPH,aO.NODE];const cO=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(nO.indexOf(eO.CPU),{menu:{entries:nO.map(((t,e)=>({name:t,value:e})))}}),this.mouse=aa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=aa.BOOLEAN(0),this.overrideRay=aa.BOOLEAN(0,{visibleIf:{mode:nO.indexOf(eO.CPU),overrideCamera:1}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=aa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=aa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=aa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{KL.PARAM_CALLBACK_update_material(t)},...oO()}),this.pixelValue=aa.VECTOR4([0,0,0,0],{cook:!1,...oO()}),this.hitThreshold=aa.FLOAT(.5,{cook:!1,...oO()}),this.intersectWith=aa.INTEGER(JL.indexOf($L.GEOMETRY),{menu:{entries:JL.map(((t,e)=>({name:t,value:e})))},...iO()}),this.pointsThreshold=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...iO()}),this.planeDirection=aa.VECTOR3([0,1,0],{...rO()}),this.planeOffset=aa.FLOAT(0,{...rO()}),this.targetType=aa.INTEGER(0,{menu:{entries:lO.map(((t,e)=>({name:t,value:e})))},...sO()}),this.targetNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.NODE)})}),this.objectMask=aa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=aa.BUTTON(null,{callback:(t,e)=>{QL.PARAM_CALLBACK_print_resolve(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.traverseChildren=aa.BOOLEAN(!0,{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO(),separatorAfter:!0}),this.tpositionTarget=aa.BOOLEAN(0,{cook:!1,...iO()}),this.position=aa.VECTOR3([0,0,0],{cook:!1,...iO({tpositionTarget:0})}),this.positionTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tpositionTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.tvelocity=aa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=aa.BOOLEAN(0,{cook:!1,...iO({tvelocity:1})}),this.velocity=aa.VECTOR3([0,0,0],{cook:!1,...iO({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tvelocity:1,tvelocityTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.geoAttribute=aa.BOOLEAN(0,sO()),this.geoAttributeName=aa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...sO({geoAttribute:1})}),this.geoAttributeType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},...sO({geoAttribute:1})}),this.geoAttributeValue1=aa.FLOAT(0,{cook:!1,...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.NUMERIC)})}),this.geoAttributeValues=aa.STRING(\\\\\\\"\\\\\\\",{...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.STRING)})})}};class hO extends ka{constructor(){super(...arguments),this.paramsConfig=cO,this.cpuController=new QL(this),this.gpuController=new KL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(hO.INPUT_TRIGGER,Jo.BASE,this._process_trigger_event_throttled.bind(this)),new Zo(hO.INPUT_MOUSE,Jo.MOUSE,this._process_mouse_event.bind(this)),new Zo(hO.INPUT_UPDATE_OBJECTS,Jo.BASE,this._process_trigger_update_objects.bind(this)),new Zo(hO.INPUT_TRIGGER_VEL_RESET,Jo.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(hO.OUTPUT_HIT,Jo.BASE),new Zo(hO.OUTPUT_MISS,Jo.BASE)])}trigger_hit(t){this.dispatchEventToOutput(hO.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(hO.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=li.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),tO-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.velocity_controller.reset()}}var uO;hO.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",hO.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",hO.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",hO.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",hO.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",hO.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(uO||(uO={}));const dO=[uO.SET,uO.TOGGLE];const pO=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=aa.BOOLEAN(0),this.displayMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tdisplay:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.display=aa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:dO.indexOf(uO.SET)},separatorAfter:!0}),this.tbypass=aa.BOOLEAN(0),this.bypassMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tbypass:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.bypass=aa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:dO.indexOf(uO.SET)}}),this.execute=aa.BUTTON(null,{callback:t=>{_O.PARAM_CALLBACK_execute(t)}})}};class _O extends ka{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=dO[this.pv.displayMode];switch(i){case uO.SET:return void n.set(this.pv.display);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=dO[this.pv.bypassMode];switch(i){case uO.SET:return void n.set(this.pv.bypass);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var mO;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(mO||(mO={}));const fO=[mO.BOOLEAN,mO.BUTTON,mO.NUMBER,mO.VECTOR2,mO.VECTOR3,mO.VECTOR4,mO.STRING],gO=fO.indexOf(mO.BOOLEAN),vO=fO.indexOf(mO.NUMBER),yO=fO.indexOf(mO.VECTOR2),xO=fO.indexOf(mO.VECTOR3),bO=fO.indexOf(mO.VECTOR4),wO=fO.indexOf(mO.STRING),TO=\\\\\\\"output\\\\\\\";const AO=new class extends la{constructor(){super(...arguments),this.param=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=aa.INTEGER(vO,{menu:{entries:fO.map(((t,e)=>({name:t,value:e})))}}),this.toggle=aa.BOOLEAN(0,{visibleIf:{type:gO}}),this.boolean=aa.BOOLEAN(0,{visibleIf:{type:gO,toggle:0}}),this.number=aa.FLOAT(0,{visibleIf:{type:vO}}),this.vector2=aa.VECTOR2([0,0],{visibleIf:{type:yO}}),this.vector3=aa.VECTOR3([0,0,0],{visibleIf:{type:xO}}),this.vector4=aa.VECTOR4([0,0,0,0],{visibleIf:{type:bO}}),this.increment=aa.BOOLEAN(0,{visibleIf:[{type:vO},{type:yO},{type:xO},{type:bO}]}),this.string=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:wO}}),this.execute=aa.BUTTON(null,{callback:t=>{MO.PARAM_CALLBACK_execute(t)}})}};class MO extends ka{constructor(){super(...arguments),this.paramsConfig=AO,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(TO,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(TO,t)}async _new_param_value(t){const e=fO[this.pv.type];switch(e){case mO.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case mO.BUTTON:return t.options.executeCallback();case mO.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Er.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case mO.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Er.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case mO.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Er.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case mO.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Er.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case mO.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ls.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const EO=new class extends la{constructor(){super(...arguments),this.outputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class SO extends ka{constructor(){super(...arguments),this.paramsConfig=EO}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[Jo.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill(Jo.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n{this._timer_active&&(this.pv.count<=0||this._current_count{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class RO extends sa{static context(){return ts.EVENT}cook(){this.cookController.endCook()}}class IO extends RO{}class FO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class DO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class BO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class zO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class kO extends RO{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class UO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const GO=\\\\\\\"int\\\\\\\";const VO=new class extends la{constructor(){super(...arguments),this.float=aa.FLOAT(0)}};class HO extends df{constructor(){super(...arguments),this.paramsConfig=VO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(GO)} = int(${hf.float(e)})`;t.addBodyLines(this,[n])}}const jO=\\\\\\\"float\\\\\\\";const WO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class qO extends df{constructor(){super(...arguments),this.paramsConfig=WO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(jO,Bo.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(jO)} = float(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const XO=\\\\\\\"bool\\\\\\\";const YO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class $O extends df{constructor(){super(...arguments),this.paramsConfig=YO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(XO,Bo.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(XO)} = bool(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const JO=new class extends la{constructor(){super(...arguments),this.bool=aa.BOOLEAN(0)}};class ZO extends df{constructor(){super(...arguments),this.paramsConfig=JO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(GO)} = int(${hf.bool(e)})`;t.addBodyLines(this,[n])}}const QO=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0)}};class KO extends df{constructor(){super(...arguments),this.paramsConfig=QO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(KO.OUTPUT_NAME,Bo.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(KO.OUTPUT_NAME)} = ${hf.float2(e,n)}`;t.addBodyLines(this,[i])}}KO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const tP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0)}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(eP.OUTPUT_NAME,Bo.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=`vec3 ${this.glVarName(eP.OUTPUT_NAME)} = ${hf.float3(e,n,i)}`;t.addBodyLines(this,[s])}}eP.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const nP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0),this.w=aa.FLOAT(0)}};class iP extends df{constructor(){super(...arguments),this.paramsConfig=nP}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(iP.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=this.variableForInputParam(this.p.w),r=`vec4 ${this.glVarName(iP.OUTPUT_NAME)} = ${hf.float4(e,n,i,s)}`;t.addBodyLines(this,[r])}}iP.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const sP=new class extends la{};class rP extends df{constructor(){super(...arguments),this.paramsConfig=sP}}function oP(t,e){const n=e.components,i=e.param_type;return class extends rP{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Ho(t,Bo.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const aP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],lP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],cP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class hP extends(oP(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Er.VECTOR2})){}class uP extends(oP(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Er.VECTOR3})){}class dP extends(oP(\\\\\\\"vec4ToFloat\\\\\\\",{components:cP,param_type:Er.VECTOR4})){}class pP extends rP{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(pP.OUTPUT_NAME_VEC3,Bo.VEC3),new Ho(pP.OUTPUT_NAME_W,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR4,pP.INPUT_NAME_VEC4,cP.map((t=>0)))}setLines(t){const e=[],n=pP.INPUT_NAME_VEC4,i=pP.OUTPUT_NAME_VEC3,s=pP.OUTPUT_NAME_W,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${r}.xyz`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.w`)}t.addBodyLines(this,e)}}pP.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",pP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",pP.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class _P extends rP{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(_P.OUTPUT_NAME_VEC2,Bo.VEC2),new Ho(_P.OUTPUT_NAME_Z,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR3,_P.INPUT_NAME_VEC3,lP.map((t=>0)))}setLines(t){const e=[],n=_P.INPUT_NAME_VEC3,i=_P.OUTPUT_NAME_VEC2,s=_P.OUTPUT_NAME_Z,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${r}.xy`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.z`)}t.addBodyLines(this,e)}}_P.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",_P.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",_P.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class mP extends rP{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(mP.OUTPUT_NAME_VEC3,Bo.VEC3)])}createParams(){this.addParam(Er.VECTOR2,mP.INPUT_NAME_VEC2,aP.map((t=>0))),this.addParam(Er.FLOAT,mP.INPUT_NAME_Z,0)}setLines(t){const e=[],n=mP.INPUT_NAME_VEC2,i=mP.INPUT_NAME_Z,s=mP.OUTPUT_NAME_VEC3,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec3 ${a} = vec3(${r}.xy, ${o})`),t.addBodyLines(this,e)}}mP.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",mP.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",mP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class fP extends rP{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(fP.OUTPUT_NAME_VEC4,Bo.VEC4)])}createParams(){this.addParam(Er.VECTOR3,fP.INPUT_NAME_VEC3,lP.map((t=>0))),this.addParam(Er.FLOAT,fP.INPUT_NAME_W,0)}setLines(t){const e=[],n=fP.INPUT_NAME_VEC3,i=fP.INPUT_NAME_W,s=fP.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),t.addBodyLines(this,e)}}fP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",fP.INPUT_NAME_W=\\\\\\\"w\\\\\\\",fP.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const gP=new class extends la{};class vP extends df{constructor(){super(...arguments),this.paramsConfig=gP}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class yP extends vP{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Bo.FLOAT]}}class xP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}}class bP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t]}}class wP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t]}}class TP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t,t]}}function AP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||\\\\\\\"in\\\\\\\";return class extends yP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return s}_gl_output_name(t){return i}gl_method_name(){return n}}}class MP extends(AP(\\\\\\\"abs\\\\\\\")){}class EP extends(AP(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class SP extends(AP(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class CP extends(AP(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class NP extends(AP(\\\\\\\"ceil\\\\\\\")){}class LP extends(AP(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class OP extends(AP(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class PP extends(AP(\\\\\\\"exp\\\\\\\")){}class RP extends(AP(\\\\\\\"exp2\\\\\\\")){}class IP extends(AP(\\\\\\\"floor\\\\\\\")){}class FP extends(AP(\\\\\\\"fract\\\\\\\")){}class DP extends(AP(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class BP extends(AP(\\\\\\\"log\\\\\\\")){}class zP extends(AP(\\\\\\\"log2\\\\\\\")){}class kP extends(AP(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class UP extends(AP(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class GP extends(AP(\\\\\\\"sign\\\\\\\")){}class VP extends(AP(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class HP extends(AP(\\\\\\\"sqrt\\\\\\\")){}class jP extends(AP(\\\\\\\"tan\\\\\\\")){}function WP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],r=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends xP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Tf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():r}const e=t||r||Bo.FLOAT;return[e,e]}}}class qP extends(WP(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class XP extends(WP(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class YP extends(WP(\\\\\\\"max\\\\\\\")){}class $P extends(WP(\\\\\\\"min\\\\\\\")){}class JP extends(WP(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Bo.FLOAT;return[t,t]}}class ZP extends(WP(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class QP extends(WP(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Bo.VEC3})){}class KP extends(WP(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function tR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=e.default||{},o=e.out_type||Bo.FLOAT,a=e.functions||[];return class extends bP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return r[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class eR extends(tR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class nR extends(tR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class iR extends(tR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function sR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.operation,r=e.allowed_in_types;return class extends xP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return hf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return s}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&r&&!r.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Bo.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,s=[];for(let t=0;t{const n=t.name();return hf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function hR(t,e){return class extends cR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class uR extends(hR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class dR extends(hR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var pR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(pR||(pR={}));var _R,mR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(_R||(_R={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(mR||(mR={}));const fR=[_R.POSITION,_R.VELOCITY,_R.MASS,_R.FORCE],gR=[mR.POSITION,mR.VELOCITY],vR={[_R.POSITION]:[0,0,0],[_R.VELOCITY]:[0,0,0],[_R.MASS]:1,[_R.FORCE]:[0,-9.8,0]};const yR=new class extends la{};class xR extends df{constructor(){super(...arguments),this.paramsConfig=yR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(mR.POSITION,Bo.VEC3),new Ho(mR.VELOCITY,Bo.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.VEC3;return[t,t,Bo.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return fR[t]}_gl_output_name(t){return gR[t]}paramDefaultValue(t){return vR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Bo.FLOAT,pR.DELTA_TIME),i=new Tf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const s=hf.any(this.variableForInput(_R.POSITION)),r=hf.any(this.variableForInput(_R.VELOCITY)),o=hf.float(this.variableForInput(_R.MASS)),a=hf.any(this.variableForInput(_R.FORCE)),l=this.glVarName(mR.POSITION),c=this.glVarName(mR.VELOCITY),h=`${e} ${c} = compute_velocity_from_acceleration(${[r,a,o,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,u=`${e} ${l} = compute_position_from_velocity(${[s,c,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[h,u])}}var bR,wR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n// let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n// let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n// out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n// out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n// out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n// out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n// return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s, oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s, oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s, 0.0, oc * axis.z * axis.x - axis.y * s, oc * axis.y * axis.z + axis.x * s, oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(bR||(bR={}));const TR=[bR.DIR,bR.UP],AR={[bR.DIR]:[0,0,1],[bR.UP]:[0,1,0]};class MR extends xP{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>TR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return AR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var ER;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(ER||(ER={}));const SR=[ER.LINEAR,ER.GAMMA,ER.SRGB,ER.RGBE,ER.RGBM,ER.RGBD,ER.LogLuv];const CR=new class extends la{constructor(){super(...arguments),this.color=aa.VECTOR4([1,1,1,1]),this.from=aa.INTEGER(SR.indexOf(ER.LINEAR),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.to=aa.INTEGER(SR.indexOf(ER.GAMMA),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=aa.FLOAT(2.2)}};class NR extends df{constructor(){super(...arguments),this.paramsConfig=CR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(NR.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=SR[this.pv.from],n=SR[this.pv.to],i=this.glVarName(NR.OUTPUT_NAME),s=hf.any(this.variableForInput(NR.INPUT_NAME)),r=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(s),e==ER.GAMMA||n==ER.GAMMA){const t=hf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}r.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);t.addBodyLines(this,r)}}var LR,OR;NR.INPUT_NAME=\\\\\\\"color\\\\\\\",NR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",NR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(LR||(LR={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(OR||(OR={}));const PR=[LR.EQUAL,LR.LESS_THAN,LR.GREATER_THAN,LR.LESS_THAN_OR_EQUAL,LR.GREATER_THAN_OR_EQUAL,LR.NOT_EQUAL],RR=[OR.EQUAL,OR.LESS_THAN,OR.GREATER_THAN,OR.LESS_THAN_OR_EQUAL,OR.GREATER_THAN_OR_EQUAL,OR.NOT_EQUAL],IR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const FR=new class extends la{constructor(){super(...arguments),this.test=aa.INTEGER(0,{menu:{entries:PR.map(((t,e)=>({name:`${RR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class DR extends df{constructor(){super(...arguments),this.paramsConfig=FR}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Bo.BOOL]))}set_test_name(t){this.p.test.set(PR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=RR[this.pv.test],s=hf.any(this.variableForInput(this._gl_input_name(0))),r=hf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Vo[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=aa.BOOLEAN(0,zR(Bo.BOOL)),this.int=aa.INTEGER(0,zR(Bo.INT)),this.float=aa.FLOAT(0,zR(Bo.FLOAT)),this.vec2=aa.VECTOR2([0,0],zR(Bo.VEC2)),this.vec3=aa.VECTOR3([0,0,0],zR(Bo.VEC3)),this.vec4=aa.VECTOR4([0,0,0,0],zR(Bo.VEC4))}};class UR extends df{constructor(){super(...arguments),this.paramsConfig=kR,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>UR.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=hf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=hf.integer(e.value));const s=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[s])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=zo[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Bo.BOOL,this.p.bool],[Bo.INT,this.p.int],[Bo.FLOAT,this.p.float],[Bo.VEC2,this.p.vec2],[Bo.VEC3,this.p.vec3],[Bo.VEC4,this.p.vec4]]);const t=zo[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(UR.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(zo.indexOf(t))}}UR.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const GR=\\\\\\\"cross\\\\\\\";const VR=new class extends la{constructor(){super(...arguments),this.x=aa.VECTOR3([0,0,1]),this.y=aa.VECTOR3([0,1,0])}};class HR extends df{constructor(){super(...arguments),this.paramsConfig=VR}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(GR,Bo.VEC3)])}setLines(t){const e=hf.float(this.variableForInputParam(this.p.x)),n=hf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(GR)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class jR extends(tR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var WR=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const qR=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR2([0,0]),this.center=aa.VECTOR2([0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class XR extends df{constructor(){super(...arguments),this.paramsConfig=qR}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}var YR=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n const float a = 4.0 / 11.0;\\\\n const float b = 8.0 / 11.0;\\\\n const float c = 9.0 / 10.0;\\\\n\\\\n const float ca = 4356.0 / 361.0;\\\\n const float cb = 35442.0 / 1805.0;\\\\n const float cc = 16061.0 / 1805.0;\\\\n\\\\n float t2 = t * t;\\\\n\\\\n return t < a\\\\n ? 7.5625 * t2\\\\n : t < b\\\\n ? 9.075 * t2 - 9.9 * t + 3.4\\\\n : t < c\\\\n ? ca * t2 - cb * t + cc\\\\n : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const $R=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],JR={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n return t == 0.0 || t == 1.0\\\\n ? t\\\\n : t < 0.5\\\\n ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n float f = t < 0.5\\\\n ? 2.0 * t\\\\n : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n return t < 0.5\\\\n ? 0.5 * g\\\\n : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n float f = 1.0 - t;\\\\n return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n return t < 0.5\\\\n ? +8.0 * pow(t, 4.0)\\\\n : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n return t < 0.5\\\\n ? 4.0 * t * t * t\\\\n : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":YR,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n float p = 2.0 * t * t;\\\\n return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n return t < 0.5\\\\n ? +16.0 * pow(t, 5.0)\\\\n : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n float f = t - 1.0;\\\\n return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},ZR={\\\\\\\"bounce-in\\\\\\\":[YR],\\\\\\\"bounce-in-out\\\\\\\":[YR]},QR={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},KR=$R.indexOf(\\\\\\\"sine-in-out\\\\\\\");const tI=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(KR,{menu:{entries:$R.map(((t,e)=>({name:t,value:e})))}}),this.input=aa.FLOAT(0)}};class eI extends df{constructor(){super(...arguments),this.paramsConfig=tI}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.FLOAT)])}setLines(t){const e=$R[this.pv.type],n=QR[e];let i=[new Tf(this,JR[e])];const s=(ZR[e]||[]).map((t=>new Tf(this,t)));s&&(i=s.concat(i));const r=hf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var nI=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const iI={srcMin:0,srcMax:1,destMin:0,destMax:1};class sI extends TP{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return iI[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const rI={srcMin:0,srcMax:1};class oI extends bP{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return rI[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const aI={destMin:0,destMax:1};class lI extends bP{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return aI[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const cI={center:.5,variance:.5};class hI extends bP{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return cI[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const uI=\\\\\\\"color\\\\\\\";const dI=new class extends la{constructor(){super(...arguments),this.mvPosition=aa.VECTOR4([0,0,0,0]),this.baseColor=aa.COLOR([0,0,0]),this.fogColor=aa.COLOR([1,1,1]),this.near=aa.FLOAT(0),this.far=aa.FLOAT(0)}};class pI extends df{constructor(){super(...arguments),this.paramsConfig=dI}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(uI,Bo.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Mf(this,Bo.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const s=new Tf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),r=hf.vector4(this.variableForInputParam(this.p.mvPosition)),o=hf.vector3(this.variableForInputParam(this.p.baseColor)),a=hf.vector3(this.variableForInputParam(this.p.fogColor)),l=hf.vector3(this.variableForInputParam(this.p.near)),c=hf.vector3(this.variableForInputParam(this.p.far)),h=`vec3 ${this.glVarName(uI)} = compute_fog(${[r,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,s]),t.addBodyLines(this,[h])}}}const _I=new class extends la{};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return ns.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),s=hf.any(this.variableForInput(i.name())),r=`\\\\t${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class fI extends df{constructor(){super(...arguments),this._children_controller_context=ts.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(mI.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const e=t.dest_connection_point(),i=hf.any(this.variableForInput(e.name())),s=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,l=hf.float(r),c=hf.float(o),h=hf.float(a),u=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${u} = ${l}; ${u} < ${c}; ${u}+= ${h}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(yI.START_INDEX)} = ${u}`;if(n.push(p),s)for(let t of s)if(t&&t.input_index>=0){const i=t.dest_connection_point(),s=this.glVarName(i.name()),r=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(e,n)}setLines(t){}}const TI=new class extends la{};class AI extends df{constructor(){super(...arguments),this.paramsConfig=TI}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const MI=new class extends la{constructor(){super(...arguments),this.hsluv=aa.VECTOR3([1,1,1])}};class EI extends df{constructor(){super(...arguments),this.paramsConfig=MI}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214 ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786 );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4 xyzToRgb(vec4 c) {return vec4( xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToXyz(vec4 c) {return vec4( rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 xyzToLuv(vec4 c) {return vec4( xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToXyz(vec4 c) {return vec4( luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToLch(vec4 c) {return vec4( luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToLuv(vec4 c) {return vec4( lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToRgb(vec4 c) {return vec4( lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToLch(vec4 c) {return vec4( rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToRgb(vec4 c) {return vec4( luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3 xyzToRgb(float x, float y, float z) {return xyzToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToXyz(float x, float y, float z) {return rgbToXyz( vec3(x,y,z) );}\\\\nvec3 xyzToLuv(float x, float y, float z) {return xyzToLuv( vec3(x,y,z) );}\\\\nvec3 luvToXyz(float x, float y, float z) {return luvToXyz( vec3(x,y,z) );}\\\\nvec3 luvToLch(float x, float y, float z) {return luvToLch( vec3(x,y,z) );}\\\\nvec3 lchToLuv(float x, float y, float z) {return lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3 lchToRgb(float x, float y, float z) {return lchToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToLch(float x, float y, float z) {return rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3 luvToRgb(float x, float y, float z) {return luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4 xyzToRgb(float x, float y, float z, float a) {return xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToXyz(float x, float y, float z, float a) {return rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4 xyzToLuv(float x, float y, float z, float a) {return xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4 luvToXyz(float x, float y, float z, float a) {return luvToXyz( vec4(x,y,z,a) );}\\\\nvec4 luvToLch(float x, float y, float z, float a) {return luvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToLuv(float x, float y, float z, float a) {return lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4 lchToRgb(float x, float y, float z, float a) {return lchToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToLch(float x, float y, float z, float a) {return rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4 luvToRgb(float x, float y, float z, float a) {return luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.hsluv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const SI=new class extends la{constructor(){super(...arguments),this.hsv=aa.VECTOR3([1,1,1])}};class CI extends df{constructor(){super(...arguments),this.paramsConfig=SI}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.hsv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const NI=\\\\\\\"condition\\\\\\\";const LI=new class extends la{};class OI extends vI{constructor(){super(...arguments),this.paramsConfig=LI}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Bo.BOOL],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=1;s[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const YI=\\\\\\\"mvMult\\\\\\\";const $I=new class extends la{constructor(){super(...arguments),this.vector=aa.VECTOR3([0,0,0])}};class JI extends df{constructor(){super(...arguments),this.paramsConfig=$I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(YI,Bo.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=hf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(YI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const ZI={mult:1};var QI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(QI||(QI={}));class KI extends wP{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[QI.VALUE,QI.PRE_ADD,QI.MULT,QI.POST_ADD][t]}paramDefaultValue(t){return ZI[t]}setLines(t){const e=hf.any(this.variableForInput(QI.VALUE)),n=hf.any(this.variableForInput(QI.PRE_ADD)),i=hf.any(this.variableForInput(QI.MULT)),s=hf.any(this.variableForInput(QI.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${s}`;t.addBodyLines(this,[a])}}class tF extends yP{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var eF;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(eF||(eF={}));const nF=[eF.CLASSIC_PERLIN_2D,eF.CLASSIC_PERLIN_3D,eF.CLASSIC_PERLIN_4D,eF.NOISE_2D,eF.NOISE_3D,eF.NOISE_4D],iF={[eF.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n',[eF.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n vec3 Pi0 = floor(P); // Integer part for indexing\\\\n vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n',[eF.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n vec4 Pi0 = floor(P); // Integer part for indexing\\\\n vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n',[eF.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n {\\\\n const vec4 C = vec4(0.211324865405187, // (3.0-sqrt(3.0))/6.0\\\\n 0.366025403784439, // 0.5*(sqrt(3.0)-1.0)\\\\n -0.577350269189626, // -1.0 + 2.0 * C.x\\\\n 0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n vec2 i = floor(v + dot(v, C.yy) );\\\\n vec2 x0 = v - i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n vec2 i1;\\\\n //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n //i1.y = 1.0 - i1.x;\\\\n i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n vec4 x12 = x0.xyxy + C.xxzz;\\\\n x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n i = mod289(i); // Avoid truncation effects in permutation\\\\n vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n m = m*m ;\\\\n m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n vec3 h = abs(x) - 0.5;\\\\n vec3 ox = floor(x + 0.5);\\\\n vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n vec3 g;\\\\n g.x = a0.x * x0.x + h.x * x0.y;\\\\n g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[eF.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n { \\\\n const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n vec3 i = floor(v + dot(v, C.yyy) );\\\\n vec3 x0 = v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n vec3 g = step(x0.yzx, x0.xyz);\\\\n vec3 l = 1.0 - g;\\\\n vec3 i1 = min( g.xyz, l.zxy );\\\\n vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n // x1 = x0 - i1 + 1.0 * C.xxx;\\\\n // x2 = x0 - i2 + 2.0 * C.xxx;\\\\n // x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n vec3 x1 = x0 - i1 + C.xxx;\\\\n vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n vec3 x3 = x0 - D.yyy; // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n vec4 p = permute( permute( permute( \\\\n i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n float n_ = 0.142857142857; // 1.0/7.0\\\\n vec3 ns = n_ * D.wyz - D.xzx;\\\\n\\\\n vec4 j = p - 49.0 * floor(p * ns.z * ns.z); // mod(p,7*7)\\\\n\\\\n vec4 x_ = floor(j * ns.z);\\\\n vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\\\\n\\\\n vec4 x = x_ *ns.x + ns.yyyy;\\\\n vec4 y = y_ *ns.x + ns.yyyy;\\\\n vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n vec4 b0 = vec4( x.xy, y.xy );\\\\n vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n vec3 p0 = vec3(a0.xy,h.x);\\\\n vec3 p1 = vec3(a0.zw,h.y);\\\\n vec3 p2 = vec3(a1.xy,h.z);\\\\n vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n m = m * m;\\\\n return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n dot(p2,x2), dot(p3,x3) ) );\\\\n }\\\\n\\\\\\\",[eF.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n {\\\\n const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n vec4 p,s;\\\\n\\\\n p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n s = vec4(lessThan(p, vec4(0.0)));\\\\n p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n return p;\\\\n }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n {\\\\n const vec4 C = vec4( 0.138196601125011, // (5 - sqrt(5))/20 G4\\\\n 0.276393202250021, // 2 * G4\\\\n 0.414589803375032, // 3 * G4\\\\n -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n vec4 i = floor(v + dot(v, vec4(F4)) );\\\\n vec4 x0 = v - i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n vec4 i0;\\\\n vec3 isX = step( x0.yzw, x0.xxx );\\\\n vec3 isYZ = step( x0.zww, x0.yyz );\\\\n// i0.x = dot( isX, vec3( 1.0 ) );\\\\n i0.x = isX.x + isX.y + isX.z;\\\\n i0.yzw = 1.0 - isX;\\\\n// i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n i0.y += isYZ.x + isYZ.y;\\\\n i0.zw += 1.0 - isYZ.xy;\\\\n i0.z += isYZ.z;\\\\n i0.w += 1.0 - isYZ.z;\\\\n\\\\n // i0 now contains the unique values 0,1,2,3 in each channel\\\\n vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n // x1 = x0 - i1 + 1.0 * C.xxxx\\\\n // x2 = x0 - i2 + 2.0 * C.xxxx\\\\n // x3 = x0 - i3 + 3.0 * C.xxxx\\\\n // x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n vec4 x1 = x0 - i1 + C.xxxx;\\\\n vec4 x2 = x0 - i2 + C.yyyy;\\\\n vec4 x3 = x0 - i3 + C.zzzz;\\\\n vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n vec4 j1 = permute( permute( permute( permute (\\\\n i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n vec4 p0 = grad4(j0, ip);\\\\n vec4 p1 = grad4(j1.x, ip);\\\\n vec4 p2 = grad4(j1.y, ip);\\\\n vec4 p3 = grad4(j1.z, ip);\\\\n vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4) ), 0.0);\\\\n m0 = m0 * m0;\\\\n m1 = m1 * m1;\\\\n return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n }\\\\n\\\\\\\"},sF={[eF.CLASSIC_PERLIN_2D]:Bo.VEC2,[eF.CLASSIC_PERLIN_3D]:Bo.VEC3,[eF.CLASSIC_PERLIN_4D]:Bo.VEC4,[eF.NOISE_2D]:Bo.VEC2,[eF.NOISE_3D]:Bo.VEC3,[eF.NOISE_4D]:Bo.VEC4},rF={[eF.CLASSIC_PERLIN_2D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_3D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_4D]:Bo.FLOAT,[eF.NOISE_2D]:Bo.FLOAT,[eF.NOISE_3D]:Bo.FLOAT,[eF.NOISE_4D]:Bo.FLOAT},oF={[eF.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[eF.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var aF;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(aF||(aF={}));const lF=[aF.NoChange,aF.Float,aF.Vec2,aF.Vec3,aF.Vec4],cF={[aF.NoChange]:\\\\\\\"Same as noise\\\\\\\",[aF.Float]:\\\\\\\"Float\\\\\\\",[aF.Vec2]:\\\\\\\"Vec2\\\\\\\",[aF.Vec3]:\\\\\\\"Vec3\\\\\\\",[aF.Vec4]:\\\\\\\"Vec4\\\\\\\"},hF={[aF.NoChange]:Bo.FLOAT,[aF.Float]:Bo.FLOAT,[aF.Vec2]:Bo.VEC2,[aF.Vec3]:Bo.VEC3,[aF.Vec4]:Bo.VEC4},uF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],dF=\\\\\\\"noise\\\\\\\",pF=nF.indexOf(eF.NOISE_3D),_F=aF.NoChange,mF={amp:1,freq:1};var fF;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(fF||(fF={}));const gF=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(pF,{menu:{entries:nF.map(((t,e)=>({name:`${t} (output: ${rF[t]})`,value:e})))}}),this.outputType=aa.INTEGER(_F,{menu:{entries:lF.map((t=>{const e=lF[t];return{name:cF[e],value:e}}))}}),this.octaves=aa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class vF extends df{constructor(){super(...arguments),this.paramsConfig=gF}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(dF,Bo.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>dF))}_gl_input_name(t){return[fF.AMP,fF.POSITION,fF.FREQ,fF.OFFSET][t]}paramDefaultValue(t){return mF[t]}_expected_input_types(){const t=nF[this.pv.type],e=this._expected_output_types()[0],n=sF[t];return[e,n,n,n]}_expected_output_types(){const t=nF[this.pv.type],e=lF[this.pv.outputType];return e==aF.NoChange?[sF[t]]:[hF[e]]}setLines(t){const e=[],n=[],i=nF[this.pv.type],s=iF[i],r=rF[i];e.push(new Tf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Tf(this,s)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==r){const t=this.single_noise_line();n.push(t)}else{const t=Vo[o],e=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;rhf.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const r=`vec${t} ${s} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(r)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=nF[this.pv.type];return`fbm_${oF[t]}_${this.name()}`}fbm_function(){const t=nF[this.pv.type],e=oF[t],n=sF[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${hf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${hf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${hf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),s=hf.any(this.variableForInput(fF.AMP)),r=hf.any(this.variableForInput(fF.POSITION)),o=hf.any(this.variableForInput(fF.FREQ));let a=hf.any(this.variableForInput(fF.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(dF),h=`${s}*${i}(${l})`;if(e)return`float ${c}${t} = (${h}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${h}`}}class yF extends yP{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const xF=new class extends la{};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class wF{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const TF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(zo.indexOf(Bo.FLOAT),{menu:{entries:zo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:zo.indexOf(Bo.VEC3)}})}};class AF extends df{constructor(){super(...arguments),this.paramsConfig=TF,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[zo[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=zo[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=zo[this.pv.type],e=Go[t];let n=ko[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=zo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class MF extends vP{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.VEC3;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const EF=\\\\\\\"SSSModel\\\\\\\";const SF=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.thickness=aa.FLOAT(.1),this.power=aa.FLOAT(2),this.scale=aa.FLOAT(16),this.distortion=aa.FLOAT(.1),this.ambient=aa.FLOAT(.4),this.attenuation=aa.FLOAT(.8)}};class CF extends df{constructor(){super(...arguments),this.paramsConfig=SF}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(EF,Bo.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(EF);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${hf.vector3(this.variableForInputParam(e))};`}}class NF extends yP{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4,Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var LF;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}(LF||(LF={}));const OF=[LF.AXIS,LF.ANGLE],PF={[LF.AXIS]:[0,0,1],[LF.ANGLE]:0};class RF extends xP{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>OF[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return PF[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class IF extends yP{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class FF extends yP{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}const DF=\\\\\\\"val\\\\\\\";const BF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=aa.FLOAT(0)}};class zF extends df{constructor(){super(...arguments),this.paramsConfig=BF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Ho(DF,Bo.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Bo.FLOAT,n=this._uniform_name(),i=this.glVarName(DF),s=new Af(this,Bo.SAMPLER_2D,n);t.addDefinitions(this,[s]);const r=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${r}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.RAMP,this.pv.name,bo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(DF)}}const kF=\\\\\\\"rand\\\\\\\";const UF=new class extends la{constructor(){super(...arguments),this.seed=aa.VECTOR2([1,1])}};class GF extends df{constructor(){super(...arguments),this.paramsConfig=UF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(kF,Bo.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=hf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(kF)} = rand(${n})`;t.addBodyLines(this,[i])}}const VF=new class extends la{constructor(){super(...arguments),this.rgb=aa.VECTOR3([1,1,1])}};class HF extends df{constructor(){super(...arguments),this.paramsConfig=VF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"hsv\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.rgb)),s=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${s} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var jF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(jF||(jF={}));const WF=[jF.AXIS,jF.QUAT],qF={[jF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[jF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},XF={[jF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[jF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},YF={[jF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[jF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},$F={[jF.AXIS]:[Bo.VEC3,Bo.VEC3,Bo.FLOAT],[jF.QUAT]:[Bo.VEC3,Bo.VEC4]},JF={vector:[0,0,1],axis:[0,1,0]};const ZF=new class extends la{constructor(){super(...arguments),this.signature=aa.INTEGER(jF.AXIS,{menu:{entries:WF.map(((t,e)=>({name:qF[t],value:e})))}})}};class QF extends df{constructor(){super(...arguments),this.paramsConfig=ZF}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=WF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=WF[this.pv.signature];return XF[e][t]}paramDefaultValue(t){return JF[t]}gl_method_name(){const t=WF[this.pv.signature];return YF[t]}_expected_input_types(){const t=WF[this.pv.signature];return $F[t]}_expected_output_types(){return[Bo.VEC3]}gl_function_definitions(){return[new Tf(this,wR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const KF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class tD extends yP{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=hf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==Vo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const t=KF.map((t=>this._simple_line(`${n}.${t}`)));r.push(`${i.type()} ${s} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,r)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const eD=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR3([0,0,0]),this.center=aa.VECTOR3([0,0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class nD extends df{constructor(){super(...arguments),this.paramsConfig=eD}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}const iD=new class extends la{};class sD extends df{constructor(){super(...arguments),this.paramsConfig=iD}static type(){return ns.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const rD=new class extends la{};class oD extends df{constructor(){super(...arguments),this.paramsConfig=rD}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?oD.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Bo.FLOAT,e=this.io.connections.inputConnections(),n=e?rr.clamp(e.length,2,16):2,i=[Bo.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(lD.OUTPUT_NAME),i=this._uniform_name(),s=new Af(this,Bo.SAMPLER_2D,i),r=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[s]),t.addBodyLines(this,[r])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var cD;lD.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(cD||(cD={}));const hD=[cD.POSITION,cD.DIR_VEC];const uD=new class extends la{constructor(){super(...arguments),this.vec=aa.VECTOR3([0,0,0]),this.interpretation=aa.INTEGER(0,{menu:{entries:hD.map(((t,e)=>({name:t,value:e})))}})}};class dD extends df{constructor(){super(...arguments),this.paramsConfig=uD}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=hf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(hD[this.pv.interpretation]){case cD.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case cD.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var pD;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(pD||(pD={}));const _D=[pD.CONDITION,pD.IF_TRUE,pD.IF_FALSE];class mD extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return _D[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Bo.FLOAT;return[Bo.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=hf.bool(this.variableForInput(pD.CONDITION)),s=hf.any(this.variableForInput(pD.IF_TRUE)),r=hf.any(this.variableForInput(pD.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${s}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${r}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const fD=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const gD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:fD.map(((t,e)=>({name:t,value:e})))}})}};class vD extends df{constructor(){super(...arguments),this.paramsConfig=gD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[fD[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return vD.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Mf(this,this.gl_type(),e),i=this.glVarName(vD.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(fD.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}vD.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const yD={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class xD extends(tR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t,t]}_expected_output_types(){return[Bo.VEC4]}paramDefaultValue(t){return yD[t]}}const bD={start:[0,0,1],end:[1,0,0]};class wD extends(WP(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t]}_expected_output_types(){return[Bo.FLOAT]}paramDefaultValue(t){return bD[t]}}const TD={only:[`${OI.context()}/${OI.type()}`,`${vI.context()}/${vI.type()}`,`${wI.context()}/${wI.type()}`]};class AD extends sa{static context(){return ts.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends la{};const MD=[jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4];const ED=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:MD.map(((t,e)=>({name:t,value:e})))}})}};class SD extends AD{constructor(){super(...arguments),this.paramsConfig=ED,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[MD[this.pv.type]]))}get input_name(){return SD.INPUT_NAME}get output_name(){return SD.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(MD.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(SD.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(SD.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}SD.INPUT_NAME=\\\\\\\"export\\\\\\\",SD.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const CD=new class extends la{};class ND extends AD{constructor(){super(...arguments),this.paramsConfig=CD}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const LD=new class extends la{};class OD extends AD{constructor(){super(...arguments),this.paramsConfig=LD}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class PD{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var RD;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(RD||(RD={}));class ID{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new PD}}class FD extends ID{constructor(t,e,n){super(RD.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class DD extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Er.BOOLEAN:case Er.BUTTON:return{value:0};case Er.COLOR:return{value:new D.a(0,0,0)};case Er.FLOAT:case Er.FOLDER:case Er.INTEGER:case Er.OPERATOR_PATH:case Er.NODE_PATH:case Er.PARAM_PATH:return{value:0};case Er.RAMP:case Er.STRING:return{value:null};case Er.VECTOR2:return{value:new d.a(0,0)};case Er.VECTOR3:return{value:new p.a(0,0,0)};case Er.VECTOR4:return{value:new _.a(0,0,0,0)}}ls.unreachable(t)}}const BD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(Wo.indexOf(jo.FLOAT),{menu:{entries:Wo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:Wo.indexOf(jo.VEC3)}})}};class zD extends AD{constructor(){super(...arguments),this.paramsConfig=BD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[Wo[this.pv.type]]))}setLines(t){const e=[],n=Wo[this.pv.type],i=this.uniform_name();e.push(new FD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=Wo[this.pv.type],e=Yo[t];let n=qo[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new DD(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new DD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=Wo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class kD extends sa{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return ts.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class UD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const GD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},VD=Object.keys(GD);function HD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=aa.BOOLEAN(0),this.front=aa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=aa.BOOLEAN(0),this.shadowDoubleSided=aa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=aa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=aa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthWrite=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthTest=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.premultipliedAlpha=aa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=aa.INTEGER(w.xb,{menu:{entries:VD.map((t=>({name:t,value:GD[t]})))}}),this.dithering=aa.BOOLEAN(0),this.polygonOffset=aa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}HD(la);class jD extends UD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let n of t){const t=s[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class WD extends(HD(la)){constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.lineWidth=aa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const qD=new WD;class XD extends kD{constructor(){super(...arguments),this.paramsConfig=qD,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new Ts.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function YD(t){return class extends t{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}YD(la);class $D extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class JD extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]);const r=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});r||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:r||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),s=this._material.uniforms[n],r=Object.keys(this._material.customMaterials);let o;for(let t of r){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(s||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(s&&$f.callback(i,s),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(li.playerMode())return this._material}}function ZD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=aa.BOOLEAN(0,{callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}})}}}ZD(la);class QD extends kD{constructor(){super(...arguments),this._children_controller_context=ts.GL,this.persisted_config=new JD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(ts.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function KD(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}KD(la);class tB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function eB(t){return class extends t{constructor(){super(...arguments),this.default=aa.FOLDER(null)}}}function nB(t){return class extends t{constructor(){super(...arguments),this.advanced=aa.FOLDER(null)}}}class iB extends(KD(HD(ZD(nB(YD(eB(la))))))){constructor(){super(...arguments),this.linewidth=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const sB=new iB;class rB extends QD{constructor(){super(...arguments),this.paramsConfig=sB,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_LINE}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function oB(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.useVertexColors=aa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}O.a;oB(la);class aB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function lB(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}lB(la);class cB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function hB(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function uB(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:ts.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class dB extends UD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const s=t,r=e;await this._update_texture_on_uniforms(s,r,n,i)}if(this._update_options.directParams){const s=t,r=e;await this._update_texture_on_material(s,r,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const s=null!=e[n]&&null!=e[n].value;let r=!1;if(s){e[n].value.uuid!=i.uuid&&(r=!0)}if(!s||r){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let e of t){const t=s[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else li.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const s=null!=e[n];let r=!1;if(s){e[n].uuid!=i.uuid&&(r=!0)}s&&!r||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,s,r,o){i.isDirty()&&await i.compute();if(i.value){s.isDirty()&&await s.compute();const i=s.value.nodeWithContext(ts.COP);if(i){const s=(await i.compute()).texture();if(s)return void r(t,e,n,s)}}o(t,e,n)}}function pB(t){return class extends t{constructor(){super(...arguments),this.useMap=aa.BOOLEAN(0,hB(_B)),this.map=aa.NODE_PATH(vi.EMPTY,uB(_B,\\\\\\\"useMap\\\\\\\"))}}}O.a;pB(la);class _B extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function mB(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(fB)}),this.alphaMap=aa.NODE_PATH(vi.EMPTY,uB(fB,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;mB(la);class fB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function gB(t){return class extends t{constructor(){super(...arguments),this.useAOMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(vB)}),this.aoMap=aa.NODE_PATH(vi.EMPTY,uB(vB,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;gB(la);class vB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var yB;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(yB||(yB={}));const xB=[yB.MULT,yB.ADD,yB.MIX],bB={[yB.MULT]:w.nb,[yB.ADD]:w.c,[yB.MIX]:w.lb};function wB(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,hB(TB)),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(TB,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=aa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:xB.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}wB(la);class TB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=bB[xB[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function AB(t){return class extends t{constructor(){super(...arguments),this.useLightMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(MB)}),this.lightMap=aa.NODE_PATH(vi.EMPTY,uB(MB,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=aa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;AB(la);class MB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var EB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(EB||(EB={}));const SB=[EB.ROUND,EB.BUTT,EB.SQUARE];var CB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(CB||(CB={}));const NB=[CB.ROUND,CB.BEVEL,CB.MITER];function LB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=aa.INTEGER(0,{menu:{entries:SB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=aa.INTEGER(0,{menu:{entries:NB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;LB(la);class OB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=SB[n.wireframeLinecap],e.wireframeLinejoin=NB[n.wireframeLinejoin],e.needsUpdate=!0}}function PB(t){return class extends t{constructor(){super(...arguments),this.textures=aa.FOLDER(null)}}}const RB={directParams:!0};class IB extends(lB(LB(HD(nB(AB(wB(gB(mB(pB(PB(oB(eB(la))))))))))))){}const FB=new IB;class DB extends kD{constructor(){super(...arguments),this.paramsConfig=FB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,RB),aoMap:new vB(this,RB),envMap:new TB(this,RB),lightMap:new MB(this,RB),map:new _B(this,RB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new lt.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function BB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0)}}}BB(la);class zB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const kB={uniforms:!0};class UB extends(KD(BB(HD(ZD(nB(wB(gB(mB(pB(PB(YD(eB(la))))))))))))){}const GB=new UB;class VB extends QD{constructor(){super(...arguments),this.paramsConfig=GB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,kB),aoMap:new vB(this,kB),envMap:new TB(this,kB),map:new _B(this,kB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_BASIC}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function HB(t){return class extends t{constructor(){super(...arguments),this.emissive=aa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=aa.BOOLEAN(0,hB(jB)),this.emissiveMap=aa.NODE_PATH(vi.EMPTY,uB(jB,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=aa.FLOAT(1)}}}O.a;HB(la);class jB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const WB={directParams:!0};class qB extends(lB(LB(HD(nB(AB(wB(HB(gB(mB(pB(PB(oB(eB(la)))))))))))))){}const XB=new qB;class YB extends kD{constructor(){super(...arguments),this.paramsConfig=XB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,WB),aoMap:new vB(this,WB),emissiveMap:new jB(this,WB),envMap:new TB(this,WB),lightMap:new MB(this,WB),map:new _B(this,WB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new ws.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function $B(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=aa.BOOLEAN(0,{callback:t=>{JB.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=aa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=aa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}$B(la);class JB extends UD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${hf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${hf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=z;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include \\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>JB.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const ZB={uniforms:!0};class QB extends($B(KD(BB(HD(ZD(nB(AB(wB(HB(gB(mB(pB(PB(YD(eB(la)))))))))))))))){}const KB=new QB;class tz extends QD{constructor(){super(...arguments),this.paramsConfig=KB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ZB),aoMap:new vB(this,ZB),emissiveMap:new jB(this,ZB),envMap:new TB(this,ZB),lightMap:new MB(this,ZB),map:new _B(this,ZB),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_LAMBERT}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function ez(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(nz)}),this.bumpMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(nz,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;ez(la);class nz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var iz;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(iz||(iz={}));const sz=[iz.TANGENT,iz.OBJECT],rz={[iz.TANGENT]:w.Uc,[iz.OBJECT]:w.zb};function oz(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(az)}),this.normalMap=aa.NODE_PATH(vi.EMPTY,uB(az,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=aa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:sz.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=aa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;oz(la);class az extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=rz[sz[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function lz(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(cz)}),this.displacementMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;lz(la);class cz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function hz(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=aa.BOOLEAN(0,hB(uz)),this.matcapMap=aa.NODE_PATH(vi.EMPTY,uB(uz,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;hz(la);class uz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const dz={directParams:!0};class pz extends(lB(HD(nB(oz(lz(ez(mB(pB(hz(PB(oB(eB(la))))))))))))){}const _z=new pz;class mz extends kD{constructor(){super(...arguments),this.paramsConfig=_z,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,dz),bumpMap:new nz(this,dz),displacementMap:new cz(this,dz),map:new _B(this,dz),matcap:new uz(this,dz),normalMap:new az(this,dz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),this.setMaterial(this.material)}}const fz={directParams:!0};class gz extends(lB(HD(oz(lz(ez(PB(eB(la)))))))){}const vz=new gz;class yz extends kD{constructor(){super(...arguments),this.paramsConfig=vz,this.controllers={advancedCommon:new jD(this),bumpMap:new nz(this,fz),displacementMap:new cz(this,fz),normalMap:new az(this,fz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshNormal\\\\\\\"}createMaterial(){return new Hf({vertexColors:!1,side:w.H,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();cB.update(this),this.setMaterial(this.material)}}function xz(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=aa.BOOLEAN(0,hB(bz)),this.specularMap=aa.NODE_PATH(vi.EMPTY,uB(bz,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;xz(la);class bz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const wz={directParams:!0};class Tz extends(lB(LB(HD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){constructor(){super(...arguments),this.flatShading=aa.BOOLEAN(0)}}const Az=new Tz;class Mz extends kD{constructor(){super(...arguments),this.paramsConfig=Az,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,wz),aoMap:new vB(this,wz),bumpMap:new nz(this,wz),displacementMap:new cz(this,wz),emissiveMap:new jB(this,wz),envMap:new TB(this,wz),lightMap:new MB(this,wz),map:new _B(this,wz),normalMap:new az(this,wz),specularMap:new bz(this,wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Ez={uniforms:!0};class Sz extends($B(KD(BB(HD(ZD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Cz=new Sz;class Nz extends QD{constructor(){super(...arguments),this.paramsConfig=Cz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Ez),aoMap:new vB(this,Ez),bumpMap:new nz(this,Ez),displacementMap:new cz(this,Ez),emissiveMap:new jB(this,Ez),envMap:new TB(this,Ez),lightMap:new MB(this,Ez),map:new _B(this,Ez),normalMap:new az(this,Ez),specularMap:new bz(this,Ez),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHONG}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Lz(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Oz)}),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(Oz,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Lz(la);class Oz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function Pz(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.metalnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=aa.FLOAT(1),this.useRoughnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.roughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=aa.FLOAT(.5)}}}O.a;Pz(la);class Rz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function Iz(t){return class extends t{constructor(){super(...arguments),this.clearcoat=aa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatNormalMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=aa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=aa.FLOAT(0),this.useClearCoatRoughnessMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatRoughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=aa.BOOLEAN(0),this.sheen=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=aa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=aa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=aa.BOOLEAN(0),this.transmissionMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.ior=aa.FLOAT(1.5,{range:[1,2.3333],rangeLocked:[!0,!0]}),this.thickness=aa.FLOAT(.01,{range:[0,10],rangeLocked:[!0,!1]}),this.useThicknessMap=aa.BOOLEAN(0),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1]}),this.attenuationColor=aa.COLOR([1,1,1])}}}Iz(la);const Fz=new Uf.a;class Dz extends dB{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;Fz.ior=t.ior;const e=Fz.reflectivity;if(this._update_options.uniforms){const n=this.node.material;n.uniforms.clearcoat.value=t.clearcoat,n.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),n.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,n.uniforms.reflectivity.value=e,n.uniforms.transmission.value=t.transmission,n.uniforms.thickness.value=t.thickness,n.uniforms.attenuationDistance.value=t.attenuationDistance,n.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.uniforms.sheen.value=t.sheen,n.uniforms.sheenRoughness.value=t.sheenRoughness,n.uniforms.sheenTint.value=this._sheenColorClone):n.uniforms.sheen.value=0,n.uniforms.ior.value=t.ior,n.specularTint=n.uniforms.specularTint.value,n.ior=n.uniforms.ior.value}if(this._update_options.directParams){const n=this.node.material;n.clearcoat=t.clearcoat,null!=n.clearcoatNormalScale&&n.clearcoatNormalScale.copy(t.clearcoatNormalScale),n.clearcoatRoughness=t.clearcoatRoughness,n.reflectivity=e,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.sheen=t.sheen,n.sheenRoughness=t.sheenRoughness,n.sheenTint=this._sheenColorClone):n.sheen=0,n.transmission=t.transmission,n.thickness=t.thickness,n.attenuationDistance=t.attenuationDistance,n.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Bz={directParams:!0};class zz extends(lB(LB(HD(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))))){}const kz=new zz;class Uz extends kD{constructor(){super(...arguments),this.paramsConfig=kz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Bz),aoMap:new vB(this,Bz),bumpMap:new nz(this,Bz),displacementMap:new cz(this,Bz),emissiveMap:new jB(this,Bz),envMap:new Oz(this,Bz),lightMap:new MB(this,Bz),map:new _B(this,Bz),metalnessRoughnessMap:new Rz(this,Bz),normalMap:new az(this,Bz),physical:new Dz(this,Bz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const Gz={uniforms:!0};class Vz extends(function(t){return class extends($B(KD(BB(HD(ZD(t)))))){}}(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la))))))))))))))))){}const Hz=new Vz;class jz extends QD{constructor(){super(...arguments),this.paramsConfig=Hz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Gz),aoMap:new vB(this,Gz),bumpMap:new nz(this,Gz),displacementMap:new cz(this,Gz),emissiveMap:new jB(this,Gz),envMap:new Oz(this,{uniforms:!0,directParams:!0}),lightMap:new MB(this,Gz),map:new _B(this,Gz),metalnessRoughnessMap:new Rz(this,{uniforms:!0,directParams:!0}),normalMap:new az(this,Gz),physical:new Dz(this,{uniforms:!0,directParams:!0}),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHYSICAL}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}createMaterial(){const t=super.createMaterial();return t.isMeshStandardMaterial=!0,t.isMeshPhysicalMaterial=!0,t}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Wz={directParams:!0};class qz extends(lB(LB(HD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){}const Xz=new qz;class Yz extends kD{constructor(){super(...arguments),this.paramsConfig=Xz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Wz),aoMap:new vB(this,Wz),bumpMap:new nz(this,Wz),displacementMap:new cz(this,Wz),emissiveMap:new jB(this,Wz),envMap:new Oz(this,Wz),lightMap:new MB(this,Wz),map:new _B(this,Wz),metalnessRoughnessMap:new Rz(this,Wz),normalMap:new az(this,Wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new bs.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const $z={uniforms:!0};class Jz extends($B(KD(BB(HD(ZD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Zz=new Jz;class Qz extends QD{constructor(){super(...arguments),this.paramsConfig=Zz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,$z),aoMap:new vB(this,$z),bumpMap:new nz(this,$z),displacementMap:new cz(this,$z),emissiveMap:new jB(this,$z),envMap:new Oz(this,$z),lightMap:new MB(this,$z),map:new _B(this,$z),metalnessRoughnessMap:new Rz(this,$z),normalMap:new az(this,$z),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_STANDARD}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Kz=U.meshphong_frag.slice(0,U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),tk=U.meshphong_frag.slice(U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),ek={uniforms:I.merge([H.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",U.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",Kz,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",tk.replace(\\\\\\\"#include \\\\\\\",(nk=U.lights_fragment_begin,ik=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",sk=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),nk.split(ik).join(sk)))].join(\\\\\\\"\\\\n\\\\\\\")};var nk,ik,sk;function rk(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function ok(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const ak={uniforms:!0};class lk extends(lB(BB(HD(nB(mB(pB(PB(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=aa.COLOR([1,1,1],{...rk(\\\\\\\"diffuse\\\\\\\")}),this.shininess=aa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{nodeSelection:{context:ts.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=aa.COLOR([.5,.3,0],{...rk(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=aa.FLOAT(.1,{...ok(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=aa.FLOAT(.4,{...ok(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=aa.FLOAT(.8,{...ok(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=aa.FLOAT(2,{range:[0,10],...ok(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=aa.FLOAT(16,{range:[0,100],...ok(\\\\\\\"thicknessScale\\\\\\\")})}}}(eB(la)))))))))){}const ck=new lk;class hk extends kD{constructor(){super(...arguments),this.paramsConfig=ck,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ak),map:new _B(this,ak)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(ek.uniforms),e=new F({uniforms:t,vertexShader:ek.vertexShader,fragmentShader:ek.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();cB.update(this),zB.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(ts.COP);n||(this.material.uniforms[e].value=null);const i=n,s=await i.compute();this.material.uniforms[e].value=s.texture()}}function uk(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=aa.BOOLEAN(0,hB(dk)),this.gradientMap=aa.NODE_PATH(vi.EMPTY,uB(dk,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;uk(la);class dk extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const pk={directParams:!0};class _k extends(lB(LB(HD(nB(oz(AB(uk(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))){}const mk=new _k;class fk extends kD{constructor(){super(...arguments),this.paramsConfig=mk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,pk),aoMap:new vB(this,pk),bumpMap:new nz(this,pk),displacementMap:new cz(this,pk),emissiveMap:new jB(this,pk),gradientMap:new dk(this,pk),lightMap:new MB(this,pk),map:new _B(this,pk),normalMap:new az(this,pk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const gk={directParams:!0};class vk extends(KD(HD(nB(mB(pB(PB(oB(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1),this.sizeAttenuation=aa.BOOLEAN(1)}}}(eB(la)))))))))){}const yk=new vk;class xk extends kD{constructor(){super(...arguments),this.paramsConfig=yk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,gk),map:new _B(this,gk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new xs.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),tB.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class bk extends(KD(HD(ZD(nB(YD(eB(la))))))){}const wk=new bk;class Tk extends QD{constructor(){super(...arguments),this.paramsConfig=wk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return jn.GL_POINTS}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class Ak extends(HD(oB(la))){}const Mk=new Ak;class Ek extends kD{constructor(){super(...arguments),this.paramsConfig=Mk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new zf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),this.setMaterial(this.material)}}class Sk extends B.a{constructor(){const t=Sk.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}Sk.prototype.isSky=!0,Sk.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ck=new class extends la{constructor(){super(...arguments),this.turbidity=aa.FLOAT(2,{range:[0,20]}),this.rayleigh=aa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=aa.FLOAT(.005),this.mieDirectional=aa.FLOAT(.8),this.inclination=aa.FLOAT(.5),this.azimuth=aa.FLOAT(.25),this.up=aa.VECTOR3([0,1,0])}};class Nk extends kD{constructor(){super(...arguments),this.paramsConfig=Ck}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new Sk).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var Lk=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include \\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Ok=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const Pk={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};function Rk(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.stepSize=aa.FLOAT(.01),this.density=aa.FLOAT(1),this.shadowDensity=aa.FLOAT(1),this.lightDir=aa.VECTOR3([-1,-1,-1])}}}Rk(la);class Ik{constructor(t){this.node=t}static render_hook(t,e,n,i,s,r,o){if(o){this._object_bbox.setFromObject(o);const t=s;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}Ik._object_bbox=new Ay.a;class Fk extends(Rk(la)){}const Dk=new Fk;class Bk extends kD{constructor(){super(...arguments),this.paramsConfig=Dk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:Lk,fragmentShader:Ok,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(Pk)});return gr.add_user_data_render_hook(t,Ik.render_hook.bind(Ik)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class zk extends(ZD(Rk(la))){}const kk=new zk;class Uk extends QD{constructor(){super(...arguments),this.paramsConfig=kk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return jn.GL_VOLUME}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class Gk extends sa{static context(){return ts.MAT}cook(){this.cookController.endCook()}}class Vk extends Gk{}class Hk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class jk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Wk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class qk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Xk extends Gk{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Yk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var $k=n(87);const Jk=\\\\\\\"parent object\\\\\\\",Zk=[Jk,Jk,Jk,Jk];var Qk;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(Qk||(Qk={}));class Kk extends sa{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return ts.OBJ}static displayedInputNames(){return Zk}_create_children_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new K.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class tU extends Kk{constructor(){super(...arguments),this.flags=new Di(this),this.renderOrder=Qk.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const eU=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1)}};class nU extends tU{constructor(){super(...arguments),this.paramsConfig=eU}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new $k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class iU extends sv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}iU.prototype.isRectAreaLight=!0;var sU,rU=n(61);class oU{static init(){const 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pU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class _U{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new uU,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(pU)||this.node.dirtyController.addPostDirtyHook(pU,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,cU[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){uU.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),uU.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),uU.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class mU{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&_U.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){mU.on_input_updated(this.node)}}dU(la);class fU extends tU{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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lt.a({side:w.i,fog:!1})))}updateMatrixWorld(){if(this.scale.set(.5*this.light.width,.5*this.light.height,1),void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);const t=this.material.color,e=Math.max(t.r,t.g,t.b);e>1&&t.multiplyScalar(1/e),this.children[0].material.color.copy(this.material.color)}this.matrixWorld.extractRotation(this.light.matrixWorld).scale(this.scale).copyPosition(this.light.matrixWorld),this.children[0].matrixWorld.copy(this.matrixWorld)}dispose(){this.geometry.dispose(),this.material.dispose(),this.children[0].geometry.dispose(),this.children[0].material.dispose()}}xU=la;var xU;class bU{constructor(t,e){this.node=t,this._name=e,this._object=this.createObject(),this._material=new lt.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class wU extends bU{createObject(){return new yU(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class TU extends(function(t){return class extends t{constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1,{range:[0,10]}),this.width=aa.FLOAT(1,{range:[0,10]}),this.height=aa.FLOAT(1,{range:[0,10]}),this.showHelper=aa.BOOLEAN(0)}}}(dU(la))){}const AU=new TU;class MU extends fU{constructor(){super(...arguments),this.paramsConfig=AU,this._helperController=new gU(this,wU,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new iU(16777215,1,1,1);return t.matrixAutoUpdate=!1,oU.initialized||(oU.init(),oU.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var EU=n(72);const SU=new p.a,CU=new tf.a;class NU extends As.a{constructor(t){const e=new S.a,n=new Ts.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],s=[],r={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),h=new D.a(3355443);function u(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),s.push(e.r,e.g,e.b),void 0===r[t]&&(r[t]=[]),r[t].push(i.length/3-1)}u(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),u(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),u(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),u(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),u(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),u(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),u(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",h),u(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",h),u(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",h),u(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",h),u(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",h),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(s,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=r,this.update()}update(){const t=this.geometry,e=this.pointMap;CU.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),LU(\\\\\\\"c\\\\\\\",e,t,CU,0,0,-1),LU(\\\\\\\"t\\\\\\\",e,t,CU,0,0,1),LU(\\\\\\\"n1\\\\\\\",e,t,CU,-1,-1,-1),LU(\\\\\\\"n2\\\\\\\",e,t,CU,1,-1,-1),LU(\\\\\\\"n3\\\\\\\",e,t,CU,-1,1,-1),LU(\\\\\\\"n4\\\\\\\",e,t,CU,1,1,-1),LU(\\\\\\\"f1\\\\\\\",e,t,CU,-1,-1,1),LU(\\\\\\\"f2\\\\\\\",e,t,CU,1,-1,1),LU(\\\\\\\"f3\\\\\\\",e,t,CU,-1,1,1),LU(\\\\\\\"f4\\\\\\\",e,t,CU,1,1,1),LU(\\\\\\\"u1\\\\\\\",e,t,CU,.7,1.1,-1),LU(\\\\\\\"u2\\\\\\\",e,t,CU,-.7,1.1,-1),LU(\\\\\\\"u3\\\\\\\",e,t,CU,0,2,-1),LU(\\\\\\\"cf1\\\\\\\",e,t,CU,-1,0,1),LU(\\\\\\\"cf2\\\\\\\",e,t,CU,1,0,1),LU(\\\\\\\"cf3\\\\\\\",e,t,CU,0,-1,1),LU(\\\\\\\"cf4\\\\\\\",e,t,CU,0,1,1),LU(\\\\\\\"cn1\\\\\\\",e,t,CU,-1,0,-1),LU(\\\\\\\"cn2\\\\\\\",e,t,CU,1,0,-1),LU(\\\\\\\"cn3\\\\\\\",e,t,CU,0,-1,-1),LU(\\\\\\\"cn4\\\\\\\",e,t,CU,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function LU(t,e,n,i,s,r,o){SU.set(s,r,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e.9&&o<.1&&(e<.2&&(r[t+0]+=1),n<.2&&(r[t+2]+=1),i<.2&&(r[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(r,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new zU(t.vertices,t.indices,t.radius,t.details)}}class kU extends zU{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new kU(t.radius,t.detail)}}class UU extends bU{constructor(){super(...arguments),this._geometry=new kU(1),this._quat=new ah.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new B.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e0)&&d.push(e,s,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new QU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class KU extends QU{constructor(t=1,e=1,n=8,i=1,s=!1,r=0,o=2*Math.PI){super(0,t,e,n,i,s,r,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:s,thetaStart:r,thetaLength:o}}static fromJSON(t){return new KU(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class tG{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;ZU.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new KU(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new B.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class eG extends(dU(la)){constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1),this.angle=aa.FLOAT(45,{range:[0,180]}),this.penumbra=aa.FLOAT(.1),this.decay=aa.FLOAT(.1,{range:[0,1]}),this.distance=aa.FLOAT(100,{range:[0,100]}),this.showHelper=aa.BOOLEAN(0),this.helperSize=aa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=aa.FOLDER(),this.castShadow=aa.BOOLEAN(1),this.shadowAutoUpdate=aa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=aa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=aa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=aa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=aa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=aa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=aa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=aa.FOLDER(),this.tvolumetric=aa.BOOLEAN(0),this.volAttenuation=aa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=aa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const nG=new eG;class iG extends fU{constructor(){super(...arguments),this.paramsConfig=nG,this._helperController=new gU(this,ZU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new tG(this)}static type(){return PU.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new JU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let sG;const rG=function(){return void 0===sG&&(sG=new(window.AudioContext||window.webkitAudioContext)),sG},oG=new p.a,aG=new ah.a,lG=new p.a,cG=new p.a;class hG extends K.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=rG(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(oG,aG,lG),cG.set(0,0,-1).applyQuaternion(aG),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(oG.x,t),e.positionY.linearRampToValueAtTime(oG.y,t),e.positionZ.linearRampToValueAtTime(oG.z,t),e.forwardX.linearRampToValueAtTime(cG.x,t),e.forwardY.linearRampToValueAtTime(cG.y,t),e.forwardZ.linearRampToValueAtTime(cG.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(oG.x,oG.y,oG.z),e.setOrientation(cG.x,cG.y,cG.z,n.x,n.y,n.z)}}class uG extends(dU(la)){}const dG=new uG;class pG extends Kk{constructor(){super(...arguments),this.paramsConfig=dG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new hG;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class _G extends As.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new Ts.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,s=this.geometry.attributes.color.array;return i.set(t),i.toArray(s,0),i.toArray(s,3),i.set(e),i.toArray(s,6),i.toArray(s,9),i.set(n),i.toArray(s,12),i.toArray(s,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var mG;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(mG||(mG={}));const fG=[mG.TOGETHER,mG.SEPARATELY];const gG=new class extends la{constructor(){super(...arguments),this.object0=aa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.object1=aa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.mode=aa.INTEGER(fG.indexOf(mG.TOGETHER),{menu:{entries:fG.map(((t,e)=>({name:t,value:e})))}}),this.blend=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class vG extends Kk{constructor(){super(...arguments),this.paramsConfig=gG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._t0=new p.a,this._q0=new ah.a,this._s0=new p.a,this._t1=new p.a,this._q1=new ah.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(ts.OBJ),e=this.p.object1.found_node_with_context(ts.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=fG[this.pv.mode];switch(n){case mG.TOGETHER:return this._blend_together(t,e);case mG.SEPARATELY:return this._blend_separately(t,e)}ls.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var yG={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const xG={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},bG=1/256e3;class wG{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(yG),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(xG),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new ot.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new B.a(t)}applyBlur(t,e,n,i){const s=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*s*bG,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*s*bG,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var TG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(TG||(TG={}));const AG=[TG.ON_RENDER,TG.MANUAL];class MG extends(dU(la)){constructor(){super(...arguments),this.shadow=aa.FOLDER(),this.dist=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=aa.VECTOR2([1,1]),this.shadowRes=aa.VECTOR2([256,256]),this.blur=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=aa.BOOLEAN(1),this.blur2=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=aa.FLOAT(1),this.opacity=aa.FLOAT(1),this.showHelper=aa.BOOLEAN(0),this.updateMode=aa.INTEGER(AG.indexOf(TG.ON_RENDER),{callback:t=>{CG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:AG.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:AG.indexOf(TG.MANUAL)}}),this.scene=aa.FOLDER(),this.include=aa.STRING(\\\\\\\"\\\\\\\"),this.exclude=aa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const EG=new MG,SG=new d.a(256,256);class CG extends Kk{constructor(){super(...arguments),this.paramsConfig=EG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._renderTarget=this._createRenderTarget(SG),this._coreRenderBlur=this._createCoreRenderBlur(SG),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new _U(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new wG(t)}createObject(){const t=new Fn.a;this._shadowGroup=new Fn.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new lt.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new B.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new ot.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new NU(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Sn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,s=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=s,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=AG[this.pv.updateMode];switch(t){case TG.ON_RENDER:return this._addRenderHook();case TG.MANUAL:return this._removeRenderHook()}ls.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,s,r){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=li.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const NG=\\\\\\\"display\\\\\\\";class LG{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(NG),i=this.node.params.boolean(NG),s=this.node.usedInScene(),r=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return s&&r&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class OG extends(dU(la)){constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.renderOrder=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const PG=new OG;class RG extends Kk{constructor(){super(...arguments),this.paramsConfig=PG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class IG extends(dU(la)){}const FG=new IG;class DG extends Kk{constructor(){super(...arguments),this.paramsConfig=FG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._helper=new _G(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const BG=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR3([0,0,0]),this.longitude=aa.FLOAT(0,{range:[0,360]}),this.latitude=aa.FLOAT(0,{range:[-180,180]}),this.depth=aa.FLOAT(1,{range:[0,10]})}},zG=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",kG=new p.a(0,1,0),UG=new p.a(-1,0,0);class GG extends Kk{constructor(){super(...arguments),this.paramsConfig=BG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new ah.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(zG)||this.dirtyController.addPostDirtyHook(zG,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(kG,Object(On.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(UG,Object(On.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class VG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=JG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||El.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=os.escapeLineBreaks(t)}}class HG{constructor(t){this._node=t}data(t={}){var e,n,i,s,r,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(s=this._node.flags.display)||void 0===s?void 0:s.active())),this._node.flags.hasOptimize()&&(null===(r=this._node.flags.optimize)||void 0===r?void 0:r.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=JG.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=VG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const s=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const r=s.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[r])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=JG.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=JG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class jG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class WG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class qG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class XG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class YG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class $G extends HG{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class JG{static dispatch_node(t){return t.polyNodeController?new $G(t):new HG(t)}static dispatch_param(t){return t instanceof io?new WG(t):t instanceof _o?new qG(t):t instanceof wo?new XG(t):t instanceof bo?new YG(t):new jG(t)}}class ZG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=li.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new ZG;if(this._objects){const e=[];for(let t of this._objects)e.push(yr.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new yr(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=_r.pointsCount(t.geometry)),{type:Ls(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new _r(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new _r(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=_r.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=os.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new ZG;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>os.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new Ay.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case Hs.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=ZG.geometryFromObject(n);if(i){new _r(i).renameAttrib(t,e)}}));break;case Hs.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new yr(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=os.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(os.matchMask(t,i))n.push(t);else{t==qs.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=qs.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new Fn.a;return t.children.forEach((t=>{const n=yr.clone(t);e.add(n)})),e}}class QG extends Fl{static context(){return ts.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new ZG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Cs.MESH){const n=QG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return QG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Ns[e])(t,n=n||Hs.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){QG.createIndexIfNone(t)}static createIndexIfNone(t){dr.createIndexIfNone(t)}}var KG;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(KG||(KG={}));const tV=\\\\\\\"input geometry\\\\\\\",eV=[tV,tV,tV,tV];class nV extends sa{constructor(){super(...arguments),this.flags=new Ui(this)}static context(){return ts.SOP}static displayedInputNames(){return eV}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,KG.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],KG.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,KG.FROM_SET_OBJECTS)}setGeometry(t,e=Cs.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],KG.FROM_SET_GEOMETRY)}setGeometries(t,e=Cs.MESH){const n=[];let i;for(let s of t)i=this.createObject(s,e),n.push(i);this._setContainerObjects(n,KG.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new ZG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return QG.createObject(t,e,n)}createObject(t,e,n){return nV.createObject(t,e,n)}static createIndexIfNone(t){QG.createIndexIfNone(t)}_createIndexIfNone(t){nV.createIndexIfNone(t)}}const iV=new class extends la{};class sV extends nV{constructor(){super(...arguments),this.paramsConfig=iV}static type(){return ns.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){this.setCoreGroup(t[0])}}class rV extends nV{constructor(){super(...arguments),this.childrenDisplayController=new aV(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const oV={dependsOnDisplayNode:!0};class aV{constructor(t,e=oV){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(sV.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Mi(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function lV(t,e){const n=new class extends la{constructor(){super(...arguments),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends rV{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new uV(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const cV=lV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.SOP,inputs:[0,4]});class hV extends cV{}class uV{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Xl({}),i=new kl(this.node);i.create_nodes(n,t);const s=this._definition.ui;s&&i.process_nodes_ui_data(n,s),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=JG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),s={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(s))}}static createNodeClass(t,e,n){switch(e){case ts.SOP:return lV(t,n);case ts.OBJ:return dV(t,n)}}}function dV(t,e){const n=new class extends la{constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends Kk{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new mU(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this.polyNodeController=new uV(this,e)}static type(){return t}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const pV=dV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.OBJ});class _V extends pV{}class mV extends K.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t{t.load(e,(function(t){n(t)}))}))}}var MV;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(MV||(MV={}));const EV=[MV.LINEAR,MV.INVERSE,MV.EXPONENTIAL];class SV extends(dU(la)){constructor(){super(...arguments),this.audio=aa.FOLDER(),this.listener=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=aa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Or.AUDIO]}}),this.volume=aa.FLOAT(1),this.loop=aa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=aa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=aa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=aa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=aa.INTEGER(EV.indexOf(MV.LINEAR),{menu:{entries:EV.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=aa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=aa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0),this.play=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_pause(t)}})}}const CV=new SV;class NV extends Kk{constructor(){super(...arguments),this.paramsConfig=CV,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(EV[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.update()))}_createHelper(t){const e=new bV(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(ts.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new xV(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new AV(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var LV;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(LV||(LV={}));const OV=[LV.ON_RENDER,LV.MANUAL];const PV=new class extends la{constructor(){super(...arguments),this.object=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{RV.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=aa.INTEGER(0,{range:[0,100]}),this.updateMode=aa.INTEGER(OV.indexOf(LV.ON_RENDER),{callback:t=>{RV.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:OV.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{RV.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:OV.indexOf(LV.MANUAL)}})}};class RV extends Kk{constructor(){super(...arguments),this.paramsConfig=PV,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new B.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=OV[this.pv.updateMode];switch(t){case LV.ON_RENDER:return this._add_render_hook();case LV.MANUAL:return this._remove_render_hook()}ls.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,s,r){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==ts.OBJ&&t.type()==RG.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class IV extends(Na(Ea(ya(fa(ua(la)))))){}const FV=new IV;class DV extends Kk{constructor(){super(...arguments),this.paramsConfig=FV,this.hierarchyController=new mU(this),this.SceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.SceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new gs;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class BV{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._updateRequired=this._controls_node.updateRequired()}updateRequired(){return this._updateRequired}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const zV=\\\\\\\"controls\\\\\\\";class kV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(zV)?this.node.params.get(zV):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(_s.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let s=!1,r=this._applied_controls_by_element_id.get(e.id);if(r&&r.get(i)&&(s=!0),!s){r=new Map,this._applied_controls_by_element_id.set(e.id,r),r.set(i,n);const s=await n.apply_controls(this.node.object,t);if(!s)return;const o=new BV(this.node.graphNodeId(),n,s);return this.set_controls_events(s),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.disposeControlsForHtmlElementId(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=OH[this.node.pv.updateFromControlsMode];switch(e){case LH.ON_END:return this._set_controls_events_to_update_on_end(t);case LH.ALWAYS:return this._set_controls_events_to_update_always(t);case LH.NEVER:return this._reset(t)}ls.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function UV(t){return class extends t{constructor(){super(...arguments),this.layer=aa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class GV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const VV={callback:t=>{UH.PARAM_CALLBACK_reset_effects_composer(t)}};function HV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=aa.BOOLEAN(0),this.postProcessNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[es.POST]},...VV})}}}class jV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,s=this.node.p.postProcessNode.value.node();if(s){if(s.type()==es.POST){const r=s,o=this.node.renderController.canvas_resolution(t);return r.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class WV extends sa{constructor(){super(...arguments),this.flags=new Pi(this)}static context(){return ts.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var qV,XV,YV,$V;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(qV||(qV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(XV||(XV={})),($V=YV||(YV={}))[$V.Linear=w.U]=\\\\\\\"Linear\\\\\\\",$V[$V.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",$V[$V.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",$V[$V.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",$V[$V.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",$V[$V.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",$V[$V.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",$V[$V.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const JV=[XV.Linear,XV.sRGB,XV.Gamma,XV.RGBE,XV.LogLuv,XV.RGBM7,XV.RGBM16,XV.RGBD],ZV=[YV.Linear,YV.sRGB,YV.Gamma,YV.RGBE,YV.LogLuv,YV.RGBM7,YV.RGBM16,YV.RGBD],QV=YV.sRGB;var KV,tH,eH;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(KV||(KV={})),(eH=tH||(tH={}))[eH.No=w.vb]=\\\\\\\"No\\\\\\\",eH[eH.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",eH[eH.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",eH[eH.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",eH[eH.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const nH=[KV.No,KV.Linear,KV.Reinhard,KV.Cineon,KV.ACESFilmic],iH=[tH.No,tH.Linear,tH.Reinhard,tH.Cineon,tH.ACESFilmic],sH=tH.ACESFilmic,rH=nH.map(((t,e)=>({name:t,value:iH[e]})));var oH;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(oH||(oH={}));const aH=[oH.HIGH,oH.MEDIUM,oH.LOW];var lH;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(lH||(lH={}));const cH=[lH.HIGH,lH.LOW,lH.DEFAULT];var hH,uH,dH;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(hH||(hH={})),(dH=uH||(uH={}))[dH.Basic=w.k]=\\\\\\\"Basic\\\\\\\",dH[dH.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",dH[dH.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",dH[dH.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const pH=[hH.Basic,hH.PCF,hH.PCFSoft,hH.VSM],_H=[uH.Basic,uH.PCF,uH.PCFSoft,uH.VSM],mH=(w.k,w.Fb,w.Gb,w.gd,uH.PCFSoft),fH={alpha:!1,precision:oH.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:lH.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const gH=new class extends la{constructor(){super(...arguments),this.tprecision=aa.BOOLEAN(0),this.precision=aa.INTEGER(aH.indexOf(oH.HIGH),{visibleIf:{tprecision:1},menu:{entries:aH.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=aa.BOOLEAN(0),this.powerPreference=aa.INTEGER(cH.indexOf(lH.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:cH.map(((t,e)=>({value:e,name:t})))}}),this.alpha=aa.BOOLEAN(1),this.premultipliedAlpha=aa.BOOLEAN(1),this.antialias=aa.BOOLEAN(1),this.stencil=aa.BOOLEAN(1),this.depth=aa.BOOLEAN(1),this.logarithmicDepthBuffer=aa.BOOLEAN(0),this.toneMapping=aa.INTEGER(sH,{menu:{entries:rH}}),this.toneMappingExposure=aa.FLOAT(1,{range:[0,2]}),this.outputEncoding=aa.INTEGER(QV,{menu:{entries:JV.map(((t,e)=>({name:t,value:ZV[e]})))}}),this.physicallyCorrectLights=aa.BOOLEAN(1),this.sortObjects=aa.BOOLEAN(1),this.tpixelRatio=aa.BOOLEAN(0),this.pixelRatio=aa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=aa.BOOLEAN(1),this.shadowMapAutoUpdate=aa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=aa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=aa.INTEGER(mH,{visibleIf:{tshadowMap:1},menu:{entries:pH.map(((t,e)=>({name:t,value:_H[e]})))}})}};class vH extends WV{constructor(){super(...arguments),this.paramsConfig=gH,this._renderers_by_canvas_id={}}static type(){return qV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(fH);let s;for(s of i)n[s]=fH[s];if(this.pv.tprecision){const t=aH[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=cH[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const r=li.renderersController.createWebGLRenderer(n);return li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),li.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[t.id]=r,r}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:xH.defaultPixelRatio();li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),li.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function yH(t){return class extends t{constructor(){super(...arguments),this.render=aa.FOLDER(),this.setScene=aa.BOOLEAN(0),this.scene=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]}}),this.setRenderer=aa.BOOLEAN(0),this.renderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:ts.ROP,types:[vH.type()]}}),this.setCSSRenderer=aa.BOOLEAN(0),this.CSSRenderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:ts.ROP,types:[qV.CSS2D,qV.CSS3D]}})}}}class xH{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,this.node.object)}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(ts.OBJ,DV.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(ts.ROP,qV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(ts.ROP,[qV.CSS2D,qV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=li.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=xH._createDefaultRenderer(t,n)),li.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return Zf.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=li.renderersController.createWebGLRenderer(n),s=this.defaultPixelRatio();return i.setPixelRatio(s),i.shadowMap.enabled=!0,i.shadowMap.type=mH,i.physicallyCorrectLights=!0,i.toneMapping=sH,i.toneMappingExposure=1,i.outputEncoding=QV,li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),li.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}delete_renderer(t){const e=this.renderer(t);e&&li.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class bH{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(t){this._config&&this._controls&&this._config.updateRequired()&&this._controls.update(t)}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Mi(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.create_controls()})),t}}class wH{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class TH{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const AH=\\\\\\\"hovered\\\\\\\";class MH{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=MH._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new wH(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Va(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new TH(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(AH)}setContainerClassHovered(){this.domElement().classList.add(AH)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}MH._next_viewer_id=0;class EH extends MH{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._clock=new Om,this._delta=0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new bH(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t;if(this._do_render){if(this._delta=this._clock.getDelta(),this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(this._scene.timeController.incrementTimeIfPlaying(this._delta),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta);this.render(this._delta),null===(t=this._controls_controller)||void 0===t||t.update(this._delta)}}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.delete_renderer(this._canvas)}render(t){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let e of this._onBeforeRenderCallbacks)e(t);const e=this.camerasController.size,n=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,e,n),this._onAfterRenderCallbacks)for(let e of this._onAfterRenderCallbacks)e(t)}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const SH={type:\\\\\\\"change\\\\\\\"},CH=1,NH=100;var LH;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(LH||(LH={}));const OH=[LH.ON_END,LH.ALWAYS,LH.NEVER];function PH(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=aa.BUTTON(null,{callback:(t,e)=>{kH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function RH(t){return class extends t{constructor(){super(...arguments),this.camera=aa.FOLDER(),this.controls=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.EVENT}}),this.updateFromControlsMode=aa.INTEGER(OH.indexOf(LH.ON_END),{menu:{entries:OH.map(((t,e)=>({name:t,value:e})))}}),this.near=aa.FLOAT(CH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=aa.FLOAT(NH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0)}}}var IH;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}(IH||(IH={}));const FH=[IH.DEFAULT,IH.COVER,IH.CONTAIN];function DH(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=aa.INTEGER(FH.indexOf(IH.DEFAULT),{menu:{entries:FH.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=aa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:FH.indexOf(IH.COVER)},{fovAdjustMode:FH.indexOf(IH.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}PH(la);HV(yH(dU(UV(RH(PH(la))))));class BH extends Kk{constructor(){super(...arguments),this.renderOrder=Qk.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(SH),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){uU.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class zH extends BH{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new kV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new GV(this)}get renderController(){return this._render_controller=this._render_controller||new xH(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new jV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(SH),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new EH(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new NU(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class kH extends BH{}class UH extends zH{PARAM_CALLBACK_update_effects_composer(t){}}const GH=-.5,VH=.5,HH=.5,jH=-.5;class WH extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1)}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const qH=new WH;class XH extends zH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return is.ORTHOGRAPHIC}createObject(){return new ot.a(2*GH,2*VH,2*HH,2*jH,CH,NH)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=GH*e*1,this._object.right=VH*e*1,this._object.top=HH*t*1,this._object.bottom=jH*t*1}}const YH=50;class $H extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.fov=aa.FLOAT(YH,{range:[0,100]})}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const JH=new $H;class ZH extends zH{constructor(){super(...arguments),this.paramsConfig=JH}static type(){return is.PERSPECTIVE}createObject(){return new tt.a(YH,1,CH,NH)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}}}class QH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=aa.FOLDER(),this.resolution=aa.INTEGER(256),this.excludedObjects=aa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_printResolve(t)}}),this.near=aa.FLOAT(1),this.far=aa.FLOAT(100),this.render=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_render(t)}}),this.renderTarget=aa.FOLDER(),this.tencoding=aa.BOOLEAN(0),this.encoding=aa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(dU(la))){}const KH=new QH;class tj extends Kk{constructor(){super(...arguments),this.paramsConfig=KH,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new _G(1)}static type(){return Ng.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,s=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&s==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new st(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new nt(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=li.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class ej extends Kk{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class nj extends ej{}class ij extends nj{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sj extends ij{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER}}class rj extends nj{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class aj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lj extends ej{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const hj=[\\\\\\\"input pass\\\\\\\"];const uj={cook:!1,callback:function(t,e){dj.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class dj extends sa{constructor(){super(...arguments),this.flags=new zi(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return ts.POST}static displayedInputNames(){return hj}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const pj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var _j={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class mj extends Im{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===Rm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Rm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===pj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(pj.uniforms),vertexShader:pj.vertexShader,fragmentShader:pj.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===_j&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(_j.uniforms),vertexShader:_j.vertexShader,fragmentShader:_j.fragmentShader,blending:w.ub}),this.fsQuad=new Bm(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Q(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Q(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Q(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new lt.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const fj=new class extends la{constructor(){super(...arguments),this.adaptive=aa.BOOLEAN(1,{...uj}),this.averageLuminance=aa.FLOAT(.7,{...uj}),this.midGrey=aa.FLOAT(.04,{...uj}),this.maxLuminance=aa.FLOAT(16,{range:[0,20],...uj}),this.adaptiveRange=aa.FLOAT(2,{range:[0,10],...uj})}};class gj extends dj{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new mj(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const vj={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class yj extends Im{constructor(t=.96){super(),void 0===vj&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=vj,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new Bm(this.shaderMaterial);const e=new lt.a;this.copyFsQuad=new Bm(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const xj=new class extends la{constructor(){super(...arguments),this.damp=aa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...uj})}};class bj extends dj{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new yj;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const wj={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Tj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...uj})}};class Aj extends dj{constructor(){super(...arguments),this.paramsConfig=Tj}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new zm(wj);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const Mj={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ej=new class extends la{constructor(){super(...arguments),this.brightness=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.contrast=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Sj extends dj{constructor(){super(...arguments),this.paramsConfig=Ej}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new zm(Mj);return e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class Cj extends Im{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const Nj=new class extends la{};class Lj extends dj{constructor(){super(...arguments),this.paramsConfig=Nj}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new Cj;return this.updatePass(e),e}updatePass(t){}}const Oj=new class extends la{};class Pj extends dj{constructor(){super(...arguments),this.paramsConfig=Oj}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const Rj={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ij=new class extends la{constructor(){super(...arguments),this.pow=aa.VECTOR3([2,2,2],{...uj}),this.mult=aa.COLOR([1,1,1],{...uj}),this.add=aa.COLOR([0,0,0],{...uj})}};class Fj extends dj{constructor(){super(...arguments),this.paramsConfig=Ij}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new zm(Rj);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const Dj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Bj extends dj{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new zm(Rm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const zj={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth; //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4 HS0 = vec4( 1.0, 0.0, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS1 = vec4( 0.309016994, 0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS2 = vec4(-0.809016994, 0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS3 = vec4(-0.809016994,-0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS4 = vec4( 0.309016994,-0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS5 = vec4( 0.0 ,0.0 , 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x, 0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0, 0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x, 0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0, wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0; kernel[1] = 2.0/16.0; kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0; kernel[4] = 4.0/16.0; kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0; kernel[7] = 2.0/16.0; kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},kj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Uj{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const s={};let r;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(r=a,r.uniforms)for(let t of Object.keys(n))r.uniforms[t].value=n[t].value}else r=_r.markedAsInstance(i)?e:t;r&&(s[o.uuid]=o.material,o.material=r)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=s[e.uuid])}}));for(let t of Object.keys(s))delete s[t]}}class Gj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new gs,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new Uj(this._scene);const s=3,r=4;this._processing_camera=new ot.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Q(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Q(this._resolution.x,this._resolution.y,o);var a=zj;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:s,SAMPLES:r}}),this._quad=new B.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=kj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=Hj.smoothstep(e.near,e.far,t.pv.focalDepth),i=Hj.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const Vj=new class extends la{constructor(){super(...arguments),this.focalDepth=aa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...uj}),this.fStep=aa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...uj}),this.maxBlur=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.vignetting=aa.BOOLEAN(0,{...uj}),this.depthBlur=aa.BOOLEAN(0,{...uj}),this.threshold=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...uj}),this.gain=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...uj}),this.bias=aa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...uj}),this.fringe=aa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...uj}),this.noise=aa.BOOLEAN(0,{...uj}),this.dithering=aa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...uj}),this.pentagon=aa.BOOLEAN(0,{...uj}),this.rings=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...uj}),this.samples=aa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...uj}),this.clearColor=aa.COLOR([1,1,1],{...uj})}};class Hj extends dj{constructor(){super(...arguments),this.paramsConfig=Vj}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new Gj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Mi(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const jj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Wj=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR2([.5,.5],{...uj}),this.angle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...uj}),this.scale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj})}};class qj extends dj{constructor(){super(...arguments),this.paramsConfig=Wj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new zm(jj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const Xj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Yj extends Im{constructor(t,e,n,i){super(),void 0===Xj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const s=Xj;this.uniforms=I.clone(s.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:s.vertexShader,fragmentShader:s.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new Bm(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const $j=new class extends la{constructor(){super(...arguments),this.noiseIntensity=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.scanlinesIntensity=aa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.scanlinesCount=aa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...uj}),this.grayscale=aa.BOOLEAN(1,{...uj})}};class Jj extends dj{constructor(){super(...arguments),this.paramsConfig=$j}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new Yj(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const Zj={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const Qj=new class extends la{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(1,uj)}};class Kj extends dj{constructor(){super(...arguments),this.paramsConfig=Qj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new zm(Zj);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const tW={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const eW=new class extends la{};class nW extends dj{constructor(){super(...arguments),this.paramsConfig=eW}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new zm(tW);return this.updatePass(e),e}updatePass(t){}}const iW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class sW extends dj{constructor(){super(...arguments),this.paramsConfig=iW}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new zm(yG);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const rW=new class extends la{constructor(){super(...arguments),this.map=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP},...uj}),this.darkness=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj}),this.offset=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class oW extends dj{constructor(){super(...arguments),this.paramsConfig=rW}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new zm(oW._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==ts.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const aW={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},lW=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",cW=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class hW extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(aW),this.material=new F({uniforms:this.uniforms,vertexShader:lW,fragmentShader:cW,transparent:!0}),this.fsQuad=new Bm(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const uW=new class extends la{};class dW extends dj{constructor(){super(...arguments),this.paramsConfig=uW}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=new Gm(e,i),o=new Gm(e,s);r.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=r,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new hW(r,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const pW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj}),this.inverse=aa.BOOLEAN(0,uj)}};class _W extends dj{constructor(){super(...arguments),this.paramsConfig=pW}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new km(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const mW=new class extends la{};class fW extends dj{constructor(){super(...arguments),this.paramsConfig=mW}static type(){return\\\\\\\"null\\\\\\\"}}class gW extends Im{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const s={minFilter:w.V,magFilter:w.V,format:w.Ib},r=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new lt.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Sn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Q(r,o,s),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Q(r,o,s),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Q(r,o,s),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(r,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(r/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Rm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Rm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new Bm(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,s&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=gW.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=gW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,s&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tuniform mat4 textureMatrix;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvPosition = mvPosition;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D depthTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 cameraNearFar;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getEdgeDetectionMaterial(){return new F({uniforms:{maskTexture:{value:null},texSize:{value:new d.a(.5,.5)},visibleEdgeColor:{value:new p.a(1,1,1)},hiddenEdgeColor:{value:new p.a(1,1,1)}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getSeperableBlurMaterial(t){return new F({defines:{MAX_RADIUS:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)},kernelRadius:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}gW.BlurDirectionX=new d.a(1,0),gW.BlurDirectionY=new d.a(0,1);const vW=new class extends la{constructor(){super(...arguments),this.objectsMask=aa.STRING(\\\\\\\"*outlined*\\\\\\\",{...uj}),this.refreshObjects=aa.BUTTON(null,{...uj}),this.printObjects=aa.BUTTON(null,{cook:!1,callback:t=>{yW.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=aa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.edgeThickness=aa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...uj}),this.edgeGlow=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.pulsePeriod=aa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...uj}),this.visibleEdgeColor=aa.COLOR([1,1,1],{...uj}),this.hiddenEdgeColor=aa.COLOR([.2,.1,.4],{...uj})}};class yW extends dj{constructor(){super(...arguments),this.paramsConfig=vW,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new gW(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const xW={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const bW=new class extends la{constructor(){super(...arguments),this.pixelSize=aa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...uj})}};class wW extends dj{constructor(){super(...arguments),this.paramsConfig=bW}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new zm(xW);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const TW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj})}};class AW extends dj{constructor(){super(...arguments),this.paramsConfig=TW}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(ts.OBJ);if(e&&(e.type()==is.PERSPECTIVE||e.type()==is.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(ts.OBJ);if(e&&e.type()==DV.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const MW={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const EW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.angle=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...uj})}};class SW extends dj{constructor(){super(...arguments),this.paramsConfig=EW}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new zm(MW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const CW={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const NW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...uj})}};class LW extends dj{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new zm(CW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const OW=new class extends la{};class PW extends dj{constructor(){super(...arguments),this.paramsConfig=OW}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const RW=I.clone(yG.uniforms);RW.delta={value:new d.a};const IW={uniforms:RW,vertexShader:yG.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const FW=new class extends la{constructor(){super(...arguments),this.delta=aa.VECTOR2([2,2],{...uj})}};class DW extends dj{constructor(){super(...arguments),this.paramsConfig=FW}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new zm(IW);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const BW={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class zW extends Im{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const s={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Q(r,o,s),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}zW.BlurDirectionX=new d.a(1,0),zW.BlurDirectionY=new d.a(0,1);const kW=new class extends la{constructor(){super(...arguments),this.strength=aa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...uj}),this.radius=aa.FLOAT(1,{...uj}),this.threshold=aa.FLOAT(0,{...uj})}};class UW extends dj{constructor(){super(...arguments),this.paramsConfig=kW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new zW(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const GW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class VW extends dj{constructor(){super(...arguments),this.paramsConfig=GW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new zm(xG);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const HW={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const jW=new class extends la{constructor(){super(...arguments),this.offset=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.darkness=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class WW extends dj{constructor(){super(...arguments),this.paramsConfig=jW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new zm(HW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class qW extends sa{static context(){return ts.POST}cook(){this.cookController.endCook()}}class XW extends qW{}class YW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class $W extends XW{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class JW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class ZW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class QW extends qW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class KW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class tq extends sa{static context(){return ts.ROP}cook(){this.cookController.endCook()}}class eq extends tq{}class nq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iq extends K.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof iq&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return K.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class sq{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof iq){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,s=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=s,i.style.transform=s,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=rr.fit(t,this._fog_near,this._fog_far,0,1),s=rr.clamp(1-n,0,1);i.style.opacity=`${s}`,0==s&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var r=0,o=t.children.length;r{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class aq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hq extends tq{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dq extends QG{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let s=[];const r=[];let o;for(let t=0;t0&&(r.push(t-1),r.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(s,s.length);const t=r[r.length-1];r.push(t),r.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r);const l=this.createObject(a,Cs.LINE_SEGMENTS);e.push(l)}}dq.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const pq=dq.DEFAULT_PARAMS;const _q=new class extends la{constructor(){super(...arguments),this.createPoint=aa.BOOLEAN(pq.createPoint),this.pointsCount=aa.INTEGER(pq.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=aa.VECTOR3(pq.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=aa.BOOLEAN(pq.connectInputPoints),this.connectToLastPoint=aa.BOOLEAN(pq.connectToLastPoint)}};class mq extends nV{constructor(){super(...arguments),this.paramsConfig=_q}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new dq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const fq=new class extends la{};class gq extends nV{constructor(){super(...arguments),this.paramsConfig=fq}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],s=n.animations;s?(i.animations=s.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class vq{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const s=e.tracks,r=s.length,o=new Array(r),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==r;++t){const e=s[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(r),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,s=i/n,r=n/i;t.warp(1,s,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,s=i.time,r=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=s,a[1]=s+n,l[0]=t/r,l[1]=e/r,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const s=this._startTime;if(null!==s){const i=(t-s)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const r=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=n===w.db;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(n===w.cb){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}var yq=n(71),xq=n(66);class bq{constructor(t,e,n){let i,s,r;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,s=this._slerpAdditive,r=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,s=this._select,r=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,s=this._lerpAdditive,r=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=s,this._setIdentity=r,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,s=t*i+i;let r=this.cumulativeWeight;if(0===r){for(let t=0;t!==i;++t)n[s+t]=n[t];r=e}else{r+=e;const t=e/r;this._mixBufferRegion(n,s,0,t,i)}this.cumulativeWeight=r}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,s=this.cumulativeWeight,r=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,s<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-s,e)}r>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){ah.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;ah.a.multiplyQuaternionsFlat(t,r,t,e,t,n),ah.a.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}var wq=n(64);class Tq extends J.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new bq(xq.a.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;t{Mq.PARAM_CALLBACK_reset(t,e)}})}};class Mq extends nV{constructor(){super(...arguments),this.paramsConfig=Aq}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(ts.ANIM)){return new Tq(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class Eq extends QG{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,s=n.mult,r=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class Bq extends nV{constructor(){super(...arguments),this.paramsConfig=Dq}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Iq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>Oq[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new Iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class zq extends QG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,s=i.attribNamesMatchingMask(e.name);for(let t of s)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var s;const r=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>r.length)null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let h=t.getAttribute(c);if(h)this._fill_dest_array(h,o,i),h.needsUpdate=!0;else{const e=o.array.slice(0,l*r);t.setAttribute(c,new C.c(e,r))}}else null===(r=this.states)||void 0===r||r.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,s=e.array,r=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new zq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class Vq extends QG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Fs[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=zs[n.type];switch(t){case Is.VERTEX:return void await this.add_point_attribute(i,e,n);case Is.OBJECT:return void await this.add_object_attribute(i,e,n)}ls.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Bs.NUMERIC:for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(Fs[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new Vq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=zs[this.pv.type];switch(t){case Is.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Is.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ls.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Bs.NUMERIC:for(let t=0;t{s[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let r;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a{i[a][t.index()]=e}));else{const t=r.value;for(let e of n)s[e.index()*this.pv.size+a]=t}for(let t=0;t{t.setAttribValue(this.pv.name,e)}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components,n={};const i=this._vector_by_attrib_size(this.pv.size);if(i){for(let e of t)n[e.index()]=i;for(let i=0;i{n[t.index()][r]=e}));else for(let e of t){n[e.index()][r]=s.value}}for(let e=0;e{i[t.index()]=e}));const s=qs.arrayToIndexedArrays(i),r=t.coreGeometry();r&&r.setIndexedAttribute(this.pv.name,s.values,s.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.compute_expression_for_objects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Is.VERTEX:this.delete_vertex_attribute(e,t);case Is.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new _r(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new yr(n,t).deleteAttribute(e),t++}))}}}class $q{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,s=t.mult,r=this._data_from_texture(t.texture);if(!r)return;const{data:o,resx:a,resy:l}=r,c=o.length/(a*l),h=e.getAttribute(t.uvAttribName).array,u=h.length/2,d=new Array(u*n);let p,_,m,f,g,v,y,x,b;const w=rr.clamp;for(v=0;v{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new Jq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var tX;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(tX||(tX={}));const eX=[tX.MIN_MAX_TO_01,tX.VECTOR_TO_LENGTH_1];class nX extends QG{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),s=os.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of s){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(eX[n.mode]){case tX.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case tX.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,s=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n({name:t,value:e})))}}),this.name=aa.STRING(iX.name),this.changeName=aa.BOOLEAN(iX.changeName),this.newName=aa.STRING(iX.newName,{visibleIf:{changeName:1}})}};class rX extends nV{constructor(){super(...arguments),this.paramsConfig=sX}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(nX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(eX.indexOf(t))}cook(t){this._operation=this._operation||new nX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oX;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(oX||(oX={}));class aX extends QG{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=os.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Is.VERTEX:return this.find_values_from_points(t,e);case Is.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let s=0;s{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Ds.filter((t=>t.value==this.pv.classFrom))[0].name,e=Ds.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new aX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const dX=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(),this.ramp=aa.RAMP(),this.changeName=aa.BOOLEAN(0),this.newName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class pX extends nV{constructor(){super(...arguments),this.paramsConfig=dX}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let s=new Array(e.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,t.hasAttrib(r)||t.addNumericVertexAttrib(r,n,0));let o=0;for(let t of s){e[o].setAttribValue(r,t),o++}}_get_remaped_values(t,e,n){switch(t){case Us.FLOAT:return this._get_normalized_float(e,n);case Us.VECTOR2:return this._get_normalized_vector2(e,n);case Us.VECTOR3:return this._get_normalized_vector3(e,n);case Us.VECTOR4:return this._get_normalized_vector4(e,n)}ls.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var fX=n(18);class gX{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new gX(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new Ay.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class vX{constructor(t){this._root=new gX(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new fX.a(t,e);let s=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,s),null==n||s.length>n&&(s=f.sortBy(s,(e=>e.position().distanceTo(t))),s=s.slice(0,n)),s}}class yX{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const xX=new class extends la{constructor(){super(...arguments),this.srcGroup=aa.STRING(),this.destGroup=aa.STRING(),this.name=aa.STRING(),this.maxSamplesCount=aa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=aa.FLOAT(1),this.blendWidth=aa.FLOAT(0)}};class bX extends nV{constructor(){super(...arguments),this.paramsConfig=xX}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new vX(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new yX;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,s){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,s);default:return this._interpolate_with_multiple_points(t,e,n,i,s)}}static _interpolate_with_1_point(t,e,n,i,s){const r=t.position(),o=e.position(),a=r.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,s):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,s,r){if(i<=s)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,s,r);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,s){const r=e.map((e=>this._interpolate_with_1_point(t,e,n,i,s)));return f.max(r)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const wX=new class extends la{constructor(){super(...arguments),this.stepSize=aa.FLOAT(.1)}};class TX extends nV{constructor(){super(...arguments),this.paramsConfig=wX}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),s=i.min,r=i.max,o=[];for(let t=s.x;t<=r.x;t+=n)for(let e=s.y;e<=r.y;e+=n)for(let i=s.z;i<=r.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Cs.POINTS)}}const AX=new class extends la{constructor(){super(...arguments),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.blend=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class MX extends nV{constructor(){super(...arguments),this.paramsConfig=AX}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),s=n.objects();let r,o;for(let t=0;tt.flip())),t}}EX.fromPolygons=function(t){let e=new EX;return e.polygons=t,e};class SX{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new SX(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(CX.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,s=e.z,r=t.x,o=t.y,a=t.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let CX=new SX,NX=new SX;class LX{constructor(t,e,n,i){this.pos=(new SX).copy(t),this.normal=(new SX).copy(e),this.uv=(new SX).copy(n),this.uv.z=0,i&&(this.color=(new SX).copy(i))}clone(){return new LX(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new LX(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class OX{constructor(t,e){this.normal=t,this.w=e}clone(){return new OX(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,s){let r=0,o=[];for(let e=0;eOX.EPSILON?1:0;r|=i,o.push(i)}switch(r){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:s.push(t);break;case 3:let r=[],a=[];for(let e=0;e=3&&i.push(new PX(r,t.shared)),a.length>=3&&s.push(new PX(a,t.shared))}}}OX.EPSILON=1e-5,OX.fromPoints=function(t,e,n){let i=CX.copy(e).sub(t).cross(NX.copy(n).sub(t)).normalize();return new OX(i.clone(),i.dot(t))};class PX{constructor(t,e){this.vertices=t,this.shared=e,this.plane=OX.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new PX(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class RX{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new RX;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;tnew PX(t.vertices.map((t=>new LX(t.pos,t.normal,t.uv))),t.shared))))},EX.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,s=t.vertices,r=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),BX=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var zX;EX.toMesh=function(t,e,n){let i,s,r=t.polygons;{let t=0;r.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=DX(3*t*3),o=DX(3*t*3),a=BX(2*t*3),l=[];if(r.forEach((i=>{let s=i.vertices,r=s.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),r&&void 0!==s[0].color&&(e||(e=DX(3*t*3)));for(let t=3;t<=r;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(s[0].pos),n.write(s[t-2].pos),n.write(s[t-1].pos),o.write(s[0].normal),o.write(s[t-2].normal),o.write(s[t-1].normal),a.write(s[0].uv),a.write(s[t-2].uv),a.write(s[t-1].uv),e&&(e.write(s[0].color)||e.write(s[t-2].color)||e.write(s[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n({name:t,value:e})))}}),this.useBothMaterials=aa.BOOLEAN(GX.useBothMaterials)}};class HX extends nV{constructor(){super(...arguments),this.paramsConfig=VX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(UX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>kX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(kX.indexOf(t))}async cook(t){this._operation=this._operation||new UX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jX extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(s.x,s.y,s.z,n,n,n),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}jX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},jX.INPUT_CLONED_STATE=Ki.NEVER;const WX=jX.DEFAULT_PARAMS;const qX=new class extends la{constructor(){super(...arguments),this.size=aa.FLOAT(WX.size),this.divisions=aa.INTEGER(WX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(WX.center)}};class XX extends nV{constructor(){super(...arguments),this.paramsConfig=qX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(jX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new jX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class YX{constructor(){}}function $X(t,e,n){return n.min.x=e[t],n.min.y=e[t+1],n.min.z=e[t+2],n.max.x=e[t+3],n.max.y=e[t+4],n.max.z=e[t+5],n}function JX(t){let e=-1,n=-1/0;for(let i=0;i<3;i++){const s=t[i+3]-t[i];s>n&&(n=s,e=i)}return e}function ZX(t,e){e.set(t)}function QX(t,e,n){let i,s;for(let r=0;r<3;r++){const o=r+3;i=t[r],s=e[r],n[r]=is?i:s}}function KX(t){const e=t[3]-t[0],n=t[4]-t[1],i=t[5]-t[2];return 2*(e*n+n*i+i*e)}const tY=Math.pow(2,-24);function eY(t,e,n,i,s=null){let r=1/0,o=1/0,a=1/0,l=-1/0,c=-1/0,h=-1/0,u=1/0,d=1/0,p=1/0,_=-1/0,m=-1/0,f=-1/0;const g=null!==s;for(let i=6*e,s=6*(e+n);il&&(l=v),g&&e_&&(_=e);const y=t[i+2],x=t[i+3],b=y-x,w=y+x;bc&&(c=w),g&&ym&&(m=y);const T=t[i+4],A=t[i+5],M=T-A,E=T+A;Mh&&(h=E),g&&Tf&&(f=T)}i[0]=r,i[1]=o,i[2]=a,i[3]=l,i[4]=c,i[5]=h,g&&(s[0]=u,s[1]=d,s[2]=p,s[3]=_,s[4]=m,s[5]=f)}const nY=32,iY=new Array(nY).fill().map((()=>({count:0,bounds:new Float32Array(6),rightCacheBounds:new Float32Array(6),candidate:0}))),sY=new Float32Array(6);function rY(t,e){function n(e,i,d,p=null,_=0){if(!u&&_>=a&&(u=!0,l&&(console.warn(`MeshBVH: Max depth of ${a} reached when generating BVH. Consider increasing maxDepth.`),console.warn(t))),d<=c||_>=a)return e.offset=i,e.count=d,e;const m=function(t,e,n,i,s,r){let o=-1,a=0;if(0===r)o=JX(e),-1!==o&&(a=(e[o]+e[o+3])/2);else if(1===r)o=JX(t),-1!==o&&(a=function(t,e,n,i){let s=0;for(let r=e,o=e+n;r=nY&&(s=31);const r=iY[s];r.count++;const o=r.bounds;for(let t=0;t<3;t++){const i=n[e+2*t],s=n[e+2*t+1],r=i-s,a=i+s;ro[t+3]&&(o[t+3]=a)}}const d=iY[31];ZX(d.bounds,d.rightCacheBounds);for(let t=30;t>=0;t--){const e=iY[t],n=iY[t+1];QX(e.bounds,n.rightCacheBounds,e.rightCacheBounds)}let p=0;for(let e=0;e<31;e++){const n=iY[e],i=n.count,c=n.bounds,h=iY[e+1].rightCacheBounds;0!==i&&(0===p?ZX(c,sY):QX(c,sY,sY)),p+=i;let u=0,d=0;0!==p&&(u=KX(sY)/r);const _=s-p;0!==_&&(d=KX(h)/r);const m=1+1.25*(u*p+d*_);m=a;)o--;if(!(r65535?new Uint32Array(new i(4*n)):new Uint16Array(new i(2*n)),t.setIndex(new Hw(s,1));for(let t=0;td&&(d=s),a>d&&(d=a);const p=(d-l)/2,_=2*t;o[r+_+0]=l+p,o[r+_+1]=p+(Math.abs(l)+p)*tY,le[t+3]&&(e[t+3]=d)}}return o}(t,i),o=t.index.array,a=e.maxDepth,l=e.verbose,c=e.maxLeafTris,h=e.strategy;let u=!1;const d=[],p=function(t){if(!t.groups||!t.groups.length)return[{offset:0,count:t.index.count/3}];const e=[],n=new Set;for(const e of t.groups)n.add(e.start),n.add(e.start+e.count);const i=Array.from(n.values()).sort(((t,e)=>t-e));for(let t=0;ta&&(a=e);const n=t[i+2];nl&&(l=n);const h=t[i+4];hc&&(c=h)}i[0]=s,i[1]=r,i[2]=o,i[3]=a,i[4]=l,i[5]=c}(r,t.offset,t.count,s),n(e,t.offset,t.count,s),d.push(e)}else for(let t of p){const e=new YX;e.boundingData=new Float32Array(6),eY(r,t.offset,t.count,e.boundingData,s),n(e,t.offset,t.count,s),d.push(e)}return d}const oY=65535;class aY{constructor(){this.min=1/0,this.max=-1/0}setFromPointsField(t,e){let n=1/0,i=-1/0;for(let s=0,r=t.length;si?r:i}this.min=n,this.max=i}setFromPoints(t,e){let n=1/0,i=-1/0;for(let s=0,r=e.length;si?o:i}this.min=n,this.max=i}isSeparated(t){return this.min>t.max||t.min>this.max}}aY.prototype.setFromBox=function(){const t=new gb;return function(e,n){const i=n.min,s=n.max;let r=1/0,o=-1/0;for(let n=0;n<=1;n++)for(let a=0;a<=1;a++)for(let l=0;l<=1;l++){t.x=i.x*n+s.x*(1-n),t.y=i.y*a+s.y*(1-a),t.z=i.z*l+s.z*(1-l);const c=e.dot(t);r=Math.min(c,r),o=Math.max(c,o)}this.min=r,this.max=o}}();!function(){const t=new aY}();const lY=function(){const t=new gb,e=new gb,n=new gb;return function(i,s,r){const o=i.start,a=t,l=s.start,c=e;n.subVectors(o,l),t.subVectors(i.end,s.start),e.subVectors(s.end,s.start);const h=n.dot(c),u=c.dot(a),d=c.dot(c),p=n.dot(a),_=a.dot(a)*d-u*u;let m,f;m=0!==_?(h*u-p*d)/_:0,f=(h+m*u)/d,r.x=m,r.y=f}}(),cY=function(){const t=new sb,e=new gb,n=new gb;return function(i,s,r,o){lY(i,s,t);let a=t.x,l=t.y;if(a>=0&&a<=1&&l>=0&&l<=1)return i.at(a,r),void s.at(l,o);if(a>=0&&a<=1)return l<0?s.at(0,o):s.at(1,o),void i.closestPointToPoint(o,!0,r);if(l>=0&&l<=1)return a<0?i.at(0,r):i.at(1,r),void s.closestPointToPoint(r,!0,o);{let t,c;t=a<0?i.start:i.end,c=l<0?s.start:s.end;const h=e,u=n;return i.closestPointToPoint(c,!0,e),s.closestPointToPoint(t,!0,n),h.distanceToSquared(c)<=u.distanceToSquared(t)?(r.copy(h),void o.copy(c)):(r.copy(t),void o.copy(u))}}}(),hY=function(){const t=new gb,e=new gb,n=new IT,i=new YN;return function(s,r){const{radius:o,center:a}=s,{a:l,b:c,c:h}=r;i.start=l,i.end=c;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=l,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=c,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;const u=r.getPlane(n);if(Math.abs(u.distanceToPoint(a))<=o){const t=u.projectPoint(a,e);if(r.containsPoint(t))return!0}return!1}}();class uY extends Lw{constructor(...t){super(...t),this.isSeparatingAxisTriangle=!0,this.satAxes=new Array(4).fill().map((()=>new gb)),this.satBounds=new Array(4).fill().map((()=>new aY)),this.points=[this.a,this.b,this.c],this.sphere=new kb,this.plane=new IT,this.needsUpdate=!1}intersectsSphere(t){return hY(t,this)}update(){const t=this.a,e=this.b,n=this.c,i=this.points,s=this.satAxes,r=this.satBounds,o=s[0],a=r[0];this.getNormal(o),a.setFromPoints(o,i);const l=s[1],c=r[1];l.subVectors(t,e),c.setFromPoints(l,i);const h=s[2],u=r[2];h.subVectors(e,n),u.setFromPoints(h,i);const d=s[3],p=r[3];d.subVectors(n,t),p.setFromPoints(d,i),this.sphere.setFromPoints(this.points),this.plane.setFromNormalAndCoplanarPoint(o,t),this.needsUpdate=!1}}uY.prototype.closestPointToSegment=function(){const t=new gb,e=new gb,n=new YN;return function(i,s=null,r=null){const{start:o,end:a}=i,l=this.points;let c,h=1/0;for(let o=0;o<3;o++){const a=(o+1)%3;n.start.copy(l[o]),n.end.copy(l[a]),cY(n,i,t,e),c=t.distanceToSquared(e),c1-1e-10)console.warn(\\\\\\\"SeparatingAxisTriangle.intersectsTriangle: Triangles are coplanar which does not support an output edge. Setting edge to 0, 0, 0.\\\\\\\"),p.start.set(0,0,0),p.end.set(0,0,0);else{const n=this.points;let i=!1;for(let t=0;t<3;t++){const s=n[t],r=n[(t+1)%3];if(c.start.copy(s),c.end.copy(r),e.intersectLine(c,i?h.start:h.end)){if(i)break;i=!0}}const s=d.points;let r=!1;for(let e=0;e<3;e++){const n=s[e],i=s[(e+1)%3];if(c.start.copy(n),c.end.copy(i),t.intersectLine(c,r?u.start:u.end)){if(r)break;r=!0}}if(h.delta(o),u.delta(a),o.dot(a)<0){let t=u.start;u.start=u.end,u.end=t}l.subVectors(h.start,u.start),l.dot(o)>0?p.start.copy(h.start):p.start.copy(u.start),l.subVectors(h.end,u.end),l.dot(o)<0?p.end.copy(h.end):p.end.copy(u.end)}}return!0}}(),uY.prototype.distanceToPoint=function(){const t=new gb;return function(e){return this.closestPointToPoint(e,t),e.distanceTo(t)}}(),uY.prototype.distanceToTriangle=function(){const t=new gb,e=new gb,n=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],i=new YN,s=new YN;return function(r,o=null,a=null){const l=o||a?i:null;if(this.intersectsTriangle(r,l))return(o||a)&&(o&&l.getCenter(o),a&&l.getCenter(a)),0;let c=1/0;for(let e=0;e<3;e++){let i;const s=n[e],l=r[s];this.closestPointToPoint(l,t),i=l.distanceToSquared(t),inew gb)),this.satAxes=new Array(3).fill().map((()=>new gb)),this.satBounds=new Array(3).fill().map((()=>new aY)),this.alignedSatBounds=new Array(3).fill().map((()=>new aY)),this.needsUpdate=!1}set(t,e,n){super.set(t,e),this.matrix=n,this.needsUpdate=!0}copy(t){super.copy(t),this.matrix.copy(t.matrix),this.needsUpdate=!0}}dY.prototype.update=function(){const t=this.matrix,e=this.min,n=this.max,i=this.points;for(let s=0;s<=1;s++)for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){const a=i[1*s|2*r|4*o];a.x=s?n.x:e.x,a.y=r?n.y:e.y,a.z=o?n.z:e.z,a.applyMatrix4(t)}const s=this.satBounds,r=this.satAxes,o=i[0];for(let t=0;t<3;t++){const e=r[t],n=s[t],a=i[1<new YN)),n=new Array(12).fill().map((()=>new YN)),i=new gb,s=new gb;return function(r,o=0,a=null,l=null){if(this.needsUpdate&&this.update(),this.intersectsBox(r))return(a||l)&&(r.getCenter(s),this.closestPointToPoint(s,i),r.closestPointToPoint(i,s),a&&a.copy(i),l&&l.copy(s)),0;const c=o*o,h=r.min,u=r.max,d=this.points;let p=1/0;for(let t=0;t<8;t++){const e=d[t];s.copy(e).clamp(h,u);const n=e.distanceToSquared(s);if(nn.far?null:t)}function wY(t,e,n,i){const s=t.a,r=t.b,o=t.c;let a=e,l=e+1,c=e+2;n&&(a=n.getX(e),l=n.getX(e+1),c=n.getX(e+2)),s.x=i.getX(a),s.y=i.getY(a),s.z=i.getZ(a),r.x=i.getX(l),r.y=i.getY(l),r.z=i.getZ(l),o.x=i.getX(c),o.y=i.getY(c),o.z=i.getZ(c)}function TY(t,e,n,i,s,r,o){const a=n.index,l=n.attributes.position;for(let n=t,c=e+t;n=0;let c,h;l?(c=CY(t),h=NY(t,a)):(c=NY(t,a),h=CY(t));const u=BY(c,r,i,OY)?IY(c,e,n,i):null;if(u){const t=u.point[o];if(l?t<=r[h+s]:t>=r[h+s+3])return u}const d=BY(h,r,i,OY)?IY(h,e,n,i):null;return u&&d?u.distance<=d.distance?u:d:u||d||null}}const FY=function(){let t,e;const n=[],i=new AY((()=>new xb));return function(...r){t=i.getPrimitive(),e=i.getPrimitive(),n.push(t,e);const o=s(...r);i.releasePrimitive(t),i.releasePrimitive(e),n.pop(),n.pop();const a=n.length;return a>0&&(e=n[a-1],t=n[a-2]),o};function s(n,i,r,o,a=null,l=0,c=0){function h(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=CY(t));return EY(t,i)}function u(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=NY(t,i));return EY(t,i)+SY(e,n)}let d=2*n,p=UY,_=GY,m=VY;if(MY(d,_)){const e=EY(n,m),i=SY(d,_);return $X(n,p,t),o(e,i,!1,c,l+n,t)}{const d=CY(n),f=NY(n,m);let g,v,y,x,b=d,w=f;if(a&&(y=t,x=e,$X(b,p,y),$X(w,p,x),g=a(y),v=a(x),vs.intersectsBox(t),intersectsTriangle:t=>{t.a.applyMatrix4(c),t.b.applyMatrix4(c),t.c.applyMatrix4(c),t.needsUpdate=!0;for(let n=3*g,i=3*(v+g);nnew uY));class s${static serialize(t,e={}){if(e.isBufferGeometry)return console.warn(\\\\\\\"MeshBVH.serialize: The arguments for the function have changed. 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See docs for new signature.');const t=new Set;n.forEach((e=>t.add(e))),e&&e.forEach((e=>t.add(e))),c$.call(this,t)}else c$.apply(this,t)},[\\\\\\\"intersectsGeometry\\\\\\\",\\\\\\\"shapecast\\\\\\\",\\\\\\\"intersectsBox\\\\\\\",\\\\\\\"intersectsSphere\\\\\\\"].forEach((t=>{const e=s$.prototype[t];s$.prototype[t]=function(...n){return(null===n[0]||n[0].isMesh)&&(n.shift(),console.warn(`MeshBVH: The function signature for \\\\\\\"${t}\\\\\\\" has changed and no longer takes Mesh. See docs for new signature.`)),e.apply(this,n)}}));const h$=new Xb,u$=new Yb,d$=vT.prototype.raycast;function p$(t,e){if(this.geometry.boundsTree){if(void 0===this.material)return;u$.copy(this.matrixWorld).invert(),h$.copy(t.ray).applyMatrix4(u$);const n=this.geometry.boundsTree;if(!0===t.firstHitOnly){const i=bY(n.raycastFirst(h$,this.material),this,t);i&&e.push(i)}else{const i=n.raycast(h$,this.material);for(let n=0,s=i.length;n{if(t===i||e)return r++,!0;s&&r++}),n);let o=0;const a=new Float32Array(24*r);let l,c;e.traverse(((t,e,n)=>{const r=t===i||e;if(r||s){$X(0,n,_$);const{min:t,max:e}=_$;for(let n=-1;n<=1;n+=2){const i=n<0?t.x:e.x;for(let n=-1;n<=1;n+=2){const s=n<0?t.y:e.y;for(let n=-1;n<=1;n+=2){const r=n<0?t.z:e.z;a[o+0]=i,a[o+1]=s,a[o+2]=r,o+=3}}}return r}}),n),c=this.displayEdges?new Uint8Array([0,4,1,5,2,6,3,7,0,2,1,3,4,6,5,7,0,1,2,3,4,5,6,7]):new Uint8Array([0,1,2,2,1,3,4,6,5,6,7,5,1,4,5,0,4,1,2,3,6,3,7,6,0,2,4,2,6,4,1,5,3,3,5,7]),l=a.length>65535?new Uint32Array(c.length*r):new Uint16Array(c.length*r);const h=c.length;for(let t=0;te;)this._roots.pop();for(let t=0;t=this._roots.length){const e=new m$(this.mesh,this.edgeMaterial,this.depth,t);this.add(e),this._roots.push(e)}const e=this._roots[t];e.depth=this.depth,e.mesh=this.mesh,e.displayParents=this.displayParents,e.displayEdges=this.displayEdges,e.material=this.displayEdges?this.edgeMaterial:this.meshMaterial,e.update()}}updateMatrixWorld(...t){this.position.copy(this.mesh.position),this.rotation.copy(this.mesh.rotation),this.scale.copy(this.mesh.scale),super.updateMatrixWorld(...t)}copy(t){this.depth=t.depth,this.mesh=t.mesh}clone(){return new f$(this.mesh,this.depth)}dispose(){this.edgeMaterial.dispose(),this.meshMaterial.dispose();const t=this.children;for(let e=0,n=t.length;e{const e=t;if(e.isMesh){const t=e.geometry;for(const e in t.attributes)\\\\\\\"position\\\\\\\"!==e&&t.deleteAttribute(e);n.push(e)}}))}const i=this._makeCompact(n);if(i){i.matrixAutoUpdate=!1,i.raycast=p$;const t=new s$(i.geometry,{verbose:!1});return i.geometry.boundsTree=t,this.createCoreGroupFromObjects([i])}return this.createCoreGroupFromObjects([])}_makeCompact(t){var e,n;const i=[];let s;for(let e of t){s=s||e.material;const t=e.geometry;t.applyMatrix4(e.matrix),i.push(t)}try{const t=_r.mergeGeometries(i);if(t){return this.createObject(t,Cs.MESH,s)}null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(n=this.states)||void 0===n||n.error.set(t.message)}}}g$.DEFAULT_PARAMS={},g$.INPUT_CLONED_STATE=Ki.ALWAYS;const v$=new class extends la{};class y$ extends nV{constructor(){super(...arguments),this.paramsConfig=v$}static type(){return\\\\\\\"BVH\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create BVH 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super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(C$.geometry=this.geometry,C$.material=this.material,void 0!==C$.material)for(let s=0;st.far||e.push({distance:a,point:O$.clone(),uv:Ks.a.getUV(O$,B$,z$,k$,U$,G$,V$,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function j$(t,e,n,i,s,r){I$.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(F$.x=r*I$.x-s*I$.y,F$.y=s*I$.x+r*I$.y):F$.copy(I$),t.copy(e),t.x+=F$.x,t.y+=F$.y,t.applyMatrix4(D$)}H$.prototype.isSprite=!0;var W$=n(92),q$=n(81),X$=n(46);class Y${constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let 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e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,s=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*s,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*s,e[6]=.315392*(3*s*s-1),e[7]=1.092548*n*s,e[8]=.546274*(n*n-i*i)}}Y$.prototype.isSphericalHarmonics3=!0;class $$ extends sv.a{constructor(t=new Y$,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}$$.prototype.isLightProbe=!0;var J$=n(63),Z$=n(43);class Q$ extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}Q$.prototype.isInstancedBufferGeometry=!0;class K$ extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const s=t.interleavedBuffers[i],r=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],s=new Uint32Array(i).buffer;return n[e]=s,s}(t,s.buffer),o=Object(It.c)(s.type,r),a=new lr.a(o,s.stride);return a.uuid=s.uuid,e[i]=a,a}const s=t.isInstancedBufferGeometry?new Q$:new S.a,r=t.data.index;if(void 0!==r){const t=Object(It.c)(r.type,r.array);s.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let r;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);r=new cr.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(It.c)(n.type,n.array);r=new(n.isInstancedBufferAttribute?A$:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(r.name=n.name),void 0!==n.usage&&r.setUsage(n.usage),void 0!==n.updateRange&&(r.updateRange.offset=n.updateRange.offset,r.updateRange.count=n.updateRange.count),s.setAttribute(e,r)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],r=[];for(let e=0,s=n.length;eNumber.EPSILON){const u=Math.sqrt(h),p=Math.sqrt(l*l+c*c),_=e.x-a/u,m=e.y+o/u,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,s=m+a*f-t.y;const g=i*i+s*s;if(g<=2)return new d.a(i,s);r=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new d.a(i/r,s/r)}const R=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);s=new J$.a(n),s.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new J$.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{SJ.PARAM_CALLBACK_reset(t,e)}})}};class SJ extends nV{constructor(){super(...arguments),this.paramsConfig=EJ}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new wJ,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const CJ=[is.ORTHOGRAPHIC,is.PERSPECTIVE],NJ={direction:new p.a(0,1,0)},LJ=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],OJ=new p.a(0,0,1);const PJ=new class extends la{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:CJ}}),this.direction=aa.VECTOR3(NJ.direction),this.offset=aa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=aa.BOOLEAN(!0),this.stepSize=aa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{RJ.PARAM_CALLBACK_update(t)}})}};class RJ extends nV{constructor(){super(...arguments),this.paramsConfig=PJ,this._plane=new Y.a,this._raycaster=new WL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new uU,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(ts.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!CJ.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of LJ){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),s=this._planeCorners[0].distanceTo(this._planeCorners[1]),r=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(s,r)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,OJ,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class IJ extends QG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),s=new Array(3*i.length);s.fill(0);for(let t=0;t0&&(r.push(t-1),r.push(t));r.push(e-1),r.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r),a}}const zJ=new p.a(0,0,1);class kJ extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=BJ.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e,Cs.LINE_SEGMENTS)}_create_disk(t){const e=new tJ(t.radius,t.segments);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e)}}kJ.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const UJ=kJ.DEFAULT_PARAMS;const GJ=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(UJ.radius),this.segments=aa.INTEGER(UJ.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=aa.BOOLEAN(UJ.open),this.arcAngle=aa.FLOAT(UJ.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=aa.VECTOR3(UJ.direction)}};class VJ extends nV{constructor(){super(...arguments),this.paramsConfig=GJ}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new kJ(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var HJ;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(HJ||(HJ={}));const jJ=[HJ.SEGMENTS_COUNT,HJ.SEGMENTS_LENGTH];var WJ;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(WJ||(WJ={}));const qJ=[WJ.ABC,WJ.ACB,WJ.AB,WJ.AC,WJ.BC];class XJ{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let s=0;for(s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new S.a;if(r.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),r.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&r.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=r}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case HJ.SEGMENTS_COUNT:return this.params.segmentsCount+1;case HJ.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}ls.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case WJ.ABC:case WJ.ACB:case WJ.AB:case WJ.AC:return this.x.copy(this.an);case WJ.BC:return this.x.copy(this.bn)}ls.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case WJ.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case WJ.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case WJ.AB:return void(this.angle=this.angle_ab);case WJ.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case WJ.BC:return void(this.angle=this.angle_bc)}ls.unreachable(t)}}const YJ=new class extends la{constructor(){super(...arguments),this.arc=aa.BOOLEAN(1),this.pointsCountMode=aa.INTEGER(jJ.indexOf(HJ.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:jJ.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=aa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=aa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=aa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=aa.INTEGER(qJ.indexOf(WJ.ABC),{visibleIf:{arc:1,full:0},menu:{entries:qJ.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=aa.BOOLEAN(1),this.addIdnAttribute=aa.BOOLEAN(1),this.center=aa.BOOLEAN(0)}};class $J extends nV{constructor(){super(...arguments),this.paramsConfig=YJ,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new XJ({arc:this.pv.arc,center:this.pv.center,pointsCountMode:jJ[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:qJ[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Cs.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Cs.POINTS)),this.setObjects(n)}}class JJ extends QG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}JJ.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const ZJ=new D.a(1,1,1),QJ=\\\\\\\"color\\\\\\\",KJ=JJ.DEFAULT_PARAMS;const tZ=new class extends la{constructor(){super(...arguments),this.fromAttribute=aa.BOOLEAN(KJ.fromAttribute),this.attribName=aa.STRING(KJ.attribName,{visibleIf:{fromAttribute:1}}),this.color=aa.COLOR(KJ.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=aa.BOOLEAN(KJ.asHsv,{visibleIf:{fromAttribute:0}})}};class eZ extends nV{constructor(){super(...arguments),this.paramsConfig=tZ,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(QJ).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,ZJ);const n=e.points(),i=e.attribSize(this.pv.attribName),s=e.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(QJ).array;switch(i){case 1:for(let t=0;t{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=r;return a}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(rZ.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):rZ.PSCALE;this._point_scale.multiplyScalar(i);const s=this._matrices.scale;s.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const r=this._matrices.translate;if(r.makeTranslation(n.x,n.y,n.z),e.multiply(r),this._do_rotate_matrices){const n=this._matrices.rotate,i=rZ.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(rZ.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(s)}static create_instance_buffer_geo(t,e,n){const i=e.points(),s=new Q$;s.copy(t),s.instanceCount=1/0;const r=i.length,o=new Float32Array(3*r),a=new Float32Array(3*r),l=new Float32Array(3*r),c=new Float32Array(4*r),h=e.hasAttrib(lZ),u=new p.a(0,0,0),d=new ah.a,_=new p.a(1,1,1),f=new uZ(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(u,d,_),u.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(h?t.attribValue(lZ,this._point_color):oZ).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*r);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):aZ).toArray(t,i)})),s.setAttribute(cZ.UV,new A$(t,2))}s.setAttribute(cZ.POSITION,new A$(o,3)),s.setAttribute(cZ.SCALE,new A$(l,3)),s.setAttribute(cZ.ORIENTATION,new A$(c,4)),s.setAttribute(cZ.COLOR,new A$(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(r*n);i.forEach(((e,i)=>{const s=e.attribValue(t);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(t,new A$(o,n))}));return new _r(s).markAsInstance(),s}}uZ._point_color=new p.a,uZ._point_uv=new d.a;class dZ extends sh{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}(hZ||(hZ={}));const pZ=[hZ.OBJECT,hZ.GEOMETRY],_Z=[{name:\\\\\\\"object\\\\\\\",value:hZ.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:hZ.GEOMETRY}];const mZ=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=aa.BOOLEAN(0),this.transformMode=aa.INTEGER(0,{menu:{entries:_Z}}),this.copyAttributes=aa.BOOLEAN(0),this.attributesToCopy=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=aa.BOOLEAN(0)}};class fZ extends nV{constructor(){super(...arguments),this.paramsConfig=mZ,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.ALWAYS,Ki.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new uZ(e).matrices();const s=new p.a,r=new ah.a,o=new p.a;i[0].decompose(s,r,o),this._attribute_names_to_copy=os.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=yr.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const s=e.attribValue(n);new yr(t,i).addAttribute(n,s)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new dZ(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const gZ=\\\\\\\"id\\\\\\\",vZ=\\\\\\\"class\\\\\\\",yZ=\\\\\\\"html\\\\\\\";class xZ extends QG{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(gZ):e.className,s=e.useClassAttrib?t.attribValue(vZ):e.className,r=e.useHtmlAttrib?t.attribValue(yZ):e.html,o=xZ.create_css_object({id:n,className:s,html:r}),a=o.element;if(e.copyAttributes){const n=os.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return xZ.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new iq(e);return n.matrixAutoUpdate=!1,n}}xZ.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},xZ.INPUT_CLONED_STATE=Ki.FROM_NODE;const bZ=xZ.DEFAULT_PARAMS;const wZ=new class extends la{constructor(){super(...arguments),this.useIdAttrib=aa.BOOLEAN(bZ.useIdAttrib),this.id=aa.STRING(bZ.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=aa.BOOLEAN(bZ.useClassAttrib),this.className=aa.STRING(bZ.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=aa.BOOLEAN(bZ.useHtmlAttrib),this.html=aa.STRING(bZ.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=aa.BOOLEAN(bZ.copyAttributes),this.attributesToCopy=aa.STRING(bZ.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class TZ extends nV{constructor(){super(...arguments),this.paramsConfig=wZ}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new xZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class AZ{constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Bs.STRING:Bs.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class MZ{constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new _r(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=os.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=qs.remapName(n);let r=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Bs.STRING)if(this._options.doConvert&&os.matchesOneMask(n,i)){const e=r.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(s,new C.c(e,a))}else{const t=qs.arrayToIndexedArrays(r);e.setIndexedAttribute(s,t.values,t.indices)}else{const e=r;t.setAttribute(s,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=os.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const s=[n,t].join(\\\\\\\":\\\\\\\"),r=i[n];os.matchesOneMask(s,e)||(this._attribute_datas_by_name[s]=AZ.from_value(r))}else os.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=AZ.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const EZ=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const SZ=new class extends la{constructor(){super(...arguments),this.data=aa.STRING(EZ)}};class CZ extends nV{constructor(){super(...arguments),this.paramsConfig=SZ}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new MZ;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Cs.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class NZ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new MZ(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const s=this._parser.createObject();t(s)})).catch((t=>{li.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const LZ=\\\\\\\"position\\\\\\\";class OZ{constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(OZ.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>qs.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(OZ.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=aa.STRING(IZ,{fileBrowse:{type:[Or.JSON]}}),this.jsonDataKeysPrefix=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.skipEntries=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convert=aa.BOOLEAN(0,{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convertToNumeric=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON),convert:1}}),this.readAttribNamesFromFile=aa.BOOLEAN(1,{visibleIf:{dataType:RZ.indexOf(PZ.CSV)}}),this.attribNames=aa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.CSV),readAttribNamesFromFile:0}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{DZ.PARAM_CALLBACK_reload(t,e)}})}};class DZ extends nV{constructor(){super(...arguments),this.paramsConfig=FZ}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(RZ[this.pv.dataType]){case PZ.JSON:return this._load_json();case PZ.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new NZ(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Cs.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new OZ(t),n=await e.load(this._url());n?this.setGeometry(n,Cs.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class BZ extends S.a{constructor(t,e,n,i){super();const s=[],r=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function h(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new zZ(n.clone(),i.clone()))}function u(t,e){const n=[],s=.5*Math.abs(i.dot(e));for(let i=0;i0,u=t[i+1].position.dot(e)-s>0,p=t[i+2].position.dot(e)-s>0;switch(c=(h?1:0)+(u?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(h&&(r=t[i+1],o=t[i+2],a=d(t[i],r,e,s),l=d(t[i],o,e,s)),u){r=t[i],o=t[i+2],a=d(t[i+1],r,e,s),l=d(t[i+1],o,e,s),n.push(a),n.push(o.clone()),n.push(r.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(r=t[i],o=t[i+1],a=d(t[i+2],r,e,s),l=d(t[i+2],o,e,s)),n.push(r.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:h||(r=t[i].clone(),o=d(r,t[i+1],e,s),a=d(r,t[i+2],e,s),n.push(r),n.push(o),n.push(a)),u||(r=t[i+1].clone(),o=d(r,t[i+2],e,s),a=d(r,t[i],e,s),n.push(r),n.push(o),n.push(a)),p||(r=t[i+2].clone(),o=d(r,t[i],e,s),a=d(r,t[i+1],e,s),n.push(r),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const s=t.position.dot(n)-i,r=s/(s-(e.position.dot(n)-i));return new zZ(new p.a(t.position.x+r*(e.position.x-t.position.x),t.position.y+r*(e.position.y-t.position.y),t.position.z+r*(e.position.z-t.position.z)),new p.a(t.normal.x+r*(e.normal.x-t.normal.x),t.normal.y+r*(e.normal.y-t.normal.y),t.normal.z+r*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class WZ{constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,s)=>{n==e&&(t[i]=s,i++)})),t}}}var qZ;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(qZ||(qZ={}));const XZ=[qZ.EQUAL,qZ.LESS_THAN,qZ.EQUAL_OR_LESS_THAN,qZ.EQUAL_OR_GREATER_THAN,qZ.GREATER_THAN,qZ.DIFFERENT],YZ={[qZ.EQUAL]:(t,e)=>t==e,[qZ.LESS_THAN]:(t,e)=>tt<=e,[qZ.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[qZ.GREATER_THAN]:(t,e)=>t>e,[qZ.DIFFERENT]:(t,e)=>t!=e},$Z=XZ.map(((t,e)=>({name:t,value:e})));class JZ{constructor(t){this.node=t}evalForEntities(t){const e=zs[this.node.pv.attribType];switch(e){case Bs.NUMERIC:return void this._eval_for_numeric(t);case Bs.STRING:return void this._eval_for_string(t)}ls.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Gs[this.node.pv.attribSize-1];switch(e){case Us.FLOAT:return this._eval_for_points_numeric_float(t);case Us.VECTOR2:return this._eval_for_points_numeric_vector2(t);case Us.VECTOR3:return this._eval_for_points_numeric_vector3(t);case Us.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ls.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=XZ[this.node.pv.attribComparisonOperator],r=YZ[s];for(let s of t)i=s.attribValue(e),r(i,n)&&this.node.entitySelectionHelper.select(s)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}}class ZZ{constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class QZ{constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new Ay.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class KZ{constructor(t){this.node=t}eval_for_objects(t){const e=Os[this.node.pv.objectType];for(let n of t){Ls(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class tQ{constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const eQ=new p.a(0,1,0),nQ=new p.a(0,-1,0);class iQ{constructor(t){this.node=t,this._matDoubleSideTmpSetter=new tQ,this._point_position=new p.a,this._raycaster=new WL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const s=n.geometry;s.computeBoundingBox();const r=s.boundingBox;for(let e of t)e.getPosition(this._point_position),r.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,eQ)&&this._isPositionInObject(this._point_position,i,nQ)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const s=this._raycaster.intersectObject(e,!1,this._intersections);if(!s)return!1;if(0==s.length)return!1;const r=null===(i=s[0].face)||void 0===i?void 0:i.normal;if(!r)return!1;return this._raycaster.ray.direction.dot(r)>=0}}const sQ=new class extends la{constructor(){super(...arguments),this.class=aa.INTEGER(Fs.indexOf(Is.VERTEX),{menu:{entries:Ds}}),this.invert=aa.BOOLEAN(0),this.byObjectType=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}}),this.objectType=aa.INTEGER(Os.indexOf(Cs.MESH),{menu:{entries:Ps},visibleIf:{class:Fs.indexOf(Is.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=aa.BOOLEAN(0),this.expression=aa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=aa.BOOLEAN(0),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},visibleIf:{byAttrib:1}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=aa.INTEGER(1,{range:Vs,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC)}}),this.attribComparisonOperator=aa.INTEGER(XZ.indexOf(qZ.EQUAL),{menu:{entries:$Z},visibleIf:{byAttrib:!0,attribType:zs.indexOf(Bs.NUMERIC),attribSize:Us.FLOAT}}),this.attribValue1=aa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:1}}),this.attribValue2=aa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:2}}),this.attribValue3=aa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:3}}),this.attribValue4=aa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:4}}),this.attribString=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.STRING)},separatorAfter:!0}),this.byBbox=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.bboxSize=aa.VECTOR3([1,1,1],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0}}),this.bboxCenter=aa.VECTOR3([0,0,0],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.keepPoints=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}})}};class rQ extends nV{constructor(){super(...arguments),this.paramsConfig=sQ,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new WZ(this),this.byExpressionHelper=new ZZ(this),this.byAttributeHelper=new JZ(this),this.byObjectTypeHelper=new KZ(this),this.byBboxHelper=new QZ(this),this.byBoundingObjectHelper=new iQ(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Is.VERTEX:await this._eval_for_points(e,n);break;case Is.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,s=[];for(let t=0;t0){const e=_r.geometryFromPoints(a,Ls(t.constructor));e&&(t.geometry=e,s.push(t))}}}this.setObjects(s)}_point_object(t){const e=t.points(),n=_r.geometryFromPoints(e,Cs.POINTS);if(n)return this.createObject(n,Cs.POINTS)}}const oQ=new class extends la{constructor(){super(...arguments),this.start=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=aa.BOOLEAN(0),this.count=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class aQ extends nV{constructor(){super(...arguments),this.paramsConfig=oQ}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class lQ{constructor(){this.pluginCallbacks=[],this.register((function(t){return new DQ(t)})),this.register((function(t){return new BQ(t)})),this.register((function(t){return new zQ(t)})),this.register((function(t){return new kQ(t)})),this.register((function(t){return new UQ(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new FQ,s=[];for(let t=0,e=this.pluginCallbacks.length;t0&&(this.options.trs=!0),this.processInput(t);const i=this;Promise.all(this.pending).then((function(){const t=i.buffers,n=i.json,s=i.options,r=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(r);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===s.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=RQ(t.result),s=new DataView(new ArrayBuffer(8));s.setUint32(0,i.byteLength,!0),s.setUint32(4,5130562,!0);const r=RQ(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let n=0,i=t.length;n255?32:i}return e.buffer}(JSON.stringify(n)),32),o=new DataView(new ArrayBuffer(8));o.setUint32(0,r.byteLength,!0),o.setUint32(4,1313821514,!0);const a=new ArrayBuffer(12),l=new DataView(a);l.setUint32(0,1179937895,!0),l.setUint32(4,2,!0);const c=12+o.byteLength+r.byteLength+s.byteLength+i.byteLength;l.setUint32(8,c,!0);const h=new Blob([a,o,r,s,i],{type:\\\\\\\"application/octet-stream\\\\\\\"}),u=new window.FileReader;u.readAsArrayBuffer(h),u.onloadend=function(){e(u.result)}}}else if(n.buffers&&n.buffers.length>0){const t=new window.FileReader;t.readAsDataURL(o),t.onloadend=function(){const i=t.result;n.buffers[0].uri=i,e(n)}}else e(n)}))}serializeUserData(t,e){if(0===Object.keys(t.userData).length)return;const n=this.options,i=this.extensionsUsed;try{const s=JSON.parse(JSON.stringify(t.userData));if(n.includeCustomExtensions&&s.gltfExtensions){void 0===e.extensions&&(e.extensions={});for(const t in s.gltfExtensions)e.extensions[t]=s.gltfExtensions[t],i[t]=!0;delete s.gltfExtensions}Object.keys(s).length>0&&(e.extras=s)}catch(e){console.warn(\\\\\\\"THREE.GLTFExporter: userData of '\\\\\\\"+t.name+\\\\\\\"' won't be serialized because of JSON.stringify error - \\\\\\\"+e.message)}}getUID(t){return this.uids.has(t)||this.uids.set(t,this.uid++),this.uids.get(t)}isNormalizedNormalAttribute(t){if(this.cache.attributesNormalized.has(t))return!1;const e=new gb;for(let n=0,i=t.count;n5e-4)return!1;return!0}createNormalizedNormalAttribute(t){const e=this.cache;if(e.attributesNormalized.has(t))return e.attributesNormalized.get(t);const n=t.clone(),i=new gb;for(let t=0,e=n.count;t4?i=t.array[s*t.itemSize+n]:0===n?i=t.getX(s):1===n?i=t.getY(s):2===n?i=t.getZ(s):3===n&&(i=t.getW(s)),e===fQ?l.setFloat32(c,i,!0):e===gQ?l.setUint32(c,i,!0):e===mQ?l.setUint16(c,i,!0):e===_Q&&l.setUint8(c,i),c+=o}const h={buffer:this.processBuffer(l.buffer),byteOffset:this.byteOffset,byteLength:a};void 0!==s&&(h.target=s),s===vQ&&(h.byteStride=t.itemSize*o),this.byteOffset+=a,r.bufferViews.push(h);return{id:r.bufferViews.length-1,byteLength:0}}processBufferViewImage(t){const e=this,n=e.json;return n.bufferViews||(n.bufferViews=[]),new Promise((function(i){const s=new window.FileReader;s.readAsArrayBuffer(t),s.onloadend=function(){const t=RQ(s.result),r={buffer:e.processBuffer(t),byteOffset:e.byteOffset,byteLength:t.byteLength};e.byteOffset+=t.byteLength,i(n.bufferViews.push(r)-1)}}))}processAccessor(t,e,n,i){const s=this.options,r=this.json;let o;if(t.array.constructor===Float32Array)o=fQ;else if(t.array.constructor===Uint32Array)o=gQ;else if(t.array.constructor===Uint16Array)o=mQ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=_Q}if(void 0===n&&(n=0),void 0===i&&(i=t.count),s.truncateDrawRange&&void 0!==e&&null===e.index){const s=n+i,r=e.drawRange.count===1/0?t.count:e.drawRange.start+e.drawRange.count;n=Math.max(n,e.drawRange.start),(i=Math.min(s,r)-n)<0&&(i=0)}if(0===i)return null;const a=function(t,e,n){const i={min:new Array(t.itemSize).fill(Number.POSITIVE_INFINITY),max:new Array(t.itemSize).fill(Number.NEGATIVE_INFINITY)};for(let s=e;s4?n=t.array[s*t.itemSize+e]:0===e?n=t.getX(s):1===e?n=t.getY(s):2===e?n=t.getZ(s):3===e&&(n=t.getW(s)),i.min[e]=Math.min(i.min[e],n),i.max[e]=Math.max(i.max[e],n)}return i}(t,n,i);let l;void 0!==e&&(l=t===e.index?yQ:vQ);const c=this.processBufferView(t,o,n,i,l),h={bufferView:c.id,byteOffset:c.byteOffset,componentType:o,count:i,max:a.max,min:a.min,type:{1:\\\\\\\"SCALAR\\\\\\\",2:\\\\\\\"VEC2\\\\\\\",3:\\\\\\\"VEC3\\\\\\\",4:\\\\\\\"VEC4\\\\\\\",16:\\\\\\\"MAT4\\\\\\\"}[t.itemSize]};return!0===t.normalized&&(h.normalized=!0),r.accessors||(r.accessors=[]),r.accessors.push(h)-1}processImage(t,e,n){const i=this,s=i.cache,r=i.json,o=i.options,a=i.pending;s.images.has(t)||s.images.set(t,{});const l=s.images.get(t),c=e===Ex?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",h=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[h])return l[h];r.images||(r.images=[]);const u={mimeType:c};if(o.embedImages){const s=IQ=IQ||document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.min(t.width,o.maxTextureSize),s.height=Math.min(t.height,o.maxTextureSize);const r=s.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(r.translate(0,s.height),r.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)r.drawImage(t,0,0,s.width,s.height);else{e!==Ex&&e!==Mx&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===Ex)for(let e=0;e1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const r=n.materials.push(i)-1;return e.materials.set(t,r),r}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e0){const n=[],i=[],s={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)s[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===r.groups.length)return null;const m=_?t.material:[t.material],f=_?r.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=h),null!==r.index){let i=this.getUID(r.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(r.index,r,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(s,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:ib.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const s=(t=lQ.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,r=[],o=[];for(let t=0;t0){const e=[];for(let i=0,s=t.children.length;i0&&(s.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,s)}));const r=e.nodes.push(s)-1;return i.set(t,r),r}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const s=[];for(let e=0,i=t.children.length;e0&&(i.nodes=s),this.serializeUserData(t,i)}processObjects(t){const e=new CE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(r.range=t.distance)):t.isSpotLight&&(r.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(r.range=t.distance),r.spot={},r.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,r.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),s[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},s[this.name]=!0);const o=i.extensions[this.name].lights;o.push(r),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class BQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class zQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,s={};e.pbrMetallicRoughness.baseColorFactor&&(s.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const r=[1,1,1];if(t.specular.toArray(r,0),s.specularFactor=r,s.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(s.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),s.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class kQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),s.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class UQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),s.thicknessTexture=e}s.attenuationDistance=t.attenuationDistance,s.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}function GQ(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}lQ.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),s=new t.TimeBufferType(t.times.length+1),r=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){s[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){s.set(t.times.slice(0,l+1),0),s[l+1]=e,s.set(t.times.slice(l+1),l+2),r.set(t.values.slice(0,(l+1)*i),0),r.set(o.evaluate(e),(l+1)*i),r.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=s,t.values=r,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},s=t.tracks;for(let t=0;t{HQ.PARAM_CALLBACK_export(t)}})}};class HQ extends nV{constructor(){super(...arguments),this.paramsConfig=VQ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new gs;for(let t of n)i.add(t);(new lQ).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",GQ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){GQ(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const jQ=new class extends la{constructor(){super(...arguments),this.makeFacesUnique=aa.BOOLEAN(0),this.addFaceCenterAttribute=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=aa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class WQ extends nV{constructor(){super(...arguments),this.paramsConfig=jQ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),s=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],r=n.itemSize,o=new Float32Array(s*r);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e{const a=t.object(),l=t.coreGeometry();if(a.isMesh&&l){i=l.faces(),l.hasAttrib(e)||l.addNumericAttrib(e,3,-1);for(let t=0;t{const n=t.object(),i=t.coreGeometry();if(n.isMesh&&i){const t=i.faces();i.hasAttrib(e)||i.addNumericAttrib(e,1,-1);for(let n=0;n{const c=t.object(),h=t.coreGeometry();if(c.isMesh&&h){r=h.faces(),h.hasAttrib(e)||h.addNumericAttrib(e,3,-1);for(let t=0;t{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==qQ.JSON&&this._options.format==qQ.AUTO)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new wJ(this.loadingManager).parse(n,(e=>{$Q.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{$Q.decrement_in_progress_loads_count(),e(t)}));else{const s=await this._loaderForFormat();if(s)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(e=>{this.on_load_success(e).then((e=>{$Q.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{li.warn(\\\\\\\"error loading\\\\\\\",n,t),$Q.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==qQ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case YQ.PDB:return this.on_load_succes_pdb(t);case YQ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case YQ.DRC:return this.on_load_succes_drc(i);default:return[new B.a(i)]}const s=t;if(null!=s.scene)switch(e){case YQ.GLTF:case YQ.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=t;if(r.geometryAtoms||r.geometryBonds)switch(e){case YQ.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(t){return[new B.a(t,$Q._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new vs.a(t.geometryAtoms,$Q._default_mat_point),new As.a(t.geometryBonds,$Q._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case qQ.AUTO:return this.moduleNamesFromExt(e);case qQ.DRC:return[Hn.DRACOLoader];case qQ.FBX:return[Hn.FBXLoader];case qQ.JSON:return[];case qQ.GLTF:return[Hn.GLTFLoader];case qQ.GLTF_WITH_DRACO:return[Hn.GLTFLoader,Hn.DRACOLoader];case qQ.OBJ:return[Hn.OBJLoader];case qQ.PDB:return[Hn.PDBLoader];case qQ.PLY:return[Hn.PLYLoader];case qQ.STL:return[Hn.STLLoader]}ls.unreachable(t)}static moduleNamesFromExt(t){switch(t){case YQ.DRC:return[Hn.DRACOLoader];case YQ.FBX:return[Hn.FBXLoader];case YQ.GLTF:return[Hn.GLTFLoader];case YQ.GLB:return[Hn.GLTFLoader,Hn.DRACOLoader];case YQ.OBJ:return[Hn.OBJLoader];case YQ.PDB:return[Hn.PDBLoader];case YQ.PLY:return[Hn.PLYLoader];case YQ.STL:return[Hn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case qQ.AUTO:return this._loaderForExt();case qQ.DRC:return this.loader_for_drc(this._node);case qQ.FBX:return this.loader_for_fbx();case qQ.JSON:return;case qQ.GLTF:return this.loader_for_gltf();case qQ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case qQ.OBJ:return this.loader_for_obj();case qQ.PDB:return this.loader_for_pdb();case qQ.PLY:return this.loader_for_ply();case qQ.STL:return this.loader_for_stl()}ls.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case YQ.DRC:return this.loader_for_drc(this._node);case YQ.FBX:return this.loader_for_fbx();case YQ.GLTF:return this.loader_for_gltf();case YQ.GLB:return this.loader_for_glb(this._node);case YQ.OBJ:return this.loader_for_obj();case YQ.PDB:return this.loader_for_pdb();case YQ.PLY:return this.loader_for_ply();case YQ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=li.modulesRegister.module(Hn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=li.modulesRegister.module(Hn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=li.modulesRegister.module(Hn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.DRACOPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return $Q.loader_for_drc(t)}static async loader_for_glb(t){const e=li.modulesRegister.module(Hn.GLTFLoader),n=li.modulesRegister.module(Hn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=li.libs.root(),s=li.libs.DRACOGLTFPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return $Q.loader_for_glb(t)}loader_for_obj(){const t=li.modulesRegister.module(Hn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=li.modulesRegister.module(Hn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=li.modulesRegister.module(Hn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=li.modulesRegister.module(Hn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$Q._default_mat_mesh=new ws.a,$Q._default_mat_point=new xs.a,$Q._default_mat_line=new Ts.a,$Q.MAX_CONCURRENT_LOADS_COUNT=$Q._init_max_concurrent_loads_count(),$Q.CONCURRENT_LOADS_DELAY=$Q._init_concurrent_loads_delay(),$Q.in_progress_loads_count=0,$Q._queue=[];const JQ=`${Gg}/models/wolf.obj`;class ZQ extends QG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new $Q({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}ZQ.DEFAULT_PARAMS={url:JQ,format:qQ.AUTO};const QQ=ZQ.DEFAULT_PARAMS;const KQ=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(QQ.url,{fileBrowse:{type:[Or.GEOMETRY]}}),this.format=aa.STRING(QQ.format,{menuString:{entries:XQ.map((t=>({name:t,value:t})))}}),this.reload=aa.BUTTON(null,{callback:t=>{tK.PARAM_CALLBACK_reload(t)}})}};class tK extends nV{constructor(){super(...arguments),this.paramsConfig=KQ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return $Q.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new ZQ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const eK=new class extends la{constructor(){super(...arguments),this.dist=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class nK extends nV{constructor(){super(...arguments),this.paramsConfig=eK}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,s={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");s[n]=s[n]||[],s[n].push(t)}const r=[];if(Object.keys(s).forEach((t=>{r.push(s[t][0])})),e.geometry.dispose(),r.length>0){const t=_r.geometryFromPoints(r,Ls(e.constructor));return t&&(e.geometry=t),e}}}class iK{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,s=Math.floor(this._param_size.x/n),r=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==r||r==s-1?0==r?a.push([r+1+(t-1)*s,r+(t-1)*s,r+t*s]):a.push([r+t*s,r+(t-1)*s,r-1+t*s]):(a.push([r+t*s,r+(t-1)*s,r-1+t*s]),a.push([r+t*s,r+1+(t-1)*s,r+(t-1)*s])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const sK=new p.a(0,1,0);const rK=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2([1,1]),this.hexagonRadius=aa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=aa.VECTOR3([0,1,0]),this.pointsOnly=aa.BOOLEAN(0)}};class oK extends nV{constructor(){super(...arguments),this.paramsConfig=rK,this._core_transform=new uU}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new iK(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,sK,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Cs.POINTS):this.setGeometry(t)}else this.setObjects([])}}var aK;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(aK||(aK={}));const lK=[aK.ADD_PARENT,aK.REMOVE_PARENT,aK.ADD_CHILD];class cK extends QG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=lK[e.mode];switch(i){case aK.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ls.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new Fn.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=aa.INTEGER(uK.levels,{range:[0,5],visibleIf:[{mode:lK.indexOf(aK.ADD_PARENT)},{mode:lK.indexOf(aK.REMOVE_PARENT)}]}),this.objectMask=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}}),this.debugObjectMask=aa.BOOLEAN(0,{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}})}};class pK extends nV{constructor(){super(...arguments),this.paramsConfig=dK}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(cK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=lK[this.pv.mode];return hK.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new cK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const _K=new class extends la{constructor(){super(...arguments),this.texture=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP}}),this.mult=aa.FLOAT(1)}};class mK extends nV{constructor(){super(...arguments),this.paramsConfig=_K}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==ts.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:s,resx:r,resy:o}=i,a=s.length/(r*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,h=l.getAttribute(\\\\\\\"uv\\\\\\\"),u=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=h.array,p=u.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((r-1)*f),y=Math.floor((o-1)*(1-g)),x=y*r+v,b=s[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Pf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function fK(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class gK extends S.a{constructor(t,e,n,i,s){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const r=[],o=[],a=new Map;function l(t,e,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=t.clone().lerp(n,i/s),a=e.clone().lerp(n,i/s),l=s-i;for(let t=0;t<=l;t++)r[i][t]=0===t&&i===s?o:o.clone().lerp(a,t/l)}for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const FK=new class extends la{constructor(){super(...arguments),this.length=aa.FLOAT(1,{range:[0,10]}),this.pointsCount=aa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=aa.VECTOR3([0,0,0]),this.direction=aa.VECTOR3([0,1,0])}};class DK extends nV{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s)}const s=new S.a;s.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),s.setIndex(n),this.setGeometry(s,Cs.LINE_SEGMENTS)}}const BK=new class extends la{constructor(){super(...arguments),this.distance0=aa.FLOAT(1),this.distance1=aa.FLOAT(2),this.autoUpdate=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{zK.PARAM_CALLBACK_update(t)}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class zK extends nV{constructor(){super(...arguments),this.paramsConfig=BK,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}_create_LOD(){const t=new Ss;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${r.states.error.message()}')`)}else null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!yr.isInGroup(n.group,t))return;const i=n.cloneMat?gr.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const s=t;this._old_mat_by_old_new_id.set(i.uuid,s.material),s.material=i,gr.apply_render_hook(t,i),gr.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}kK.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new yi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},kK.INPUT_CLONED_STATE=Ki.FROM_NODE;const UK=kK.DEFAULT_PARAMS;const GK=new class extends la{constructor(){super(...arguments),this.group=aa.STRING(UK.group),this.assignMat=aa.BOOLEAN(UK.assignMat),this.material=aa.NODE_PATH(UK.material.path(),{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=aa.BOOLEAN(UK.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=aa.BOOLEAN(UK.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=aa.BOOLEAN(UK.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=aa.BOOLEAN(UK.swapCurrentTex),this.texSrc0=aa.STRING(UK.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=aa.STRING(UK.texDest0,{visibleIf:{swapCurrentTex:1}})}};class VK extends nV{constructor(){super(...arguments),this.paramsConfig=GK}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(kK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new kK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class HK extends QG{static type(){return\\\\\\\"merge\\\\\\\"}cook(t,e){let n=[];for(let i of t)if(i){const t=i.objects();if(e.compact)for(let e of t)e.traverse((t=>{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._makeCompact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_makeCompact(t){const e=new Map,n=new Map,i=[];for(let s of t)s.traverse((t=>{if(t instanceof Fn.a)return;const s=t;if(s.geometry){const t=Ls(s.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,s.material),h.pushOnArrayAtEntry(n,t,s)}}}));const s=[];return i.forEach((t=>{var i,r;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const r=_r.mergeGeometries(n);if(r){const n=e.get(t),i=this.createObject(r,t,n);s.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(r=this.states)||void 0===r||r.error.set(t.message)}}})),s}}HK.DEFAULT_PARAMS={compact:!1},HK.INPUT_CLONED_STATE=Ki.FROM_NODE;const jK=\\\\\\\"geometry to merge\\\\\\\",WK=HK.DEFAULT_PARAMS;const qK=new class extends la{constructor(){super(...arguments),this.compact=aa.BOOLEAN(WK.compact),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{XK.PARAM_CALLBACK_setInputsCount(t)}})}};class XK extends nV{constructor(){super(...arguments),this.paramsConfig=qK}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[jK,jK,jK,jK]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(HK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new HK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}class YK extends K.a{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}YK.prototype.isImmediateRenderObject=!0;class $K extends YK{constructor(t,e,n,i){super(e);const s=this,r=new Float32Array(36),o=new Float32Array(36),a=new Float32Array(36);function l(t,e,n){return t+(e-t)*n}function c(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i+m*s.delta,r[e+1]=c,r[e+2]=h,o[e+0]=l(f[t+0],f[t+3],m),o[e+1]=l(f[t+1],f[t+4],m),o[e+2]=l(f[t+2],f[t+5],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function h(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c+m*s.delta,r[e+2]=h;const g=t+3*s.yd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function u(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c,r[e+2]=h+m*s.delta;const g=t+3*s.zd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function d(t){const e=3*t;0===s.normal_cache[e]&&(s.normal_cache[e+0]=s.field[t-1]-s.field[t+1],s.normal_cache[e+1]=s.field[t-s.yd]-s.field[t+s.yd],s.normal_cache[e+2]=s.field[t-s.zd]-s.field[t+s.zd])}function p(t,e,n,i,l,p){const m=i+1,f=i+s.yd,g=i+s.zd,v=m+s.yd,y=m+s.zd,x=i+s.yd+s.zd,b=m+s.yd+s.zd;let w=0;const T=s.field[i],A=s.field[m],M=s.field[f],E=s.field[v],S=s.field[g],C=s.field[y],N=s.field[x],L=s.field[b];T=s.maxCount-3&&(s.hasPositions=!0,s.hasNormals=!0,s.enableUvs&&(s.hasUvs=!0),s.enableColors&&(s.hasColors=!0),a(s))}function m(t,e,n){const i=new Float32Array(t.length+n);return i.set(t,0),i.set(e.slice(0,n),t.length),i}this.enableUvs=void 0!==n&&n,this.enableColors=void 0!==i&&i,this.init=function(t){this.resolution=t,this.isolation=80,this.size=t,this.size2=this.size*this.size,this.size3=this.size2*this.size,this.halfsize=this.size/2,this.delta=2/this.size,this.yd=this.size,this.zd=this.size2,this.field=new Float32Array(this.size3),this.normal_cache=new Float32Array(3*this.size3),this.palette=new Float32Array(3*this.size3),this.maxCount=4096,this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=new Float32Array(3*this.maxCount),this.normalArray=new Float32Array(3*this.maxCount),this.enableUvs&&(this.uvArray=new Float32Array(2*this.maxCount)),this.enableColors&&(this.colorArray=new Float32Array(3*this.maxCount))},this.begin=function(){this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasUvs=!1,this.hasColors=!1},this.end=function(t){if(0!==this.count){for(let t=3*this.count;tthis.size-1&&(_=this.size-1);let m=Math.floor(u-c);m<1&&(m=1);let f=Math.floor(u+c);f>this.size-1&&(f=this.size-1);let g=Math.floor(d-c);g<1&&(g=1);let v,y,x,b,w,T,A,M,E,S,C,N=Math.floor(d+c);for(N>this.size-1&&(N=this.size-1),x=p;x<_;x++)for(w=this.size2*x,M=x/this.size-n,E=M*M,y=m;y0){this.field[b+v]+=C*o;const t=Math.sqrt((v-d)*(v-d)+(y-u)*(y-u)+(x-h)*(x-h))/c,e=1-t*t*t*(t*(6*t-15)+10);this.palette[3*(b+v)+0]+=l.r*e,this.palette[3*(b+v)+1]+=l.g*e,this.palette[3*(b+v)+2]+=l.b*e}},this.addPlaneX=function(t,e){const n=this.size,i=this.yd,s=this.zd,r=this.field;let o,a,l,c,h,u,d,p=n*Math.sqrt(t/e);for(p>n&&(p=n),o=0;o0)for(a=0;an&&(_=n),a=0;a<_;a++)if(u=a/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=a*i,o=0;on&&(_=n),l=0;l<_;l++)if(u=l/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=s*l,a=0;a=i))for(let e=-1;e<=1;e+=2){const r=e+o;if(!(r<0||r>=i))for(let e=-1;e<=1;e+=2){const o=e+a;if(o<0||o>=i)continue;const u=n[s*o+i*r+l];h++,c+=t*(u-c)/h}}}e[l]=c}},this.reset=function(){for(let t=0;tu?(d=1,p=0):(d=0,p=1);const _=h-d+l,m=u-p+l,f=h-1+2*l,g=u-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-h*h-u*u;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],h,u));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let M=.5-f*f-g*g;return M<0?s=0:(M*=M,s=M*M*this.dot(this.grad3[w],f,g)),70*(n+i+s)}noise3d(t,e,n){let i,s,r,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),h=Math.floor(n+a),u=1/6,d=(l+c+h)*u,p=t-(l-d),_=e-(c-d),m=n-(h-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),M=r[A][0]>=3?1:0,E=r[A][1]>=3?1:0,S=r[A][2]>=3?1:0,C=r[A][3]>=3?1:0,N=r[A][0]>=2?1:0,L=r[A][1]>=2?1:0,O=r[A][2]>=2?1:0,P=r[A][3]>=2?1:0,R=r[A][0]>=1?1:0,I=r[A][1]>=1?1:0,F=r[A][2]>=1?1:0,D=r[A][3]>=1?1:0,B=x-M+l,z=b-E+l,k=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-P+2*l,W=x-R+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,st=o[K+M+o[tt+E+o[et+S+o[nt+C]]]]%32,rt=o[K+N+o[tt+L+o[et+O+o[nt+P]]]]%32,ot=o[K+R+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(s[it],x,b,w,T));let ct=.6-B*B-z*z-k*k-U*U;ct<0?h=0:(ct*=ct,h=ct*ct*this.dot4(s[st],B,z,k,U));let ht=.6-G*G-V*V-H*H-j*j;ht<0?u=0:(ht*=ht,u=ht*ht*this.dot4(s[rt],G,V,H,j));let ut=.6-W*W-q*q-X*X-Y*Y;ut<0?d=0:(ut*=ut,d=ut*ut*this.dot4(s[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(s[at],$,J,Z,Q)),27*(c+h+u+d+p)}}var s0;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(s0||(s0={}));const r0=[s0.ADD,s0.SET,s0.MULT,s0.SUBSTRACT,s0.DIVIDE];const o0=new class extends la{constructor(){super(...arguments),this.amplitude=aa.FLOAT(1),this.tamplitudeAttrib=aa.BOOLEAN(0),this.amplitudeAttrib=aa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=aa.VECTOR3([1,1,1]),this.offset=aa.VECTOR3([0,0,0]),this.octaves=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10]}),this.seed=aa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=aa.BOOLEAN(0),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=aa.BOOLEAN(0),this.restP=aa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=aa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=aa.INTEGER(r0.indexOf(s0.ADD),{menu:{entries:r0.map((t=>({name:t,value:r0.indexOf(t)})))}}),this.computeNormals=aa.BOOLEAN(1)}};class a0 extends nV{constructor(){super(...arguments),this.paramsConfig=o0,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}setOperation(t){this.p.operation.set(r0.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=Bs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=this._get_simplex(),r=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=r0[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,h=this.pv.tamplitudeAttrib;let u,f,g=new p.a;for(let t=0;t{s[3*t.index()+0]=e}));else{let t;for(let e=0;e{s[3*t.index()+1]=e}));else{let t;for(let e=0;e{s[3*t.index()+2]=e}));else{let t;for(let e=0;e{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}m0.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},m0.INPUT_CLONED_STATE=Ki.FROM_NODE;const f0=m0.DEFAULT_PARAMS;const g0=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(f0.applyToChildren,{separatorAfter:!0}),this.tname=aa.BOOLEAN(f0.tname),this.name=aa.STRING(f0.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=aa.BOOLEAN(f0.trenderOrder),this.renderOrder=aa.INTEGER(f0.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=aa.BOOLEAN(f0.tfrustumCulled),this.frustumCulled=aa.BOOLEAN(f0.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=aa.BOOLEAN(f0.tmatrixAutoUpdate),this.matrixAutoUpdate=aa.BOOLEAN(f0.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=aa.BOOLEAN(f0.tvisible),this.visible=aa.BOOLEAN(f0.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=aa.BOOLEAN(f0.tcastShadow),this.castShadow=aa.BOOLEAN(f0.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=aa.BOOLEAN(f0.treceiveShadow),this.receiveShadow=aa.BOOLEAN(f0.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class v0 extends nV{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(m0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new m0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const y0=new class extends la{};class x0 extends nV{constructor(){super(...arguments),this.paramsConfig=y0,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class b0 extends cf{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),s=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const r=i.type(),o=`${r} ${s} = ${this.read_attribute(t,r,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Sf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const s=this._texture_allocations_controller.variable(n);if(!s)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=s.component(),n=s.allocation();if(n){const s=n.textureName(),r=new Af(t,Bo.SAMPLER_2D,s);i.addDefinitions(t,[r]);return`texture2D( ${s}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Bo.VEC2,b0.UV_ATTRIB),i=new Mf(t,Bo.VEC2,b0.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${b0.UV_VARYING} = ${b0.UV_ATTRIB}`],xf.VERTEX)}}b0.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",b0.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",b0.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class w0{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new b0(b0.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,gr.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),t>this._last_simulated_frame&&this._compute_simulation(t-this._last_simulated_frame))}_compute_simulation(t=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();for(let e=0;et)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await li.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new A0(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var s=this._gpu_compute.init();null!==s&&(console.error(s),this.node.states.error.set(s))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Mi(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[pR.TIME]={value:this.node.scene().time()},t.uniforms[pR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const s=t.param_uniform_pairs;for(let t of s){const s=t[0],r=t[1],o=this.node.params.get(s),a=e[r];for(let t of n)t.uniforms[r]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[r])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[pR.TIME].value=this.node.scene().time(),t.material.uniforms[pR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));s++);const r=b0.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?A$:C.a;n.setAttribute(r,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const s=i.variables();if(!s)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const r=e.image.data;for(let t of s){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let s=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);r[s]=t}else e.attribValue(n).toArray(r,s);s+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const s=[];for(let t of i)for(let e of t.points())s.push(e);return s}return[]}}class C0{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new C0(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const s=t.graph.nodeFromId(i);if(s){const t=s.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class N0{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ss.ATTRIBUTE?[gf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new N0;for(let n of t){const t=C0.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const L0=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class O0{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==bF.type())),n=t.filter((t=>t.type()!=bF.type())),i=n.map((t=>t.name())).sort(),s=new Map;for(let t of n)s.set(t.name(),t);for(let t of i){const n=s.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=O0._sortNodes(t),e=O0._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case bF.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new C0(i.name(),Vo[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const e=new C0(i.attribute_name,Vo[r.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case AI.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(L0.includes(t)){const s=i.io.outputs.namedOutputConnectionPointsByName(t);if(s){const i=s.type(),r=new C0(t,Vo[i]);r.addGraphNodeId(e),n.push(r)}}}break}case gf.type():{const i=t,s=i.output_connection_point();if(s){const t=new C0(i.attribute_name,Vo[s.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)h.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;et.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new O0;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class P0 extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],s=new F,r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]),s.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(s,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){li.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return O0.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const R0=new class extends la{constructor(){super(...arguments),this.startFrame=aa.FLOAT(El.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=aa.BOOLEAN(1),this.maxTexturesSize=aa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=aa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=aa.INTEGER(0,{menu:{entries:M0.map(((t,e)=>({value:e,name:t})))}}),this.reset=aa.BUTTON(null,{callback:(t,e)=>{I0.PARAM_CALLBACK_reset(t)}}),this.material=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1})}};class I0 extends nV{constructor(){super(...arguments),this.paramsConfig=R0,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new P0(this),this.globals_handler=new b0(b0.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new S0(this),this.renderController=new w0(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=ts.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return jn.GL_PARTICLES}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.reset_gpu_compute_and_set_dirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.reset_gpu_compute(),this.gpuController.reset_particle_groups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class F0 extends QG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,s;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(s of(i=new _r(n),i.points())){const t=s.normal(),n=s.position().clone().add(t.multiplyScalar(e.amount));s.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}F0.DEFAULT_PARAMS={amount:0};const D0=F0.DEFAULT_PARAMS;const B0=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(D0.amount,{range:[-1,1]})}};class z0 extends nV{constructor(){super(...arguments),this.paramsConfig=B0}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){this._operation=this._operation||new F0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const k0=new p.a(0,0,1),U0=new p.a(0,0,1),G0=new p.a(0,1,0);class V0 extends QG{constructor(){super(...arguments),this._core_transform=new uU,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,k0,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),s=this._create_plane(i,e);this._core_transform.rotate_geometry(s,U0,G0);const r=this._core_transform.translation_matrix(this._center);return s.applyMatrix4(r),this.createCoreGroupFromGeometry(s)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}V0.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},V0.INPUT_CLONED_STATE=Ki.NEVER;const H0=V0.DEFAULT_PARAMS;const j0=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2(H0.size),this.useSegmentsCount=aa.BOOLEAN(H0.useSegmentsCount),this.stepSize=aa.FLOAT(H0.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2(H0.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=aa.VECTOR3(H0.direction),this.center=aa.VECTOR3(H0.center)}};class W0 extends nV{constructor(){super(...arguments),this.paramsConfig=j0}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(V0.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new V0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class q0 extends QG{static type(){return\\\\\\\"playerCapsule\\\\\\\"}cook(t,e){return this.createCoreGroupFromGeometry(Oy(e))}}q0.DEFAULT_PARAMS={radius:.5,height:1};const X0=q0.DEFAULT_PARAMS;const Y0=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(X0.radius,{range:[0,1],rangeLocked:[!0,!1]}),this.height=aa.FLOAT(X0.height,{range:[0,1],rangeLocked:[!0,!1]})}};class $0 extends nV{constructor(){super(...arguments),this.paramsConfig=Y0}static type(){return\\\\\\\"playerCapsule\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new q0(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const J0=\\\\\\\"position\\\\\\\";const Z0=new class extends la{constructor(){super(...arguments),this.updateX=aa.BOOLEAN(0),this.x=aa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=aa.BOOLEAN(0),this.y=aa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=aa.BOOLEAN(0),this.z=aa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=aa.BOOLEAN(1)}};class Q0 extends nV{constructor(){super(...arguments),this.paramsConfig=Z0,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t{h[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=aa.VECTOR3(o1.center.toArray()),this.longitude=aa.FLOAT(o1.longitude,{range:[0,360]}),this.latitude=aa.FLOAT(o1.latitude,{range:[-180,180]}),this.depth=aa.FLOAT(o1.depth,{range:[0,10]})}};class l1 extends nV{constructor(){super(...arguments),this.paramsConfig=a1}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(r1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new r1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c1{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,s=null;for(let r=0;r{r[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{r[t].push(e)}))})),o>0&&(s.push(o-1),s.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=r[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(s),o}static line_segment_to_geometries(t){var e;const n=[],i=new _r(t),s=i.attribNames(),r=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(r,i,s,e),n.push(t)}))}return n}}class h1{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new _r(this.geometry0),e=new _r(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const s=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+u),p.push(t[1]),p.push(n[1]+u),p.push(n[0]+u)}));f.intersection(r.attribNames(),o.attribNames()).forEach((t=>{const e=r.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const u1=new p.a(0,0,0),d1=new p.a(1,1,1);const p1=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.segmentsRadial=aa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=aa.BOOLEAN(0)}};class _1 extends nV{constructor(){super(...arguments),this.paramsConfig=p1,this._core_transform=new uU,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof As.a&&this._create_tube(t);const n=_r.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Cs.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new _r(n).points(),s=null===(e=n.getIndex())||void 0===e?void 0:e.array,r=c1.accumulated_curve_point_indices(s);for(let t of r){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=BJ.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,s=this._core_transform.matrix(e,u1,d1,1,hU),r=n.clone();r.applyMatrix4(s),i.push(r)}for(let t=0;t0){const e=i[t],n=i[t-1],s=this._skin(n,e);this._geometries.push(s)}}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}const m1=\\\\\\\"dist\\\\\\\";class f1 extends QG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new tQ,this._raycaster=function(){const t=new WL;return t.firstHitOnly=!0,t}(),this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,s;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(m1)||t.addNumericVertexAttrib(m1,1,-1));const r=t.points();for(let t of r)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),s=this._raycaster.intersectObjects(e.objects(),!0)[0],s){if(n.transformPoints&&t.setPosition(s.point),n.addDistAttribute){const e=this._pointPos.distanceTo(s.point);console.log(e),t.setAttribValue(m1,e)}n.transferFaceNormals&&s.face&&t.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}f1.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},f1.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const g1=f1.DEFAULT_PARAMS;const v1=new class extends la{constructor(){super(...arguments),this.useNormals=aa.BOOLEAN(g1.useNormals),this.direction=aa.VECTOR3(g1.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=aa.BOOLEAN(g1.transformPoints),this.transferFaceNormals=aa.BOOLEAN(g1.transferFaceNormals),this.addDistAttribute=aa.BOOLEAN(g1.addDistAttribute)}};class y1 extends nV{constructor(){super(...arguments),this.paramsConfig=v1}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(f1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new f1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const x1={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},b1=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",w1=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",T1={minFilter:w.V,magFilter:w.V,format:w.ic};class A1 extends B.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new Y.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new tt.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Q(n,i,T1),Object(On.i)(n)&&Object(On.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new wG(new d.a(n,i)),this.material=new F({uniforms:I.clone(x1),fragmentShader:w1,vertexShader:b1}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,s,r){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,h=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=h,t.setRenderTarget(l);var u=o.viewport;void 0!==u&&t.state.viewport(u),this.visible=!0}}}class M1 extends QG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],s=await li.renderersController.firstRenderer();if(!s)return this.createCoreGroupFromObjects(i);const r=n.objectsWithGeo();for(let t of r){const n=new A1(t.geometry,{clipBias:e.clipBias,renderer:s,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}M1.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},M1.INPUT_CLONED_STATE=Ki.NEVER;const E1=M1.DEFAULT_PARAMS;const S1=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(E1.active),this.clipBias=aa.FLOAT(E1.clipBias),this.color=aa.COLOR(E1.color.toArray()),this.opacity=aa.FLOAT(E1.opacity),this.pixelRatio=aa.INTEGER(E1.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=aa.BOOLEAN(E1.tblur),this.blur=aa.FLOAT(E1.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=aa.FLOAT(E1.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=aa.BOOLEAN(E1.tblur2,{visibleIf:{tblur:1}}),this.blur2=aa.FLOAT(E1.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=aa.FLOAT(E1.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class C1 extends nV{constructor(){super(...arguments),this.paramsConfig=S1}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(M1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new M1(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var N1=n(85);var L1;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(L1||(L1={}));const O1=[L1.POINTS_COUNT,L1.SEGMENT_LENGTH];var P1;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(P1||(P1={}));const R1=[P1.CENTRIPETAL,P1.CHORDAL,P1.CATMULLROM];const I1=new class extends la{constructor(){super(...arguments),this.method=aa.INTEGER(O1.indexOf(L1.POINTS_COUNT),{menu:{entries:O1.map(((t,e)=>({name:t,value:e})))}}),this.curveType=aa.INTEGER(R1.indexOf(P1.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:R1.map(((t,e)=>({name:t,value:e})))}}),this.tension=aa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=aa.INTEGER(100,{visibleIf:{method:O1.indexOf(L1.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=aa.FLOAT(1,{visibleIf:{method:O1.indexOf(L1.SEGMENT_LENGTH)}})}};class F1 extends nV{constructor(){super(...arguments),this.paramsConfig=I1}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;ei[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=hr(o);return this.createObject(a,Cs.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=R1[this.pv.curveType],i=this.pv.tension,s=new N1.a(e,!1,n,i),r=this._get_points_from_curve(s);let o=[];const a=[];for(let t=0;t0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=O1[this.pv.method];switch(e){case L1.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case L1.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}ls.unreachable(e)}}class D1 extends QG{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}D1.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const B1=D1.DEFAULT_PARAMS;const z1=new class extends la{constructor(){super(...arguments),this.tposition=aa.BOOLEAN(B1.tposition),this.position=aa.STRING(B1.position,{visibleIf:{tposition:!0}}),this.restP=aa.STRING(B1.restP,{visibleIf:{tposition:!0}}),this.tnormal=aa.BOOLEAN(B1.tnormal),this.normal=aa.STRING(B1.normal,{visibleIf:{tnormal:!0}}),this.restN=aa.STRING(B1.restN,{visibleIf:{tnormal:!0}})}};class k1 extends nV{constructor(){super(...arguments),this.paramsConfig=z1}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new D1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class U1 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"roundedBox\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.size,n=new Ly(e.x,e.y,e.z,t.divisions,t.bevel);return n.translate(t.center.x,t.center.y,t.center.z),n.computeVertexNormals(),n}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new Ly(s.x,s.y,s.z,n,e.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}U1.DEFAULT_PARAMS={size:new p.a(1,1,1),divisions:2,bevel:.1,center:new p.a(0,0,0)},U1.INPUT_CLONED_STATE=Ki.NEVER;const G1=U1.DEFAULT_PARAMS;const V1=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR3(G1.size),this.divisions=aa.INTEGER(G1.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=aa.FLOAT(G1.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(G1.center)}};class H1 extends nV{constructor(){super(...arguments),this.paramsConfig=V1}static type(){return\\\\\\\"roundedBox\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(U1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new U1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class j1 extends QG{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(t,e){const n=t[0];let i=n.faces();const s=[];let r=0;const o=new Map;for(let t of i){const e=t.area();o.set(t.index(),e)}const a=f.sortBy(i,(t=>o.get(t.index())||-1));let l=0;for(let t of a)r+=o.get(t.index()),s[l]=r,l++;const c=[];let h=[];e.transferAttributes&&(h=n.attribNamesMatchingMask(e.attributesToTransfer));const u=new Map,d=new Map;for(let t of h)u.set(t,[]),d.set(t,n.attribSize(t));const p=new yX,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rr.randFloat(_+t)*r;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Cs.POINTS);return this.createCoreGroupFromObjects([v])}}j1.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},j1.INPUT_CLONED_STATE=Ki.FROM_NODE;const W1=j1.DEFAULT_PARAMS;const q1=new class extends la{constructor(){super(...arguments),this.pointsCount=aa.INTEGER(W1.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=aa.INTEGER(W1.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=aa.BOOLEAN(W1.transferAttributes),this.attributesToTransfer=aa.STRING(W1.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=aa.BOOLEAN(W1.addIdAttribute),this.addIdnAttribute=aa.BOOLEAN(W1.addIdnAttribute)}};class X1 extends nV{constructor(){super(...arguments),this.paramsConfig=q1}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this._operation=this._operation||new j1(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Y1;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(Y1||(Y1={}));const $1=[Y1.MATRIX,Y1.AXIS];var J1;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(J1||(J1={}));const Z1=[J1.BBOX_CENTER,J1.BBOX_CENTER_OFFSET,J1.CUSTOM];class Q1 extends QG{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new Y.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=$1[e.mode];switch(n){case Y1.MATRIX:return this._applyMatrixShear(t,e);case Y1.AXIS:return this._applyAxisShear(t,e)}ls.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new _r(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=Z1[e.centerMode];switch(n){case J1.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case J1.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case J1.CUSTOM:return this._getAxisModeCenterCustom(e)}ls.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var K1;Q1.DEFAULT_PARAMS={mode:$1.indexOf(Y1.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:Z1.indexOf(J1.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},Q1.INPUT_CLONED_STATE=Ki.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(K1||(K1={}));const t2=Q1.DEFAULT_PARAMS;const e2=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(t2.mode,{menu:{entries:$1.map(((t,e)=>({name:t,value:e})))}}),this.xy=aa.FLOAT(t2.xy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.xz=aa.FLOAT(t2.xz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yx=aa.FLOAT(t2.yx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yz=aa.FLOAT(t2.yz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zx=aa.FLOAT(t2.zx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zy=aa.FLOAT(t2.zy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.centerMode=aa.INTEGER(t2.centerMode,{visibleIf:{mode:$1.indexOf(Y1.AXIS)},menu:{entries:Z1.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=aa.VECTOR3(t2.centerOffset.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.BBOX_CENTER_OFFSET)}}),this.center=aa.VECTOR3(t2.center.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.CUSTOM)}}),this.planeAxis=aa.VECTOR3(t2.planeAxis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axis=aa.VECTOR3(t2.axis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axisAmount=aa.FLOAT(t2.axisAmount,{range:[-1,1],visibleIf:{mode:$1.indexOf(Y1.AXIS)}})}};class n2 extends nV{constructor(){super(...arguments),this.paramsConfig=e2}static type(){return K1.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Q1.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set($1.indexOf(t))}cook(t){this._operation=this._operation||new Q1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const i2=new class extends la{};class s2 extends nV{constructor(){super(...arguments),this.paramsConfig=i2}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=c1.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const s=e[n-1],r=this._skin(s,t);i.push(r)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),s=this._get_line_segments(n),r=f.sortBy([i,s],(t=>-t.length)),o=r[0],a=r[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,s=this._skin(e,i);l.push(s)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}var r2;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(r2||(r2={}));const o2=[r2.X,r2.Y,r2.Z];class a2 extends QG{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new yr(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const s=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const r=o2[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),r){case r2.X:o=this._pointPos.x;break;case r2.Y:o=this._pointPos.y;break;case r2.Z:o=this._pointPos.z}this._positions[a]=o,h.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const u=f.uniq(l);for(let t of u){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(s.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=aa.BOOLEAN(l2.invert)}};class h2 extends nV{constructor(){super(...arguments),this.paramsConfig=c2}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new a2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const u2=new class extends la{constructor(){super(...arguments),this.startFrame=aa.INTEGER(El.START_FRAME)}};class d2 extends rV{constructor(){super(...arguments),this.paramsConfig=u2,this._last_simulated_frame=null,this.childrenDisplayController=new aV(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e({name:t,value:f2[t]})))}}),this.radius=aa.FLOAT(y2.radius,{visibleIf:{type:f2.default}}),this.resolution=aa.VECTOR2(y2.resolution,{visibleIf:{type:f2.default}}),this.open=aa.BOOLEAN(y2.open,{visibleIf:{type:f2.default}}),this.phiStart=aa.FLOAT(y2.phiStart,{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.phiLength=aa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaStart=aa.FLOAT(y2.thetaStart,{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaLength=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.detail=aa.INTEGER(y2.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:f2.isocahedron}}),this.center=aa.VECTOR3(y2.center)}};class b2 extends nV{constructor(){super(...arguments),this.paramsConfig=x2}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(v2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new v2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const w2=new class extends la{constructor(){super(...arguments),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\")}};class T2 extends nV{constructor(){super(...arguments),this.paramsConfig=w2,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const s=t.object(),r=e.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=Us.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of r)t=e.indexedAttribValue(n),h.pushOnArrayAtEntry(i,t,e);else for(let e of r)t=e.attribValue(n),h.pushOnArrayAtEntry(i,t,e)}const a=Ls(s.constructor);i.forEach(((t,e)=>{const i=_r.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);yr.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const A2=new A.a,M2=new K.a,E2=new p.a;class S2 extends J.a{constructor(){super(),this.uuid=On.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new G.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e0)for(let t=0;t0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t=0;t--){const e=r[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,s),_=c(_,3,r),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),r){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(h(e.normal)),a){const t=e.vertexNormals;n.push(h(t[0]),h(t[1]),h(t[2]))}if(l&&n.push(u(e.color)),p){const t=e.vertexColors;n.push(u(t[0]),u(t[1]),u(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=r),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new S2).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],s=t.morphTargets[n];for(let t=0,e=s.length;t0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}S2.prototype.isGeometry=!0;class C2{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,s;const r=t.faces;for(i=0;i0,r=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){u=[];for(let t=0;t0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,s,r){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(s),r[t].edges.push(o),r[e].edges.push(o)}function i(t,e,n,i,s){t.push(new N2(e,n,i,void 0,void 0,s))}function s(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function r(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}L2.prototype.smooth=function(o){var a,l,c,h,u,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,M,E,S,C,N,L,O,P,R,I,F,D,B,z=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(z)for(var k=0;k3&&(O=3/(8*_)),P=1-_*O,R=O,_<=2&&2==_&&(P=3/4,R=1/8),B=D.clone().multiplyScalar(P),w.set(0,0,0),k=0;k<_;k++)A=(I=F[k]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(R),B.add(w),b.push(B)}h=b.concat(x);var U,G,V,H,j,W,q,X=b.length;u=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{z2.PARAM_CALLBACK_reset(t)}})}};class z2 extends nV{constructor(){super(...arguments),this.paramsConfig=B2}static type(){return ns.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.inputGraphNode(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var k2=n(82);class U2 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new k2.a(this.loadingManager),s=await this._urlToLoad();i.load(s,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new k2.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new Fn.a;for(let t=0;t{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}V2.DEFAULT_PARAMS={url:G2,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const H2=V2.DEFAULT_PARAMS;const j2=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(H2.url,{fileBrowse:{type:[Or.SVG]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{W2.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=aa.BOOLEAN(H2.drawFillShapes),this.fillShapesWireframe=aa.BOOLEAN(H2.fillShapesWireframe),this.drawStrokes=aa.BOOLEAN(H2.drawStrokes),this.strokesWireframe=aa.BOOLEAN(H2.strokesWireframe)}};class W2 extends nV{constructor(){super(...arguments),this.paramsConfig=j2}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Hn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new V2(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const q2=\\\\\\\"geometry to switch to\\\\\\\";const X2=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Y2 extends nV{constructor(){super(...arguments),this.paramsConfig=X2}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[q2,q2,q2,q2]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class $2 extends gK{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const J2=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.detail=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=aa.BOOLEAN(0),this.center=aa.VECTOR3([0,0,0])}};class Z2 extends nV{constructor(){super(...arguments),this.paramsConfig=J2}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new $2(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Cs.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class Q2 extends cJ{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var K2=n(48);class t9 extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(this.manager);r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=s.parse(n);e&&e(i)}),n,i)}parse(t){return new e9(t)}}class e9{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),s=e/n.resolution,r=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*s,o=[];let a=0,l=0;for(let t=0;t{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await li.modulesRegister.module(Hn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await li.modulesRegister.module(Hn.SVGLoader);if(t)return t}}var s9;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(s9||(s9={}));const r9=[s9.MESH,s9.FLAT,s9.LINE,s9.STROKE],o9=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const a9=new class extends la{constructor(){super(...arguments),this.font=aa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Or.FONT]}}),this.text=aa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=aa.INTEGER(0,{menu:{entries:r9.map(((t,e)=>({name:t,value:e})))}}),this.size=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=aa.FLOAT(.1,{visibleIf:{type:r9.indexOf(s9.MESH)}}),this.segments=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:r9.indexOf(s9.MESH)}}),this.strokeWidth=aa.FLOAT(.02,{visibleIf:{type:r9.indexOf(s9.STROKE)}})}};class l9 extends nV{constructor(){super(...arguments),this.paramsConfig=a9,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(r9[this.pv.type]){case s9.MESH:return this._create_geometry_from_type_mesh(t);case s9.FLAT:return this._create_geometry_from_type_flat(t);case s9.LINE:return this._create_geometry_from_type_line(t);case s9.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new Q2(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(o9)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new _J(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let s=0;s0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||s||(s=n[e.textureName]))}));if(s)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=s;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=s)}n.needsUpdate=!0}}));return n}}c9.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},c9.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const h9=c9.DEFAULT_PARAMS;const u9=new class extends la{constructor(){super(...arguments),this.textureName=aa.STRING(h9.textureName)}};class d9 extends nV{constructor(){super(...arguments),this.paramsConfig=u9}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(c9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new c9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class p9 extends QG{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const s=i.map((t=>this._update_object(t,e)));return await Promise.all(s),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await li.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}p9.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Xm,tmagFilter:!1,magFilter:qm},p9.INPUT_CLONED_STATE=Ki.FROM_NODE;const _9=p9.DEFAULT_PARAMS;const m9=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(_9.applyToChildren,{separatorAfter:!0}),this.tencoding=aa.BOOLEAN(_9.tencoding),this.encoding=aa.INTEGER(_9.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=aa.BOOLEAN(_9.tmapping),this.mapping=aa.INTEGER(_9.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=aa.BOOLEAN(_9.twrap),this.wrapS=aa.INTEGER(_9.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=aa.INTEGER(_9.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=aa.BOOLEAN(_9.tanisotropy),this.useRendererMaxAnisotropy=aa.BOOLEAN(_9.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=aa.INTEGER(_9.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(_9.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(_9.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class f9 extends nV{constructor(){super(...arguments),this.paramsConfig=m9}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(p9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new p9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const g9=new p.a(0,0,1);class v9 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=new fJ(n,i,s,r);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,g9,e.direction),this.createCoreGroupFromGeometry(o)}}v9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},v9.INPUT_CLONED_STATE=Ki.FROM_NODE;const y9=v9.DEFAULT_PARAMS;const x9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(y9.radius,{range:[0,1]}),this.radiusTube=aa.FLOAT(y9.radiusTube,{range:[0,1]}),this.segmentsRadial=aa.INTEGER(y9.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=aa.INTEGER(y9.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=aa.VECTOR3(y9.direction),this.center=aa.VECTOR3(y9.center)}};class b9 extends nV{constructor(){super(...arguments),this.paramsConfig=x9}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new v9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class w9 extends QG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=e.p,a=e.q,l=new gJ(n,i,s,r,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}w9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},w9.INPUT_CLONED_STATE=Ki.FROM_NODE;const T9=w9.DEFAULT_PARAMS;const A9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(T9.radius),this.radiusTube=aa.FLOAT(T9.radiusTube),this.segmentsRadial=aa.INTEGER(T9.segmentsRadial,{range:[1,128]}),this.segmentsTube=aa.INTEGER(T9.segmentsTube,{range:[1,32]}),this.p=aa.INTEGER(T9.p,{range:[1,10]}),this.q=aa.INTEGER(T9.q,{range:[1,10]}),this.center=aa.VECTOR3(T9.center)}};class M9 extends nV{constructor(){super(...arguments),this.paramsConfig=A9}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new w9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var E9;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(E9||(E9={}));const S9=[E9.SET_PARAMS,E9.UPDATE_MATRIX];class C9 extends QG{constructor(){super(...arguments),this._core_transform=new uU,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=aU[e.applyOn];switch(n){case sU.GEOMETRIES:return this._update_geometries(t,e);case sU.OBJECTS:return this._update_objects(t,e)}ls.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=ZG._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=S9[e.objectMode];switch(n){case E9.SET_PARAMS:return this._update_objects_params(t,e);case E9.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ls.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=cU[e.rotationOrder];this._r.copy(e.r).multiplyScalar(On.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,cU[t.rotationOrder])}}C9.DEFAULT_PARAMS={applyOn:aU.indexOf(sU.GEOMETRIES),objectMode:S9.indexOf(E9.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:cU.indexOf(lU.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},C9.INPUT_CLONED_STATE=Ki.FROM_NODE;const N9=C9.DEFAULT_PARAMS;const L9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(N9.applyOn,{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=aa.INTEGER(N9.objectMode,{visibleIf:{applyOn:aU.indexOf(sU.OBJECTS)},menu:{entries:S9.map(((t,e)=>({name:t,value:e})))}}),this.group=aa.STRING(N9.group,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}}),this.rotationOrder=aa.INTEGER(N9.rotationOrder,{menu:{entries:cU.map(((t,e)=>({name:t,value:e})))}}),this.t=aa.VECTOR3(N9.t),this.r=aa.VECTOR3(N9.r),this.s=aa.VECTOR3(N9.s),this.scale=aa.FLOAT(N9.scale,{range:[0,10]}),this.pivot=aa.VECTOR3(N9.pivot,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}})}};class O9 extends nV{constructor(){super(...arguments),this.paramsConfig=L9}static type(){return K1.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(C9.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(aU.indexOf(t))}setObjectMode(t){this.p.objectMode.set(S9.indexOf(t))}cook(t){this._operation=this._operation||new C9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const P9=new class extends la{constructor(){super(...arguments),this.useSecondInput=aa.BOOLEAN(1),this.reference=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},visibleIf:{useSecondInput:0}})}};class R9 extends nV{constructor(){super(...arguments),this.paramsConfig=P9}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let s=0;s({name:t,value:e})))}};function D9(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const B9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(aU.indexOf(sU.GEOMETRIES),{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.count=aa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(0)}),this.r0=aa.VECTOR3([0,0,0],{...D9(0)}),this.rotationOrder1=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(1)}),this.r1=aa.VECTOR3([0,0,0],{...D9(1)}),this.rotationOrder2=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(2)}),this.r2=aa.VECTOR3([0,0,0],{...D9(2)}),this.rotationOrder3=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(3)}),this.r3=aa.VECTOR3([0,0,0],{...D9(3)}),this.rotationOrder4=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(4)}),this.r4=aa.VECTOR3([0,0,0],{...D9(4)}),this.rotationOrder5=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(5)}),this.r5=aa.VECTOR3([0,0,0],{...D9(5)})}};class z9 extends nV{constructor(){super(...arguments),this.paramsConfig=B9,this._core_transform=new uU,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=aU[this.pv.applyOn];switch(n){case sU.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case sU.OBJECTS:return this._apply_matrix_to_objects(t,e)}ls.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[js.POSITION,js.NORMAL,js.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&qs.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class V9 extends nV{constructor(){super(...arguments),this.paramsConfig=G9,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}setMode(t){this.p.mode.set(U9.indexOf(t))}cook(t){const e=U9[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case k9.RESET_OBJECT:return this._reset_objects(e);case k9.CENTER_GEO:return this._center_geos(e,!1);case k9.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ls.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),uU.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let s=i;const r=t[1];r&&(s=r.objectsWithGeo());for(let t=0;te.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let s=0,r=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,r=Math.max(r,e.y+e.h),s=Math.max(s,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?bi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.inputGraphNode(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const s=t;n=bi.findNode(i,s,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new ho,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(li.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return zr.create(this.param,e,t,n)}}class f3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class g3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const v3=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],y3=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class x3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const s=t[0],r=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(s)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,r,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(v3.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?y3.indexOf(n)>=0?t[n]:-1:t}return-1}}class b3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],s=t[1];let r=null;try{r=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(r){const t=r.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(s){const t=n[s];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class w3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new ho,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const s=t[0],r=this.getReferencedParam(s);if(r){r.isDirty()&&await r.compute();const t=r.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class T3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],s=t[2],r=this.node(),o=r?bi.findNode(r,n):null;let a;o&&o.type()==fZ.type()&&(a=o.stamp_value(s)),null==a&&(a=i),e(a)}else e(0)}))}}class A3 extends m3{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class M3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class E3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class S3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class C3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class N3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.objectsCount())}}else e(0)}))}}class L3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(os.tailDigits(t))}else e(0)}else e(0)}))}}class O3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class P3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class R3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class I3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.pointsCount())}}else e(0)}))}}class F3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class D3 extends m3{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class B3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class z3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let s=t[2]||1;return n&&(e=n.substr(i,s)),e}}class k3 extends m3{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class U3{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),s=b.clone(i);if(0==this._validateNames(s))return;b.clone(this._created_spare_param_names).concat(s).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=JG.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(s.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},s=b.merge(n,i);let r=this._init_value_serialized_by_param_name.get(t.name());null==r&&(r=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:r,raw_input:o,options:s})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Er.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class G3{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new U3(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var V3;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(V3||(V3={}));class H3{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class j3{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class W3{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)h.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)h.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class q3{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new W3(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const X3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\";\\\\\\\",[V3.BODY]:\\\\\\\";\\\\\\\"},Y3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\"\\\\\\\",[V3.BODY]:\\\\\\\"\\\\t\\\\\\\"};class $3{static node_comment(t,e){let n=`// ${t.path()}`,i=Y3[e];if(e==V3.BODY){let e=this.node_distance_to_material(t);t.type()==ns.OUTPUT&&(e+=1),i=i.repeat(e)}return e==V3.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=Y3[n];if(n==V3.BODY&&(s=s.repeat(this.node_distance_to_material(t))),e=`${s}${e}`,i){const t=e[e.length-1],i=X3[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==V3.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=ns.INPUT&&t.type()!=ns.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class J3{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new wF,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)h.pushOnArrayAtEntry(n,t,i)}const s=new Map;for(let t of i)s.set(t.graphNodeId(),!0);for(let e of t)s.get(e.graphNodeId())||(i.push(e),s.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new q3(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,yf.FUNCTION,V3.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,V3.DEFINE),this.addDefinitions(t,e,yf.VARYING,V3.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,V3.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,V3.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const s=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)s.push(e)}}if(s.length>0){const t=new vf(s),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const r=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),h.pushOnArrayAtEntry(r,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=r.get(e);if(n){const t=n[0];if(t){const e=$3.node_comment(t.node,i);h.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=$3.line_wrap(t.node,e.line,i);h.pushOnArrayAtEntry(a,i,n)}const s=$3.post_line_separator(i);h.pushOnArrayAtEntry(a,i,s)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let s=0;s0){const r=this._lines.get(e),o=$3.node_comment(t,n);if(h.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((e=>{e=$3.line_wrap(t,e,n),h.pushOnArrayAtEntry(r,n,e)})),n!=V3.BODY||!i){const t=$3.post_line_separator(n);h.pushOnArrayAtEntry(r,n,t)}}}}class Z3{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&h.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=xf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const s=this._graph_id_by_depth.get(e);s&&s.forEach((e=>{var s;if(null===(s=this._graph_ids_by_shader_name.get(t))||void 0===s?void 0:s.get(e)){const s=this._graph.nodeFromId(e);this.add_nodes_with_children(s,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==ns.INPUT){const s=t.parent();if(s){const r=this.sorted_nodes_for_shader_name_for_parent(s,i);for(let s of r)s.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(s,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const s=this._graph_id_by_depth.get(n);s&&s.forEach((n=>{var s;if(!e||(null===(s=this._graph_ids_by_shader_name.get(e))||void 0===s?void 0:s.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const s=i[0];return t.context()==s.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(h.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),s=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(s.length>0)for(let e of s)h.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==ns.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const Q3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),K3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),t4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class e4 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case bF.type():case AF.type():e.push(t);break;case gf.type():case Lf.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case AI.type():e.push(t);break;case gf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new J3(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(e4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"mvPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldNormal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"gl_Position\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"cameraPosition\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return e4.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new j3(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return e4.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return Q3.get(t)}insert_body_after(t){return K3.get(t)}lines_to_remove(t){return t4.get(t)}_replace_template(t,e){const n=this.builder_lines(e,V3.FUNCTION_DECLARATION),i=this.builder_lines(e,V3.DEFINE),s=this.builder_lines(e,V3.BODY);let r=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let h=!1,u=!1;for(let t of r){1==h&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),h=!1),1==u&&(s&&this._insert_lines(o,s),u=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(h=!0),l&&t.indexOf(l)>=0&&(u=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=U[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var n4,i4;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(n4||(n4={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(i4||(i4={}));const s4=[i4.GL_FRAGCOORD,i4.GL_POINTCOORD];class r4 extends e4{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const s=i.createMaterial();s.name=e,t.customMaterials[e]=s}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const s=t.customMaterials[n];s&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(s),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const s=this._shaders_by_name.get(xf.VERTEX),r=this._shaders_by_name.get(xf.FRAGMENT);s&&r&&(t.vertexShader=s,t.fragmentShader=this.processFilterFragmentShader(r),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const s=t.io.inputs.named_input(n),r=t.variableForInput(n),o=this.variable_config(n);let a=null;if(s)a=hf.vector3(r);else if(o.default_from_attribute()){const s=t.io.inputs.namedInputConnectionPointsByName(n);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),s=o.if_condition();s&&e.addBodyLines(t,[`#if ${s}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const r=o.postLines();r&&e.addBodyLines(t,r),s&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,s=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(s)for(let n of s)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),s=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),r=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${r} = ${s}`])}handle_globals_output_name(t){var e;switch(t.output_name){case i4.TIME:return void this.handleTime(t);case i4.RESOLUTION:return void this.handle_resolution(t);case i4.MV_POSITION:return void this.handle_mvPosition(t);case i4.GL_POSITION:return void this.handle_gl_Position(t);case i4.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case i4.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Af(t.globals_node,Bo.FLOAT,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),h.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Bo.VEC2,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let s of l){const l=t.glVarName(s),c=e.current_shader_name,h={globals_node:t,shaders_collection_controller:e,output_name:s,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:r,body_lines_by_shader_name:a};this.handle_globals_output_name(h)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==xf.VERTEX&&s4.includes(t)||i.push(t);return i}}const o4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const a4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var l4=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const c4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),h4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const u4=new Map([]);u4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return o4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),u4.set(n4.DEPTH,class extends r4{templateShader(){const t=H.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return a4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),u4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l4,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return c4.get(t)}insert_body_after(t){return h4.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class d4 extends r4{custom_assembler_class_by_custom_name(){return u4}}class p4 extends d4{templateShader(){const t=H.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class _4 extends d4{templateShader(){const t=H.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class m4 extends d4{templateShader(){const t=H.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var f4=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class g4 extends d4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",g4.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?H.physical:H.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),g4.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"metalness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"roughness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"emissive\\\\\\\",Bo.VEC3,[1,1,1])),g4.USE_SSS&&e.push(new Ho(\\\\\\\"SSSModel\\\\\\\",Bo.SSS_MODEL,f4)),this.isPhysical()&&(e.push(new Ho(\\\\\\\"transmission\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"thickness\\\\\\\",Bo.FLOAT,1))),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){const t=[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"];return this.isPhysical()&&(t.push(\\\\\\\"transmission\\\\\\\"),t.push(\\\\\\\"thickness\\\\\\\")),[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,t,[xf.VERTEX])]}create_variable_configs(){const t=e4.create_variable_configs();return t.push(new j3(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new j3(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new j3(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),g4.USE_SSS&&t.push(new j3(\\\\\\\"SSSModel\\\\\\\",{default:f4,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),this.isPhysical()&&(t.push(new j3(\\\\\\\"transmission\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_transmission = \\\\\\\"})),t.push(new j3(\\\\\\\"thickness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_thickness = \\\\\\\"}))),t}}g4.USE_SSS=!0;class v4 extends g4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshPhysicalBuilderMatNode\\\\\\\",v4.filterFragmentShader)}isPhysical(){return!0}static filterFragmentShader(t){return t=t.replace(\\\\\\\"#include \\\\\\\",function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");let n=0;for(let t of e)t.includes(\\\\\\\"float transmissionFactor = transmission;\\\\\\\")&&(t=\\\\\\\"float transmissionFactor = transmission * POLY_transmission;\\\\\\\",e[n]=t),t.includes(\\\\\\\"float thicknessFactor = thickness;\\\\\\\")&&(t=\\\\\\\"float thicknessFactor = thickness * POLY_thickness;\\\\\\\",e[n]=t),n++;return e.join(\\\\\\\"\\\\n\\\\\\\")}(k))}}const y4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),x4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const b4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),w4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const T4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),A4=new Map;A4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return y4.get(t)}insert_body_after(t){return x4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),A4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:l4,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return b4.get(t)}insert_body_after(t){return w4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class M4 extends r4{custom_assembler_class_by_custom_name(){return A4}templateShader(){const t=H.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"gl_PointSize\\\\\\\",Bo.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return e4.create_globals_node_output_connections().concat([new Ho(i4.GL_POINTCOORD,Bo.VEC2)])}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return e4.create_variable_configs().concat([new j3(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return T4.get(t)}}const E4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),S4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const C4=new Map([]);C4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:l4,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return E4.get(t)}insert_body_after(t){return S4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const N4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class L4 extends r4{templateShader(){const t=H.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",C4),C4}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(L4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return L4.create_globals_node_output_connections()}create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return N4.get(t)}}class O4 extends e4{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include \\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._root_nodes)switch(s.type()){case bF.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._leaf_nodes)switch(s.type()){case AI.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}setup_shader_names_and_variables(){var t;const e=new Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new O0,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new O0}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,s){var r;if(n){const n=t.variableForInput(e),o=hf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=s.current_shader_name;if(e&&(null===(r=e.allocation())||void 0===r?void 0:r.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;s.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const s=n;this.add_export_body_line(t,n,i,s,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const s=t.gl_type(),r=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,s,r,e),a=t.glVarName(t.output_name),l=`${s} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(r),h=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==h){const n=this.textureAllocationsController().variable(r);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Af(t,Bo.FLOAT,e);n.addDefinitions(t,[i]);const s=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[s]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,e,n);if(o){const i=`${r} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class P4 extends e4{templateShader(){return{fragmentShader:\\\\\\\"#include \\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(xf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==xf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}create_shader_configs(){return[new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let s;\\\\\\\"color\\\\\\\"==i&&(s=`diffuseColor.xyz = ${hf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${hf.any(n)}`),s&&e.addBodyLines(t,[s])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],s=[];for(let e of t.io.outputs.used_output_names()){const r=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":s.push(new Af(t,Bo.FLOAT,e)),i.push(`float ${r} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,r)}}e.addDefinitions(t,s,n),e.addBodyLines(t,i)}}const R4=new Map([]);class I4 extends r4{custom_assembler_class_by_custom_name(){return R4}}const F4=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),D4=new Map([[xf.FRAGMENT,[]]]);class B4 extends I4{templateShader(){return{vertexShader:Lk,fragmentShader:Ok,uniforms:I.clone(Pk)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return gr.add_user_data_render_hook(e,Ik.render_hook.bind(Ik)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"density\\\\\\\",Bo.FLOAT,1)])}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"pos_normalized\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return B4.create_globals_node_output_connections()}insert_body_after(t){return F4.get(t)}lines_to_remove(t){return D4.get(t)}create_shader_configs(){return[new H3(xf.VERTEX,[],[]),new H3(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{}),new j3(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return B4.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map;let l,c;for(let s of t.io.outputs.used_output_names()){const u=t.glVarName(s),d=e.current_shader_name;switch(s){case\\\\\\\"time\\\\\\\":l=new Af(t,Bo.FLOAT,s),d&&h.pushOnArrayAtEntry(o,d,l),c=`float ${u} = ${s}`;for(let t of r)h.pushOnArrayAtEntry(o,t,l),h.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class z4{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(z_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(hm,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Mm,Kl),t.registerNode(Em,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Eg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(zg,ic),t.registerNode(Kg,ic),t.registerNode(iv,ic),t.registerNode(lv,sc),t.registerNode(hv,sc),t.registerNode(_v,sc),t.registerNode(fv,sc),t.registerNode(yv,nc),t.registerNode(wv,ic),t.registerNode(Mv,nc),t.registerNode(Cv,rc),t.registerNode(Lv,rc),t.registerNode(Nv,rc),t.registerNode(Ov,rc),t.registerNode(Pv,rc),t.registerNode(Rv,rc)}}.run(t),class{static run(t){t.registerNode(Bv,cc),t.registerNode(Uv,lc),t.registerNode(Vv,lc),t.registerNode(Wv,lc),t.registerNode(ey,oc),t.registerNode(_y,oc),t.registerNode(py,oc),t.registerNode(fy,lc),t.registerNode(xl,ac),t.registerNode(Yy,oc),t.registerNode(al,ac),t.registerNode(Qy,lc),t.registerNode(tx,lc),t.registerNode(AL,oc),t.registerNode(Xa,ac),t.registerNode(CL,cc),t.registerNode(PL,lc),t.registerNode(GL,ac),t.registerNode(Qa,ac),t.registerNode(hO,lc),t.registerNode(nl,cc),t.registerNode(_O,cc),t.registerNode(MO,cc),t.registerNode(SO,lc),t.registerNode(LO,lc),t.registerNode(wl,ac),t.registerNode(PO,lc),t.registerNode(dl,ac),t.registerNode(FO,hc),t.registerNode(DO,hc),t.registerNode(BO,hc),t.registerNode(zO,hc),t.registerNode(kO,hc),t.registerNode(UO,hc)}}.run(t),class{static run(t){t.registerNode(MP,gc),t.registerNode(xR,vc),t.registerNode(EP,xc),t.registerNode(rR,gc),t.registerNode(MR,xc),t.registerNode(uR,fc),t.registerNode(SP,xc),t.registerNode(CP,xc),t.registerNode(gf,_c,{except:[`${ts.COP}/builder`]}),t.registerNode(ZO,dc),t.registerNode(NP,gc),t.registerNode(eR,gc),t.registerNode(NR,uc),t.registerNode(DR,fc),t.registerNode(BR,gc),t.registerNode(UR,_c),t.registerNode(LP,xc),t.registerNode(HR,pc),t.registerNode(jR,gc),t.registerNode(OP,dc),t.registerNode(XR,pc),t.registerNode(qP,pc),t.registerNode(oR,gc),t.registerNode(XP,pc),t.registerNode(eI,gc),t.registerNode(PP,gc),t.registerNode(RP,gc),t.registerNode(nR,pc),t.registerNode(sI,gc),t.registerNode(oI,gc),t.registerNode(lI,gc),t.registerNode(hI,gc),t.registerNode(HO,dc),t.registerNode(KO,dc),t.registerNode(eP,dc),t.registerNode(iP,dc),t.registerNode(IP,gc),t.registerNode(pI,uc),t.registerNode(wI,fc),t.registerNode(FP,gc),t.registerNode(AI,_c),t.registerNode(EI,uc),t.registerNode(CI,uc),t.registerNode(OI,fc),t.registerNode(RI,bc),t.registerNode($O,dc),t.registerNode(qO,dc),t.registerNode(DP,gc),t.registerNode(UI,pc),t.registerNode(GI,pc),t.registerNode(HI,uc),t.registerNode(BP,gc),t.registerNode(zP,gc),t.registerNode(YP,gc),t.registerNode(WI,gc),t.registerNode($P,gc),t.registerNode(JP,gc),t.registerNode(JI,gc),t.registerNode(XI,gc),t.registerNode(lR,gc),t.registerNode(KI,gc),t.registerNode(tF,gc),t.registerNode(yF,bc),t.registerNode(vF,pc),t.registerNode(kP,gc),t.registerNode(dR,fc),t.registerNode(bF,_c),t.registerNode(AF,_c),t.registerNode(ZP,gc),t.registerNode(NF,yc),t.registerNode(RF,yc),t.registerNode(IF,yc),t.registerNode(FF,yc),t.registerNode(zF,_c),t.registerNode(GF,_c),t.registerNode(UP,dc),t.registerNode(QP,pc),t.registerNode(MF,pc),t.registerNode(HF,uc),t.registerNode(QF,pc),t.registerNode(tD,gc),t.registerNode(GP,gc),t.registerNode(VP,xc),t.registerNode(iR,gc),t.registerNode(nD,pc),t.registerNode(HP,gc),t.registerNode(CF,mc),t.registerNode(KP,pc),t.registerNode(vI,fc),t.registerNode(sD,fc,TD),t.registerNode(mI,fc,TD),t.registerNode(aR,gc),t.registerNode(oD,fc),t.registerNode(jP,xc),t.registerNode(lD,uc),t.registerNode(dD,_c),t.registerNode(mD,fc),t.registerNode(Lf,_c),t.registerNode(vD,_c),t.registerNode(hP,dc),t.registerNode(mP,dc),t.registerNode(uP,dc),t.registerNode(_P,dc),t.registerNode(fP,dc),t.registerNode(dP,dc),t.registerNode(pP,dc),t.registerNode(xD,pc),t.registerNode(wD,pc)}}.run(t),class{static run(t){t.registerNode(SD,wc),t.registerNode(ND,wc),t.registerNode(OD,wc),t.registerNode(zD,wc)}}.run(t),class{static run(t){t.registerNode(XD,Ac),t.registerNode(rB,Ac),t.registerNode(DB,Mc),t.registerNode(VB,Tc),t.registerNode(YB,Mc),t.registerNode(tz,Tc),t.registerNode(mz,Mc),t.registerNode(yz,Mc),t.registerNode(Mz,Mc),t.registerNode(Nz,Tc),t.registerNode(Uz,Mc),t.registerNode(jz,Tc),t.registerNode(Yz,Mc),t.registerNode(Qz,Tc),t.registerNode(hk,Mc),t.registerNode(fk,Mc),t.registerNode(xk,Sc),t.registerNode(Tk,Tc),t.registerNode(Ek,Tc),t.registerNode(Nk,Mc),t.registerNode(Bk,Cc),t.registerNode(Uk,Cc),t.registerNode(Hk,Ec),t.registerNode(jk,Ec),t.registerNode(Wk,Ec),t.registerNode(qk,Ec),t.registerNode(Xk,Ec),t.registerNode(Yk,Ec)}}.run(t),class{static run(t){t.registerNode(nU,Rc),t.registerNode(MU,Rc),t.registerNode(DU,Rc),t.registerNode(HU,Rc),t.registerNode($U,Rc),t.registerNode(iG,Rc),t.registerNode(pG,Lc),t.registerNode(vG,Fc),t.registerNode(CG,Nc),t.registerNode(RG,Pc),t.registerNode(DG,Fc),t.registerNode(GG,Fc),t.registerNode(_V,Nc),t.registerNode(NV,Lc),t.registerNode(RV,Fc),t.registerNode(DV,Nc),t.registerNode(XH,Oc),t.registerNode(ZH,Oc),t.registerNode(tj,Oc),t.registerNode(sj,Ic),t.registerNode(rj,Ic),t.registerNode(oj,Ic),t.registerNode(aj,Ic),t.registerNode(lj,Ic),t.registerNode(cj,Ic)}}.run(t),class{static run(t){t.registerNode(gj,Qc),t.registerNode(bj,Qc),t.registerNode(Aj,Zc),t.registerNode(Sj,Zc),t.registerNode(Lj,Kc),t.registerNode(Pj,Kc),t.registerNode(Fj,Kc),t.registerNode(Bj,Kc),t.registerNode(qj,Qc),t.registerNode(Hj,Qc),t.registerNode(Jj,Qc),t.registerNode(Kj,Kc),t.registerNode(nW,Zc),t.registerNode(sW,Jc),t.registerNode(oW,Kc),t.registerNode(dW,Kc),t.registerNode(_W,Kc),t.registerNode(fW,Kc),t.registerNode(yW,Qc),t.registerNode(wW,Qc),t.registerNode(AW,Kc),t.registerNode(SW,Qc),t.registerNode(LW,Zc),t.registerNode(PW,Kc),t.registerNode(DW,Jc),t.registerNode(UW,Qc),t.registerNode(VW,Jc),t.registerNode(WW,Qc),t.registerNode(YW,th),t.registerNode($W,th),t.registerNode(JW,th),t.registerNode(ZW,th),t.registerNode(QW,th),t.registerNode(KW,th)}}.run(t),class{static run(t){t.registerNode(oq,Dc),t.registerNode(vH,zc),t.registerNode(aq,Bc),t.registerNode(nq,Bc),t.registerNode(lq,Bc),t.registerNode(cq,Bc),t.registerNode(hq,Bc),t.registerNode(uq,Bc)}}.run(t),class{static run(t){t.registerOperation(dq),t.registerOperation(Eq),t.registerOperation(Iq),t.registerOperation(zq),t.registerOperation(Vq),t.registerOperation(nX),t.registerOperation(Jq),t.registerOperation(aX),t.registerOperation(UX),t.registerOperation(jX),t.registerOperation(g$),t.registerOperation(x$),t.registerOperation(IJ),t.registerOperation(kJ),t.registerOperation(xZ),t.registerOperation(kZ),t.registerOperation(ZQ),t.registerOperation(cK),t.registerOperation(yK),t.registerOperation(TK),t.registerOperation(NK),t.registerOperation(HK),t.registerOperation(KK),t.registerOperation(kK),t.registerOperation(h0),t.registerOperation(m0),t.registerOperation(F0),t.registerOperation(V0),t.registerOperation(q0),t.registerOperation(K0),t.registerOperation(r1),t.registerOperation(f1),t.registerOperation(M1),t.registerOperation(D1),t.registerOperation(U1),t.registerOperation(j1),t.registerOperation(Q1),t.registerOperation(a2),t.registerOperation(v2),t.registerOperation(O2),t.registerOperation(V2),t.registerOperation(c9),t.registerOperation(p9),t.registerOperation(v9),t.registerOperation(w9),t.registerOperation(C9),t.registerOperation(X9),t.registerOperation(t3),t.registerOperation(s3),t.registerNode(mq,Hc),t.registerNode(gq,Uc),t.registerNode(Mq,Uc),t.registerNode(Nq,Gc),t.registerNode(Bq,Gc),t.registerNode(Gq,Gc),t.registerNode(qq,Gc),t.registerNode(Yq,Gc),t.registerNode(Kq,Gc),t.registerNode(rX,Gc),t.registerNode(uX,Gc),t.registerNode(pX,Gc),t.registerNode(mX,Gc),t.registerNode(bX,Gc),t.registerNode(TX,qc),t.registerNode(MX,qc),t.registerNode(HX,qc),t.registerNode(XX,Yc),t.registerNode(y$,kc),t.registerNode(T$,kc),t.registerNode(SJ,Wc),t.registerNode(RJ,Yc),t.registerNode(DJ,Yc),t.registerNode(VJ,Yc),t.registerNode($J,Yc),t.registerNode(eZ,qc),t.registerNode(sZ,Yc),t.registerNode(fZ,qc),t.registerNode(TZ,Yc),t.registerNode(CZ,Hc),t.registerNode(DZ,Hc),t.registerNode(VZ,Wc),t.registerNode(jZ,Wc),t.registerNode(rQ,qc),t.registerNode(aQ,qc),t.registerNode(HQ,kc),t.registerNode(WQ,qc),t.registerNode(tK,Hc),t.registerNode(nK,qc),t.registerNode(oK,Yc),t.registerNode(mK,qc),t.registerNode(pK,Wc),t.registerNode(wK,Yc),t.registerNode(EK,$c),t.registerNode(CK,$c),t.registerNode(PK,qc),t.registerNode(IK,qc),t.registerNode(DK,Yc),t.registerNode(zK,kc),t.registerNode(VK,$c),t.registerNode(XK,Wc),t.registerNode(n0,Yc),t.registerNode(a0,Wc),t.registerNode(c0,qc),t.registerNode(d0,Wc),t.registerNode(_0,Hc),t.registerNode(v0,qc),t.registerNode(x0,kc,{userAllowed:!1}),t.registerNode(I0,Vc),t.registerNode(z0,qc),t.registerNode(W0,Yc),t.registerNode($0,qc),t.registerNode(l1,qc),t.registerNode(Q0,qc),t.registerNode(n1,jc),t.registerNode(hV,kc),t.registerNode(_1,qc),t.registerNode(y1,qc),t.registerNode(C1,$c),t.registerNode(F1,qc),t.registerNode(k1,Gc),t.registerNode(H1,Hc),t.registerNode(X1,qc),t.registerNode(s2,qc),t.registerNode(n2,qc),t.registerNode(d2,kc),t.registerNode(_2,kc),t.registerNode(h2,qc),t.registerNode(b2,Yc),t.registerNode(T2,qc),t.registerNode(I2,qc),t.registerNode(D2,Wc),t.registerNode(z2,Wc),t.registerNode(sV,Wc),t.registerNode(W2,Hc),t.registerNode(Y2,Wc),t.registerNode(Z2,Yc),t.registerNode(l9,Yc),t.registerNode(d9,qc),t.registerNode(f9,qc),t.registerNode(b9,Yc),t.registerNode(M9,Yc),t.registerNode(O9,qc),t.registerNode(R9,qc),t.registerNode(z9,qc),t.registerNode(V9,qc),t.registerNode(W9,Yc),t.registerNode($9,qc),t.registerNode(K9,qc),t.registerNode(i3,qc),t.registerNode(o3,qc),t.registerNode(c3,Xc),t.registerNode(h3,Xc),t.registerNode(u3,Xc),t.registerNode(d3,Xc),t.registerNode(p3,Xc),t.registerNode(_3,Xc)}}.run(t)}}.run(li),class{static run(t){t.registerCamera(XH),t.registerCamera(ZH)}}.run(li),class{static run(t){t.expressionsRegister.register(f3,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(g3,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(x3,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(b3,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(w3,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(T3,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(A3,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(M3,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(E3,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(S3,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(C3,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(N3,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(L3,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(O3,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(P3,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(R3,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(I3,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(F3,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(D3,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(B3,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(z3,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(k3,\\\\\\\"windowSize\\\\\\\")}}.run(li),class{static run(t){t.assemblersRegister.register(jn.GL_MESH_BASIC,G3,p4),t.assemblersRegister.register(jn.GL_MESH_LAMBERT,G3,_4),t.assemblersRegister.register(jn.GL_MESH_PHONG,G3,m4),t.assemblersRegister.register(jn.GL_MESH_STANDARD,G3,g4),t.assemblersRegister.register(jn.GL_MESH_PHYSICAL,G3,v4),t.assemblersRegister.register(jn.GL_PARTICLES,G3,O4),t.assemblersRegister.register(jn.GL_POINTS,G3,M4),t.assemblersRegister.register(jn.GL_LINE,G3,L4),t.assemblersRegister.register(jn.GL_TEXTURE,G3,P4),t.assemblersRegister.register(jn.GL_VOLUME,G3,B4)}}.run(li))}}z4._started=!1,z4.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2813173"}},"type":2,"external":true,"timestamp":1723912466928},{"data":{"url":"blob:https://ipfs.arkivo.art/830febdb-714c-4836-8828-e433af48fdd8","host":"","path":"https://ipfs.arkivo.art/830febdb-714c-4836-8828-e433af48fdd8","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":1723912466929},{"data":{"url":"blob:https://ipfs.arkivo.art/830febdb-714c-4836-8828-e433af48fdd8","body":"\"// ../../Documents/PJS/src/polygonjs/PolyConfig.js\\nfunction configurePolygonjs(poly) {\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n configureScene\\n};\\n\"","status":200,"headers":{"content-type":"application/javascript","content-length":"175"}},"type":2,"external":true,"timestamp":1723912471680}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized 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