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t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,l)}lookAt(t,e,n){const i=this.elements;return h.subVectors(t,e),0===h.lengthSq()&&(h.z=1),h.normalize(),c.crossVectors(n,h),0===c.lengthSq()&&(1===Math.abs(n.z)?h.x+=1e-4:h.z+=1e-4,h.normalize(),c.crossVectors(n,h)),c.normalize(),u.crossVectors(h,c),i[0]=c.x,i[4]=u.x,i[8]=h.x,i[1]=c.y,i[5]=u.y,i[9]=h.y,i[2]=c.z,i[6]=u.z,i[10]=h.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=s.set(i[0],i[1],i[2]).length();const a=s.set(i[4],i[5],i[6]).length(),l=s.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/r,u=1/a,h=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=u,o.elements[5]*=u,o.elements[6]*=u,o.elements[8]*=h,o.elements[9]*=h,o.elements[10]*=h,e.setFromRotationMatrix(o),n.x=r,n.y=a,n.z=l,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}r.prototype.isMatrix4=!0;const s=new i.a,o=new r,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,u=new i.a,h=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(3);const r={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},s={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class u{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=a(r,i,t+1/3),this.g=a(r,i,t),this.b=a(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=r[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new h.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}r.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)a&&t>g?tg?a1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),r=n(1),s=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=s.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=r.xb,this.side=r.H,this.vertexColors=!1,this.opacity=1,this.format=r.Ib,this.transparent=!1,this.blendSrc=r.Nc,this.blendDst=r.Db,this.blendEquation=r.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=r.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=r.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=r.R,this.stencilZFail=r.R,this.stencilZPass=r.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===r.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==r.xb&&(n.blending=this.blending),this.side!==r.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==r.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(29);class r{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),r=n(2),s=n(18),o=n(39),a=n(5),l=n(10),c=n(40),u=n(1),h=n(27),d=n(7);const p=new a.a,_=new o.a,m=new s.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,E=new r.a,M=new r.a,S=new r.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new h.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(r),!1===t.ray.intersectsSphere(m))return;if(p.copy(r).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,s,f,g,v,C);if(O){h&&(E.fromBufferAttribute(h,p),M.fromBufferAttribute(h,_),S.fromBufferAttribute(h,m),O.uv=c.a.getUV(C,f,g,v,E,M,S,new r.a)),d&&(E.fromBufferAttribute(d,p),M.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,E,M,S,new r.a));const t={a:p,b:_,c:m,normal:new 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s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}o.prototype.isMeshBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(8),r=n(0),s=n(5),o=n(3);const a=new s.a,l=new i.a;class c{constructor(t=0,e=0,n=0,i=c.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],a=i[8],l=i[1],c=i[5],u=i[9],h=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(o.d)(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-u,p),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-h,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-h,p),this._z=Math.atan2(-s,c)):(this._y=0,this._z=Math.atan2(l,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,r)):(this._x=0,this._z=Math.atan2(-s,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-u,c),this._y=Math.atan2(-h,r)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-u,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return a.makeRotationFromQuaternion(t),this.setFromRotationMatrix(a,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return l.setFromEuler(this),this.setFromQuaternion(l,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new r.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}c.prototype.isEuler=!0,c.DefaultOrder=\\\\\\\"XYZ\\\\\\\",c.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let r,s=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===s&&void 0!==i.onStart&&i.onStart(t,o,a),s=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||a.getNormalMatrix(t),i=this.coplanarPoint(s).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}l.prototype.isPlane=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(41),r=n(0),s=n(4);const o=new r.a,a=new r.a;class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;t0)if(p=l*u-c,_=l*c-u,f=r*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*u)+h}else _=r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _=-r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _<=-f?(p=Math.max(0,-(-l*r+c)),_=p>0?-r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h):_<=f?(p=0,_=Math.min(Math.max(-r,-u),r),m=_*(_+2*u)+h):(p=Math.max(0,-(l*r+c)),_=p>0?r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h);else _=l>0?-r:r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(s),m}intersectSphere(t,e){r.subVectors(t.center,this.origin);const n=r.dot(this.direction),i=r.dot(r)-n*n,s=t.radius*t.radius;if(i>s)return null;const o=Math.sqrt(s-i),a=n-o,l=n+o;return a<0&&l<0?null:a<0?this.at(l,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,r)}intersectTriangle(t,e,n,i,r){l.subVectors(e,t),c.subVectors(n,t),u.crossVectors(l,c);let s,o=this.direction.dot(u);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}a.subVectors(this.origin,t);const h=s*this.direction.dot(c.crossVectors(a,c));if(h<0)return null;const d=s*this.direction.dot(l.cross(a));if(d<0)return null;if(h+d>o)return null;const p=-s*a.dot(u);return p<0?null:this.at(p/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const r=new i.a,s=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),r.subVectors(t,e),i.cross(r);const s=i.lengthSq();return s>0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){r.subVectors(i,e),s.subVectors(n,e),o.subVectors(t,e);const l=r.dot(r),c=r.dot(s),u=r.dot(o),h=s.dot(s),d=s.dot(o),p=l*h-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(h*u-c*d)*_,f=(l*d-c*u)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,r,s,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(r,a.x),l.addScaledVector(s,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return r.subVectors(n,e),s.subVectors(t,e),r.cross(s).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return r.subVectors(this.c,this.b),s.subVectors(this.a,this.b),.5*r.cross(s).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return _.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;l.subVectors(i,n),c.subVectors(r,n),h.subVectors(t,n);const a=l.dot(h),_=c.dot(h);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return s=a/(a-m),e.copy(n).addScaledVector(l,s);p.subVectors(t,r);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(r);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return u.subVectors(r,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(u,o);const w=1/(b+x+g);return s=x*w,o=g*w,e.copy(n).addScaledVector(l,s).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),r=n(39),s=n(5),o=n(10),a=n(0),l=n(24),c=n(7),u=n(4);const h=new a.a,d=new a.a,p=new s.a,_=new r.a,m=new i.a;class f extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;tl)continue;d.applyMatrix4(this.matrixWorld);const a=t.ray.origin.distanceTo(d);at.far||e.push({distance:a,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;nl)continue;d.applyMatrix4(this.matrixWorld);const 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super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}a.prototype.isMeshPhysicalMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));const i=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",r=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),s=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",o=\\\\\\\"[^\\\\\\\"+i.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",a=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",s),l=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",o),c=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",s),u=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",s),h=new RegExp(\\\\\\\"^\\\\\\\"+a+l+c+u+\\\\\\\"$\\\\\\\"),d=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class p{constructor(t,e,n){this.path=e,this.parsedPath=n||p.parseTrackName(e),this.node=p.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new p.Composite(t,e,n):new p(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(r,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=h.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==d.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;ii.length-2?i.length-1:a+1],d=i[a>i.length-3?i.length-1:a+2];return n.set(Object(r.a)(l,c.x,u.x,h.x,d.x),Object(r.a)(l,c.y,u.y,h.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;ethis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return r.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}s.prototype.isBox2=!0;var o=n(7),a=n(21),l=n(4),c=n(13),u=n(11),h=n(47),d=n(46),p=n(48),_=n(53),m=n(0);class f extends c.a{constructor(t){super(t),this.defaultDPI=90,this.defaultUnit=\\\\\\\"px\\\\\\\"}load(t,e,n,i){const r=this,s=new a.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(n))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this;function n(t,e,n,i,s,o,a,l){if(0==e||0==n)return void t.lineTo(l.x,l.y);i=i*Math.PI/180,e=Math.abs(e),n=Math.abs(n);const c=(a.x-l.x)/2,u=(a.y-l.y)/2,h=Math.cos(i)*c+Math.sin(i)*u,d=-Math.sin(i)*c+Math.cos(i)*u;let p=e*e,_=n*n;const m=h*h,f=d*d,g=m/p+f/_;if(g>1){const t=Math.sqrt(g);p=(e*=t)*e,_=(n*=t)*n}const v=p*f+_*m,y=(p*_-v)/v;let x=Math.sqrt(Math.max(0,y));s===o&&(x=-x);const b=x*e*d/n,w=-x*n*h/e,T=Math.cos(i)*b-Math.sin(i)*w+(a.x+l.x)/2,A=Math.sin(i)*b+Math.cos(i)*w+(a.y+l.y)/2,E=r(1,0,(h-b)/e,(d-w)/n),M=r((h-b)/e,(d-w)/n,(-h-b)/e,(-d-w)/n)%(2*Math.PI);t.currentPath.absellipse(T,A,e,n,E,E+M,0===o,i)}function r(t,e,n,i){const r=t*n+e*i,s=Math.sqrt(t*t+e*e)*Math.sqrt(n*n+i*i);let o=Math.acos(Math.max(-1,Math.min(1,r/s)));return t*i-e*n<0&&(o=-o),o}function s(t,e){e=Object.assign({},e);let n={};if(t.hasAttribute(\\\\\\\"class\\\\\\\")){const e=t.getAttribute(\\\\\\\"class\\\\\\\").split(/\\\\s/).filter(Boolean).map((t=>t.trim()));for(let t=0;t=0;t--){const r=i[t].trim();if(\\\\\\\"\\\\\\\"===r)continue;const s=r.indexOf(\\\\\\\"(\\\\\\\"),o=r.length;if(s>0&&s=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.translate(t,i)}break;case\\\\\\\"rotate\\\\\\\":if(e.length>=1){let t=0,i=0,r=0;t=-e[0]*Math.PI/180,e.length>=3&&(i=e[1],r=e[2]),b.identity().translate(-i,-r),w.identity().rotate(t),T.multiplyMatrices(w,b),b.identity().translate(i,r),n.multiplyMatrices(b,T)}break;case\\\\\\\"scale\\\\\\\":if(e.length>=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.scale(t,i)}break;case\\\\\\\"skewX\\\\\\\":1===e.length&&n.set(1,Math.tan(e[0]*Math.PI/180),0,0,1,0,0,0,1);break;case\\\\\\\"skewY\\\\\\\":1===e.length&&n.set(1,0,0,Math.tan(e[0]*Math.PI/180),1,0,0,0,1);break;case\\\\\\\"matrix\\\\\\\":6===e.length&&n.set(e[0],e[2],e[4],e[1],e[3],e[5],0,0,1)}}e.premultiply(n)}}return e}(t);y.length>0&&e.premultiply(y[y.length-1]);return M.copy(e),y.push(e),e}(e);let c=!0,m=null;switch(e.nodeName){case\\\\\\\"svg\\\\\\\":break;case\\\\\\\"style\\\\\\\":!function(t){if(!t.sheet||!t.sheet.cssRules||!t.sheet.cssRules.length)return;for(let e=0;et.trim()));for(let t=0;t0&&(r.copy(l),e.currentPath.currentPoint.copy(r),c=!0);break;default:console.warn(i)}u=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new p.a;l.moveTo(e+i,n),l.lineTo(e+s-i,n),(0!==i||0!==r)&&l.bezierCurveTo(e+s-i*a,n,e+s,n+r*a,e+s,n+r);l.lineTo(e+s,n+o-r),(0!==i||0!==r)&&l.bezierCurveTo(e+s,n+o-r*a,e+s-i*a,n+o,e+s-i,n+o);l.lineTo(e+i,n+o),(0!==i||0!==r)&&l.bezierCurveTo(e+i*a,n+o,e,n+o-r*a,e,n+o-r);l.lineTo(e,n+r),(0!==i||0!==r)&&l.bezierCurveTo(e,n+r*a,e+i*a,n,e+i,n);return l}(e);break;case\\\\\\\"polygon\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!0,i}(e);break;case\\\\\\\"polyline\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),r=new h.a;r.absarc(e,n,i,0,2*Math.PI);const s=new p.a;return s.subPaths.push(r),s}(e);break;case\\\\\\\"ellipse\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),s=new h.a;s.absellipse(e,n,i,r,0,2*Math.PI);const o=new p.a;return o.subPaths.push(s),o}(e);break;case\\\\\\\"line\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),s=new p.a;return s.moveTo(e,n),s.lineTo(i,r),s.currentPath.autoClose=!1,s}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":r=s(e,r);const l=e.href.baseVal.substring(1),u=e.viewportElement.getElementById(l);u?t(u,r):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==r.fill&&\\\\\\\"none\\\\\\\"!==r.fill&&m.color.setStyle(r.fill),function(t,e){function n(t){E.set(t.x,t.y,1).applyMatrix3(e),t.set(E.x,E.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),r=t.subPaths;for(let t=0,s=r.length;t0?M.copy(y[y.length-1]):M.identity())}(S.documentElement,{fill:\\\\\\\"#000\\\\\\\",fillOpacity:1,strokeOpacity:1,strokeWidth:1,strokeLineJoin:\\\\\\\"miter\\\\\\\",strokeLineCap:\\\\\\\"butt\\\\\\\",strokeMiterLimit:4});return{paths:g,xml:S.documentElement}}static createShapes(t){const e=999999999,n=0,r=1,o=2,a=3,l=4,c=5,u=6,p={loc:n,t:0};function m(t,e,i,r){const s=t.x,a=e.x,l=i.x,c=r.x,u=t.y,h=e.y,d=i.y,_=r.y,m=(c-l)*(u-d)-(_-d)*(s-l),g=(_-d)*(a-s)-(c-l)*(h-u),v=m/g,y=((a-s)*(u-d)-(h-u)*(s-l))/g;if(0===g&&0!==m||v<=0||v>=1||y<0||y>1)return null;if(0===m&&0===g){for(let l=0;l<2;l++){if(f(0===l?i:r,t,e),p.loc==n){const t=0===l?i:r;return{x:t.x,y:t.y,t:p.t}}if(p.loc==o){return{x:+(s+p.t*(a-s)).toPrecision(10),y:+(u+p.t*(h-u)).toPrecision(10),t:p.t}}}return null}for(let s=0;s<2;s++)if(f(0===s?i:r,t,e),p.loc==n){const t=0===s?i:r;return{x:t.x,y:t.y,t:p.t}}return{x:+(s+v*(a-s)).toPrecision(10),y:+(u+v*(h-u)).toPrecision(10),t:v}}function f(t,e,i){const s=i.x-e.x,h=i.y-e.y,d=t.x-e.x,_=t.y-e.y,m=s*_-d*h;if(t.x===e.x&&t.y===e.y)return p.loc=n,void(p.t=0);if(t.x===i.x&&t.y===i.y)return p.loc=r,void(p.t=1);if(m<-Number.EPSILON)return void(p.loc=a);if(m>Number.EPSILON)return void(p.loc=l);if(s*d<0||h*_<0)return void(p.loc=c);if(Math.sqrt(s*s+h*h){if(e.boundingBox.containsPoint(r)){(function(t,e){const n=[],r=[];for(let s=1;st.t<=s.t+Number.EPSILON&&t.t>=s.t-Number.EPSILON))&&(n.push(s),r.push(new i.a(s.x,s.y)))}}return r})(t,e.points).forEach((t=>{s.push({identifier:e.identifier,isCW:e.isCW,point:t})}))}})),s.sort(((t,e)=>t.point.x-e.point.x)),s}let v=0,y=e,x=-999999999,b=t.subPaths.map((t=>{const n=t.getPoints();let r=-999999999,o=e,a=-999999999,l=e;for(let t=0;tr&&(r=e.y),e.ya&&(a=e.x),e.x=l&&(y=l-1),{points:n,isCW:_.a.isClockWise(n),identifier:v++,boundingBox:new s(new i.a(l,o),new i.a(a,r))}}));b=b.filter((t=>t.points.length>1));const w=b.map((e=>function(t,e,n,r,s){null!=s&&\\\\\\\"\\\\\\\"!==s||(s=\\\\\\\"nonzero\\\\\\\");const o=new i.a;t.boundingBox.getCenter(o);const a=g([new i.a(n,o.y),new i.a(r,o.y)],t.boundingBox,e);a.sort(((t,e)=>t.point.x-e.point.x));const l=[],c=[];a.forEach((e=>{e.identifier===t.identifier?l.push(e):c.push(e)}));const u=l[0].point.x,h=[];let d=0;for(;d0&&h[h.length-1]===c[d].identifier?h.pop():h.push(c[d].identifier),d++;if(h.push(t.identifier),\\\\\\\"evenodd\\\\\\\"===s){const e=h.length%2==0,n=h[h.length-2];return{identifier:t.identifier,isHole:e,for:n}}if(\\\\\\\"nonzero\\\\\\\"===s){let n=!0,i=null,r=null;for(let t=0;t{if(!w[t.identifier].isHole){const e=new d.a(t.points);w.filter((e=>e.isHole&&e.for===t.identifier)).forEach((t=>{const n=b[t.identifier];e.holes.push(new h.a(n.points))})),T.push(e)}})),T}static getStrokeStyle(t,e,n,i,r){return{strokeColor:e=void 0!==e?e:\\\\\\\"#000\\\\\\\",strokeWidth:t=void 0!==t?t:1,strokeLineJoin:n=void 0!==n?n:\\\\\\\"miter\\\\\\\",strokeLineCap:i=void 0!==i?i:\\\\\\\"butt\\\\\\\",strokeMiterLimit:r=void 0!==r?r:4}}static pointsToStroke(t,e,n,i){const r=[],s=[],a=[];if(0===f.pointsToStrokeWithBuffers(t,e,n,i,r,s,a))return null;const c=new o.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new l.c(r,3)),c.setAttribute(\\\\\\\"normal\\\\\\\",new l.c(s,3)),c.setAttribute(\\\\\\\"uv\\\\\\\",new l.c(a,2)),c}static pointsToStrokeWithBuffers(t,e,n,r,s,o,a,l){const c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a,_=new i.a,m=new i.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,E=new i.a;n=void 0!==n?n:12,r=void 0!==r?r:.001,l=void 0!==l?l:0;const M=(t=function(t){let e=!1;for(let n=1,i=t.length-1;n=r&&n.push(t[e]);return n.push(t[t.length-1]),n}(t)).length;if(M<2)return 0;const S=t[0].equals(t[M-1]);let C,N,L=t[0];const O=e.strokeWidth/2,R=1/(M-1);let P,I,F,D,k=0,B=!1,z=0,U=3*l,G=2*l;V(t[0],t[1],c).multiplyScalar(O),f.copy(t[0]).sub(c),g.copy(t[0]).add(c),v.copy(f),y.copy(g);for(let 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l{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((t,e)=>{console.log(e,t,t.graphPredecessors(),t.graphSuccessors())}))}setScene(t){this._scene=t}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(t){const e=[];for(let n of t){const t=this.nodeFromId(n);t&&e.push(t)}return e}nodeFromId(t){return this._nodes_by_id.get(t)}hasNode(t){return null!=this._nodes_by_id.get(t.graphNodeId())}addNode(t){this._nodes_by_id.set(t.graphNodeId(),t),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(t.graphNodeId(),t)}removeNode(t){this._nodes_by_id.delete(t.graphNodeId()),this._successors.delete(t.graphNodeId()),this._predecessors.delete(t.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(t.graphNodeId())}nodesCount(){return this._nodesCount}connect(t,e,n=!0){const i=t.graphNodeId(),r=e.graphNodeId();if(this.hasNode(t)&&this.hasNode(e)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let e=!1;return n&&(e=this._hasPredecessor(i,r)),!e&&(this._createConnection(i,r),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${r}`),!1}disconnect(t,e){this._remove_connection(t.graphNodeId(),e.graphNodeId()),t.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(t){const e=this.predecessors(t);for(let n of e)this.disconnect(n,t)}disconnectSuccessors(t){const e=this.successors(t);for(let n of e)this.disconnect(t,n)}predecessorIds(t){const e=this._predecessors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}predecessors(t){const e=this.predecessorIds(t.graphNodeId());return this.nodesFromIds(e)}successorIds(t){const e=this._successors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}successors(t){const e=this.successorIds(t.graphNodeId())||[];return this.nodesFromIds(e)}allPredecessorIds(t){return this.allNextIds(t,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(t){return this.allNextIds(t,\\\\\\\"successorIds\\\\\\\")}allPredecessors(t){const e=this.allPredecessorIds(t);return this.nodesFromIds(e)}allSuccessors(t){const e=this.allSuccessorIds(t);return this.nodesFromIds(e)}_createConnection(t,e){let n=this._successors.get(t);if(n||(n=new Set,this._successors.set(t,n)),n.has(e))return;n.add(e);let i=this._predecessors.get(e);i||(i=new Set,this._predecessors.set(e,i)),i.add(t)}_remove_connection(t,e){let n=this._successors.get(t);n&&(n.delete(e),0==n.size&&this._successors.delete(t));let i=this._predecessors.get(e);i&&(i.delete(t),0==i.size&&this._predecessors.delete(e))}allNextIds(t,e){const n=new Map,i=[];let r=this[e](t.graphNodeId());for(;r.length>0;){const t=[];for(let n of r)for(let i of this[e](n))t.push(i);for(let t of r)n.set(t,!0);for(let e of t)r.push(e);r=t}return n.forEach(((t,e)=>{i.push(e)})),i}_hasPredecessor(t,e){const n=this.predecessorIds(t);if(n){if(n.includes(e))return!0;for(let t of n)return this._hasPredecessor(t,e)}return!1}}class c{constructor(t){this._node=t,this._cooks_count=0,this._total_cook_time=0,this._total_inputs_time=0,this._total_params_time=0}update_cook_data(t){this._cooks_count+=1,this._total_cook_time+=t.cookTime,this._total_inputs_time+=t.inputsTime,this._total_params_time+=t.paramsTime}total_time(){return this._total_cook_time+this._total_inputs_time+this._total_params_time}total_cook_time(){return this._total_cook_time}cook_time_per_iteration(){return this._cooks_count>0?this._total_cook_time/this._cooks_count:0}total_inputs_time(){return this._total_inputs_time}inputs_time_per_iteration(){return this._cooks_count>0?this._total_inputs_time/this._cooks_count:0}total_params_time2(){return this._total_params_time}params_time_per_iteration2(){return this._cooks_count>0?this._total_params_time/this._cooks_count:0}cooks_count(){return 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h.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return h.difference(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static toSet(t){const e=new Set;for(let n of t)e.add(n);return e}static isEqual(t,e){if(t.length!=e.length)return!1;const n=t.length;for(let i=0;i{r[s]=t,s++})),m.isString(r[0])?r.sort():r.sort(((t,e)=>t-e));const o=new Array(t.length);s=0;for(let t of r){const e=n.get(t);if(e)for(let t of e)o[s]=t,s++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),r=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),u.push(a/f),u.push(1-s/g),M+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}F.prototype.isShaderMaterial=!0;var D=n(6),k=n(14),B=\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\\\n\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\\\n\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\\\n\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t// y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim := ( 4 , 2 )\\\\n\\\\t\\\\t// # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\";const z={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:B,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\",transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\n\\\\t// modulation\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshnormal_vert:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",meshnormal_frag:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\nuniform float opacity;\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n#endif\\\\n}\\\\n\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};var U=n(11);const G={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new U.a},uv2Transform:{value:new U.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}}},V={basic:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.fog]),vertexShader:z.meshbasic_vert,fragmentShader:z.meshbasic_frag},lambert:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.fog,G.lights,{emissive:{value:new D.a(0)}}]),vertexShader:z.meshlambert_vert,fragmentShader:z.meshlambert_frag},phong:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.fog,G.lights,{emissive:{value:new D.a(0)},specular:{value:new 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wt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new ot({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#define 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r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Ct(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=St(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<_t.length;t++)_t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(vt),t.scissorTest=!1,Mt(t,0,0,t.width,t.height)}_fromTexture(t){vt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:w.ob,minFilter:w.ob,generateMipmaps:!1,type:w.Zc,format:w.hc,encoding:Tt(t)?t.encoding:w.gc,depthBuffer:!1},n=Et(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Et(e),n}_compileMaterial(t){const e=new k.a(_t[0],t);this._renderer.compile(e,pt)}_sceneToCubeUV(t,e,n,i){const r=new K.a(90,1,e,n),s=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,l=a.autoClear,c=a.outputEncoding,u=a.toneMapping;a.getClearColor(gt),a.toneMapping=w.vb,a.outputEncoding=w.U,a.autoClear=!1;const h=new at.a({name:\\\\\\\"PMREM.Background\\\\\\\",side:w.i,depthWrite:!1,depthTest:!1}),d=new k.a(new N,h);let p=!1;const _=t.background;_?_.isColor&&(h.color.copy(_),t.background=null,p=!0):(h.color.copy(gt),p=!0);for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(o[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,o[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,o[e])),Mt(i,n*lt,e>2?lt:0,lt,lt),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=dt[w.U]:t.value=dt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Ct()):null==this._equirectShader&&(this._equirectShader=St());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new k.a(_t[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),Mt(e,0,0,3*lt,2*lt),n.setRenderTarget(e),n.render(r,pt)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eht&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,pt)}}function Tt(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function At(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=ct[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Z(3*lt,3*lt,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function Mt(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function St(){const t=new d.a(1,1);return new ot({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Ct(){return new ot({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===w.D||s===w.E,a=s===w.o||s===w.p;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const r=n.morphAttributes;for(const n in r){const i=r[n];for(let n=0,r=i.length;ne.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const p=new Float32Array(u*h*4*i),_=new kt(p,u,h,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const r=e*n;let s=$t[r];if(void 0===s&&(s=new Float32Array(r),$t[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function ee(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function sn(t){return t.replace(rn,on)}function on(t,e){const n=z[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return sn(n)}const an=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,ln=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function cn(t){return t.replace(ln,hn).replace(an,un)}function un(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. 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i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[dn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define 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USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define 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USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define 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y=v+f+o,x=v+g+a,b=Xe(r,r.VERTEX_SHADER,y),T=Xe(r,r.FRAGMENT_SHADER,x);if(r.attachShader(m,b),r.attachShader(m,T),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(b).trim(),n=r.getShaderInfoLog(T).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,r.LINK_STATUS)){i=!1;const e=Je(r,b,\\\\\\\"vertex\\\\\\\"),n=Je(r,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,r.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info 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0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||fn),r.length>1&&r.sort(e||gn),s.length>1&&s.sort(e||gn)}}}function yn(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new vn(t),e.set(n,[r])):i>=e.get(n).length?(r=new vn(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function xn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let bn=0;function wn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function Tn(t,e){const n=new xn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new p.a);const s=new p.a,o=new A.a,a=new A.a;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(wn);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_FLOAT_1,r.rectAreaLTC2=G.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_HALF_1,r.rectAreaLTC2=G.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=bn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new An(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class Mn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Mn.prototype.isMeshDepthMaterial=!0;class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Sn.prototype.isMeshDistanceMaterial=!0;function Cn(t,e,n){let i=new T.a;const r=new d.a,s=new d.a,o=new _.a,a=new Mn({depthPacking:w.Hb}),l=new Sn,c={},u=n.maxTextureSize,h={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new k.a(f,p),v=this;function y(n,i){const r=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,m,g,null)}function x(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,u===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:h[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function b(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let 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0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function zn(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(Pt.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,R=e.width,P=e.height,I=1,F=null,D=null;const k=new _.a(0,0,R,P),B=new _.a(0,0,R,P);let z=!1;const U=[],G=new T.a;let V=!1,X=!1,$=null;const J=new A.a,Q=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ut,ht,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Et=n;function Mt(t,n){for(let i=0;i0&&function(t,e,n){if(null===$){const t=!0===o&&!0===nt.isWebGL2;$=new(t?Vt:Z)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget($),g.clear();const r=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=r,at.updateMultisampleRenderTarget($),at.updateRenderTargetMipmap($),g.setRenderTarget(i)}(r,e,n),i&&it.viewport(N.copy(i)),r.length>0&&Xt(r,e,n),s.length>0&&Xt(s,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?f[f.length-1]:null,m.pop(),h=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 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Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Et.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Et.RGB&&(o=Et.RGB8),o===Et.RGBA&&(o=Et.RGBA8)),at.setTexture2D(e,0),Et.copyTexImage2D(Et.TEXTURE_2D,n,o,t.x,t.y,r,s,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,n.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?Et.compressedTexSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Et.generateMipmap(Et.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let u;if(i.isDataTexture3D)at.setTexture3D(i,0),u=Et.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),u=Et.TEXTURE_2D_ARRAY}Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,i.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,i.unpackAlignment);const h=Et.getParameter(Et.UNPACK_ROW_LENGTH),d=Et.getParameter(Et.UNPACK_IMAGE_HEIGHT),p=Et.getParameter(Et.UNPACK_SKIP_PIXELS),_=Et.getParameter(Et.UNPACK_SKIP_ROWS),m=Et.getParameter(Et.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Et.pixelStorei(Et.UNPACK_ROW_LENGTH,f.width),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,f.height),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,t.min.x),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,t.min.y),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Et.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,f.data)):Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f),Et.pixelStorei(Et.UNPACK_ROW_LENGTH,h),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,d),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,p),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,_),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,m),0===r&&i.generateMipmaps&&Et.generateMipmap(u),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Un={};var Gn,Vn,Hn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Gn||(Gn={}));class jn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Gn.WEBGL2))}createWebGLRenderer(t){const e=new zn(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Gn.WEBGL2:Gn.WEBGL;let i=t.getContext(n,Un);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Gn.EXPERIMENTAL_WEBGL2:Gn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Un)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Vt(t,e,n):new Z(t,e,n)}}class Wn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class qn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(t,e,n){const i=t.context(),r=t.type().toLowerCase();let s=this._node_register.get(i);s||(s=new Map,this._node_register.set(i,s));if(s.get(r))console.error(`node ${i}/${r} already registered`);else{if(s.set(r,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(r,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(r,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,r;null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(r=this._node_register_options.get(t))||void 0===r||r.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&null!=n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const r=n.type().toLowerCase(),s=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(r);if(s){const n=s.only,i=s.except,r=`${t}/${e}`;return n?n.includes(r):!i||!i.includes(r)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Xn{constructor(t){this.poly=t,this._operation_register=new Map}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=t.type().toLowerCase();if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return n.get(e.toLowerCase())}}class Yn extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Vn||(Vn={}));class $n extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Zn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Qn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var Kn=n(86);class ti{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,ai.playerMode()),ai.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(Kn.a)(t)}}class ei{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],r=this._map[n];return r?i?`${r}?${i}`:r:void 0}}var ni=n(93),ii=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:r,unzippedData:s}=e,o=Object.keys(r);for(let t of o){const e=s[`assets/${r[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:ai.blobs.createBlobUrl(n)};ai.blobs.registerBlobUrl(i)}ai.setPlayerMode(!0),ai.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=ai;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,s)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[ii.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let r=document.getElementById(t.scriptElementId);const s=[];return s.push(`import {configurePolygonjs, configureScene} from '${i}';`),s.push(`configurePolygonjs(window.${t.Poly});`),s.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),s.push(`delete window.${t.loadSceneArgs};`),r||(r=document.createElement(\\\\\\\"script\\\\\\\"),r.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),r.text=s.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(r)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const r=new n(e),s=await r.scene();i&&i(s);const o=s.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);s.play(),t.scene=s,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ni.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(Kn.a)(i)}}class si{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class oi{constructor(){this.renderersController=new jn,this.nodesRegister=new qn(this),this.operationsRegister=new Xn(this),this.expressionsRegister=new Yn,this.modulesRegister=new $n,this.assemblersRegister=new Jn,this.pluginsRegister=new Zn(this),this.camerasRegister=new Qn(this),this.blobs=new ti,this.assetUrls=new ei,this.selfContainedScenesLoader=new ri,this.performance=new si,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new oi;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new Wn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const ai=oi._instance_();class li{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=ai.performance.performanceManager(),i=n.now();e();const r=n.now()-i;console.log(`${t}: ${r}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=ai.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const r=t[e];n.push(r),null==i[r]&&(i[r]=[]),i[r].push(e)}n.sort(((t,e)=>t-e));const r=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const s=[];for(let t of r){const n=i[t];for(let i of n){const n=e[i],r={duration:t,name:i,count:n,duration_per_iteration:t/n};s.push(r)}}return console.table(s),s}}class ci{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class hi{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class di{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var pi;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(pi||(pi={}));const _i=[pi.LOADED,pi.PLAY,pi.PAUSE,pi.TICK];class mi extends di{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}}class fi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(pi.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolve_missing_references(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const gi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class vi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=xi.findNode(t,this._path)}clone(){const t=new vi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=xi.findParam(t,this._path)}clone(){const t=new yi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class xi{static split_parent_child(t){const e=t.split(xi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(xi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR).filter((t=>t.length>0)),r=i[0];let s=null;if(e[0]!==xi.SEPARATOR){switch(r){case xi.PARENT:null==n||n.add_path_element(r),s=t.parent();break;case xi.CURRENT:null==n||n.add_path_element(r),s=t;break;default:s=t.node(r),s&&(null==n||n.add_node(r,s))}if(null!=s&&i.length>1){const t=i.slice(1).join(xi.SEPARATOR);s=this.findNode(s,t,n)}return s}{const i=e.substr(1);s=this.findNode(t.root(),i,n)}return s}static findParam(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(xi.SEPARATOR),r=this.findNode(t,e,n);if(null!=r){const t=i[i.length-1],e=r.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let r=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(xi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)s[l]||a.push(t),l++;return`${r}${a.join(xi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),r=Math.min(n.length,i.length);let s=null;for(let t=0;t{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();if(e){if(this.scene.node(e))return!0;{const t=xi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}}class Ti{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=ai.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Ai{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ti(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Mi{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Si{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ci extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Ni extends Si{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Li extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Oi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Ni(this.node)}hasDisplay(){return!0}}}function Pi(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ci(this.node)}hasBypass(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.optimize=new Li(this.node)}hasOptimize(){return!0}}}class Fi extends(Ri(Oi)){}class Di extends(Pi(Oi)){}class ki extends(Pi(Ri(Oi))){}class Bi extends(Ii(Pi(Oi))){}class zi extends(Ii(Pi(Ri(Oi)))){}class Ui{constructor(t){this.node=t}}class Gi extends Ui{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Vi extends Ui{set(t){this._message!=t&&(t&&ai.warn(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class Hi{constructor(t){this.node=t,this.timeDependent=new Gi(this.node),this.error=new Vi(this.node)}}class ji{constructor(t){this.node=t,this._graph_node=new Ai(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class Wi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(ji.base_name(this.node)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(t){const e=xi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==xi.CURRENT||t==xi.CURRENT_WITH_SLASH)return this.node;if(t==xi.PARENT||t==xi.PARENT_WITH_SLASH)return this.node.parent();const e=xi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return xi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const qi=/[, ]/,Xi=/\\\\d+$/,Yi=/^0+/,$i=/,| /,Ji=/^-?\\\\d+\\\\.?\\\\d*$/;var Zi,Qi,Ki,tr,er,nr,ir,rr;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Zi||(Zi={}));class sr{static isBoolean(t){return t==Zi.TRUE||t==Zi.FALSE}static toBoolean(t){return t==Zi.TRUE}static isNumber(t){return Ji.test(t)}static tailDigits(t){const e=t.match(Xi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Xi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const r=n.match(Yi);r&&(i=r[0]);const s=parseInt(n);0==s&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${s+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)sr.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(qi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let r=0;return n.forEach((t=>{i[r]=t,r++})),i}static indices(t){const e=t.split($i);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class or{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Qi||(Qi={}));class ar{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class lr{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=1:e;if(m.isString(e)){if(n){if(n instanceof vi)return n.set_path(e);if(n instanceof yi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new cr(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t{n[i]=t[e]}));for(let i=0;i=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class dr{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(Ki||(Ki={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(tr||(tr={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(er||(er={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(nr||(nr={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ir||(ir={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rr||(rr={}));const pr=[rr.DEVICE_ORIENTATION,rr.MAP,rr.ORBIT,rr.FIRST_PERSON,rr.MOBILE_JOYSTICK];class _r{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var mr=n(69);class fr extends Q.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}fr.prototype.isScene=!0;var gr=n(49),vr=n(52),yr=n(42),xr=n(55),br=n(61),wr=n(24),Tr=n(35);const Ar=new p.a,Er=new p.a;class Mr extends Q.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t0){let n,i;for(n=1,i=e.length;n0){Ar.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ar);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ar.setFromMatrixPosition(t.matrixWorld),Er.setFromMatrixPosition(this.matrixWorld);const n=Ar.distanceTo(Er)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;in?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,r){return(t-e)/(n-e)*(r-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*is}static radians_to_degrees(t){return t/is}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),r=this.deg2rad(e.lat-t.lat),s=this.deg2rad(e.lng-t.lng),o=Math.sin(r/2)*Math.sin(r/2)+Math.cos(n)*Math.cos(i)*Math.sin(s/2)*Math.sin(s/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}rs.Easing=ns,rs.fract=t=>t-Math.floor(t),rs._vec={x:0,y:136574},rs._triangle_mid=new p.a,rs._triangle_mid_to_corner=new p.a;class ss{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Zr(this._core_geometry,e[n+0]),new Zr(this._core_geometry,e[n+1]),new Zr(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new Qr.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}random_position(t){let e=[rs.randFloat(t),rs.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attrib_value_at_position(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),r=this._geometry.attributes[t].itemSize,s=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(r){case 1:a=0;for(let t of s)a+=t*i[l],l++;o=a;break;default:for(let t of s){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolated_value(t,e,n,i){const r=[e.a,e.b,e.c],s=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=r.map((t=>new p.a(s[3*t+0],s[3*t+1],s[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=r.map((t=>l[t]));break;case 2:c=r.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=r.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const u=r.map(((t,e)=>n.distanceTo(o[e]))),h=f.sum([u[0]*u[1],u[0]*u[2],u[1]*u[2]]),_=[u[1]*u[2]/h,u[0]*u[2]/h,u[0]*u[1]/h];let m;switch(a){case 1:m=f.sum(r.map(((t,e)=>_[e]*c[e])));break;default:var g=r.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class os{from_points(t){t=this._filter_points(t);const e=new S.a,n=new ps(e),i=t[0];if(null!=i){const r=i.geometry(),s=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),s={};r.forEach(((t,e)=>s[t]=e)),n.userDataAttribs()[i]=r;const o=[];for(let e of t){const t=s[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,r=new Array(t.length*n);switch(n){case 1:for(let e=0;enew ps(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},r=[];for(let i of e){const e=i.points();for(let i of e){r.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const s=Object.keys(n);for(let e of r){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=s}const r=cs(t),s=new ps(r);return Object.keys(i).forEach((t=>{const e=i[t];s.setIndexedAttributeValues(t,e)})),r&&delete r.userData.mergedUserData,r}}class ps{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return ps.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=Wr.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=Wr.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=Wr.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Dr.STRING:Dr.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return ps.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=sr.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)sr.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=Wr.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let r=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew ss(this,t)))}}var _s;ps._mesh_builder=new class extends os{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],r=i.length;let s,o,a;for(let t=0;t{};class fs{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,r,s,o,a)=>{n(e,i,r,s,o,a,t)})}t.onBeforeRender=ms}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,r=n.customMaterials[i];r&&(t[i]=r,r.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e]=s.uniforms[e]||n)}}}const gs=\\\\\\\"name\\\\\\\";class vs extends qr{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new ps(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=sr.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),r=i[0];if(\\\\\\\"@\\\\\\\"==r[0]){const t=r.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,r=t.userData;r.attributes=r.attributes||{},r.attributes[e]=i}addAttribute(t,e){vs.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=Wr.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==gs)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==gs?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return vs.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return vs.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(gs)}humanType(){return Vr.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?Dr.STRING:Dr.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:Wr.attribSizeFromValue(e)}clone(){return vs.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return vs.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const r=n.get(e),s=e;if(s.geometry){const t=r.geometry;s.geometry=ps.clone(t);const e=s.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(s.material){s.material=r.material,fs.applyCustomMaterials(e,s.material);const t=s.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(r.userData));const o=r;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=r,u=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=u.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new ps(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class xs{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=ys[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const bs=ai.performance.performanceManager();class ws{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=bs.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=bs.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=bs.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=bs.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=bs.now())}recordCookEnd(){this.active()&&(this._data.cookTime=bs.now()-this._cook_start,this._cooksCount+=1)}}class Ts{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new ws(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),ai.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?ai.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(ai.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let r;for(let s=0;st.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Es,Ms;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Es||(Es={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Ms||(Ms={}));const Ss=\\\\\\\"dependentOnFoundNode\\\\\\\",Cs=\\\\\\\"visibleIf\\\\\\\";var Ns,Ls;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Ns||(Ns={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Ls||(Ls={}));class Os{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Ss in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:tObject.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Ai(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Ms.VISIBLE_UPDATED)}}}class Rs{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Ps{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Is{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Fs{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&ai.warn(this.param.path(),this._message),this.param.emitController.emit(Ms.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Ds{constructor(t){this.param=t,this.timeDependent=new Is(this.param),this.error=new Fs(this.param)}}class ks extends Ai{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.update_from_method_dependency_name_change()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const r=m.isNumber(e),s=new ks(t,e,i);if(n)s.set_resolved_graph_node(n);else if(!r){const t=e;s.set_unresolved_path(t)}return s}}const Bs=[];class zs extends Ai{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Os(this),this._emit_controller=new Rs(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Os(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rs(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ps(this)}get states(){return this._states=this._states||new Ds(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof ks&&t.dispose();null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Es.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Ms.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=xi.relativePath(t,this.node);return e.length>0?`${e}${xi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return Bs}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Us=n(96),Gs=n.n(Us);Gs.a.addUnaryOp(\\\\\\\"@\\\\\\\");Gs.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Vs{constructor(){}parse_expression(t){try{this.reset(),this.node=Gs()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Vs.string_value_elements(t),n=[];for(let t=0;t{e.push(js.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(js.assign_item_size_line(t)),e.push(js.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=js.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return js.var_attribute_size(t)}var_array(t){return js.var_array(t)}}const Ws={math_random:\\\\\\\"random\\\\\\\"},qs=Object.keys(ns),Xs={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),Object.keys(Ws).forEach((t=>{const e=Ws[t];Xs[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Xs[t]=`Core.Math.${t}`})),qs.forEach((t=>{Xs[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Xs[t]=`Core.String.${t}`}));const Ys={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},$s={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{$s[t]=`Math.${t}`}));const Js={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Zs extends Hs{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new js,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller.reset(),t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_body()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}function_body(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rs,String:sr};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=Ys[n];if(i)return i(e);const r=`${e.join(\\\\\\\", \\\\\\\")}`,s=Xs[n];if(s)return`${s}(${r})`;const o=ai.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],s=`return ${e[0]}`;let o,a=[];try{o=new Function(s),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${r}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;ai.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,r=t.object,s=t.property;e=r.name,n=s.name;break}}if(e){if(e=Wr.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Js[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=$s[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=ai.expressionsRegister,r=i.getMethod(t);if(!r){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void ai.warn(e)}const s=new r(this.param);if(this.method_index+=1,this.methods[this.method_index]=s,s.require_dependency()){const t=s.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Qs extends Hs{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class Ks{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class to{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Vs,this.function_generator=new Zs(this.param),this.dependencies_controller=new Ks(this.param)}parse_expression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Vs,this.reset(),this.param.type()==Es.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Qs(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class eo{constructor(t){this.param=t}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new to(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class no extends zs{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.removeDirtyState()}}class io extends no{static type(){return Es.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=io.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends zs{constructor(){super(...arguments),this._components_contructor=io}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;ee))}}processRawInput(){const t=this.scene().cooker;t.block();const e=this.components;for(let t of e)t.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let t=0;t linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(so||(so={}));so.NONE,so.GAMMA_TO_LINEAR,so.LINEAR_TO_GAMMA,so.SRGB_TO_LINEAR,so.LINEAR_TO_SRGB;class oo{static set_hsv(t,e,n,i){t=Object(Ln.f)(t,1),e=Object(Ln.d)(e,0,1),n=Object(Ln.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const ao=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class lo extends no{static type(){return Es.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=lo.convert(t);return e?this.options.ensureInRange(e):e}}class co{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(xi.SEPARATOR);const n=e[0];return n&&(xi.NON_LETTER_PREFIXES.includes(n)||(e=`${xi.SEPARATOR}${e}`)),e}}class uo extends zs{constructor(){super(...arguments),this.decomposed_path=new co}}var ho;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(ho||(ho={}));class po extends uo{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Es.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,r=this.options.paramSelectionOptions()?ho.PARAM:ho.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(r==ho.PARAM?n=xi.findParam(this.node,t,this.decomposed_path):e=xi.findNode(this.node,t,this.decomposed_path));const s=r==ho.PARAM?this._found_param:this._found_node,o=r==ho.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==s?void 0:s.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),r==ho.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var _o,mo=n(33),fo=n(70);class go{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new go;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new go(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(_o||(_o={}));class vo{constructor(t=_o.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(Ln.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(go.from_json(n));return new vo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new vo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation)return!1;const e=t.points;if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.is_equal(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.is_equal_json(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!go.are_equal_json(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.from_json(n):this._points.push(go.from_json(n)),e+=1}}}const yo=1024;class xo extends zs{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new mo.a(this._texture_data,yo,1,w.ic)}static type(){return Es.RAMP}defaultValueSerialized(){return this._default_value instanceof vo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof vo?t.clone():vo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof vo?this._raw_input.toJSON():vo.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof vo?e instanceof vo?t.is_equal(e):t.is_equal_json(e):e instanceof vo?e.is_equal_json(t):vo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof vo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof vo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=vo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/yo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new fo.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const t=this.value.points,e=f.sortBy(t,(t=>t.position)),n=new Float32Array(e.length),i=new Float32Array(e.length);let r=0;for(let t of e)n[r]=t.position,i[r]=t.value,r++;return xo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}xo.DEFAULT_VALUE=new vo(_o.LINEAR,[new go(0,0),new go(1,1)]),xo.DEFAULT_VALUE_JSON=xo.DEFAULT_VALUE.toJSON();class bo extends zs{static type(){return Es.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Vs.string_value_elements(t)}}const wo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Es.BOOLEAN]:class extends no{static type(){return Es.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(sr.isBoolean(t))return sr.toBoolean(t);if(sr.isNumber(t)){return parseFloat(t)>=1}}return null}},[Es.BUTTON]:class extends zs{static type(){return Es.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Es.COLOR]:class extends ro{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Es.COLOR}componentNames(){return ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=so.NONE){switch(t){case so.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case so.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case so.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case so.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ar.unreachable(t)}this._value_serialized_dirty=!0}},[Es.FLOAT]:io,[Es.FOLDER]:class extends zs{static type(){return Es.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Es.INTEGER]:lo,[Es.OPERATOR_PATH]:po,[Es.PARAM_PATH]:class extends uo{static type(){return Es.PARAM_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.NODE_PATH]:class extends uo{static type(){return Es.NODE_PATH}initialize_param(){this._value=new vi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.RAMP]:xo,[Es.STRING]:bo,[Es.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Es.VECTOR2}componentNames(){return wo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Es.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Es.VECTOR3}componentNames(){return To}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Es.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Es.VECTOR4}componentNames(){return Ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class Mo{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Es.STRING:return this._handle_string_param(t);case Es.OPERATOR_PATH:return this._handle_operator_path_param(t);case Es.NODE_PATH:return this._handle_node_path_param(t);case Es.PARAM_PATH:return this._handle_param_path_param(t);case Es.FLOAT:case Es.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class So{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new Mo}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Ai(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notify_params_updated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let r;this.node.params_init_value_overrides&&(r=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,r)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):ai.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Es.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Es.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Es.FLOAT)}get_operator_path(t){return this.param_with_type(t,Es.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Es.BOOLEAN)}float(t){return this.value_with_type(t,Es.FLOAT)}integer(t){return this.value_with_type(t,Es.INTEGER)}string(t){return this.value_with_type(t,Es.STRING)}vector2(t){return this.value_with_type(t,Es.VECTOR2)}vector3(t){return this.value_with_type(t,Es.VECTOR3)}color(t){return this.value_with_type(t,Es.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(ai.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},r){const s=i.spare||!1;!1!==this._param_create_mode||s||ai.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&ai.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Eo[t];if(null!=o){const a=this._params_by_name[e];a&&(s?a.type()!=t&&this._deleteParam(a.name()):ai.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==r)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=r.raw_input)l.set(r.raw_input);else if(null!=r.simple_data)l.set(r.simple_data);else if(null!=r.complex_data){const t=r.complex_data.raw_input;t?l.set(t):l.set(n);const e=r.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void ai.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?ai.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class Co{constructor(){}}class No{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=No._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}No._next_id=0;class Lo{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.set_min_inputs_count(0),this.set_max_inputs_count(t.length),this.init_graph_node_inputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}init_graph_node_inputs(){for(let t=0;t0)for(let e=0;e0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let t=0;tt.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const s=this._graph_node_inputs[i];if(null==s){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&r==l||(null!=o&&this._depends_on_inputs&&s.removeGraphInput(o),null!=e?s.addGraphInput(e)?(this._depends_on_inputs||s.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new No(e,this.node,r,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),s.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Lo(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Ro{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const r=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&r.push(n)}return r}return[]}}class Po{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class Io{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class Fo{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Do;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Do||(Do={}));const ko=[Do.BOOL,Do.INT,Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4],Bo={[Do.BOOL]:Es.BOOLEAN,[Do.INT]:Es.INTEGER,[Do.FLOAT]:Es.FLOAT,[Do.VEC2]:Es.VECTOR2,[Do.VEC3]:Es.VECTOR3,[Do.VEC4]:Es.VECTOR4,[Do.SAMPLER_2D]:Es.RAMP,[Do.SSS_MODEL]:Es.STRING},zo={[Es.BOOLEAN]:Do.BOOL,[Es.COLOR]:Do.VEC3,[Es.INTEGER]:Do.INT,[Es.FLOAT]:Do.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Do.VEC2,[Es.VECTOR3]:Do.VEC3,[Es.VECTOR4]:Do.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Uo={[Do.BOOL]:!1,[Do.INT]:0,[Do.FLOAT]:0,[Do.VEC2]:[0,0],[Do.VEC3]:[0,0,0],[Do.VEC4]:[0,0,0,0],[Do.SAMPLER_2D]:xo.DEFAULT_VALUE_JSON,[Do.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Go={[Do.BOOL]:1,[Do.INT]:1,[Do.FLOAT]:1,[Do.VEC2]:2,[Do.VEC3]:3,[Do.VEC4]:4,[Do.SAMPLER_2D]:1,[Do.SSS_MODEL]:1};class Vo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Uo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Bo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Ho;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(Ho||(Ho={}));const jo=[Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4],Wo={[Ho.BOOL]:Es.BOOLEAN,[Ho.INT]:Es.INTEGER,[Ho.FLOAT]:Es.FLOAT,[Ho.VEC2]:Es.VECTOR2,[Ho.VEC3]:Es.VECTOR3,[Ho.VEC4]:Es.VECTOR4},qo={[Es.BOOLEAN]:Ho.BOOL,[Es.COLOR]:Ho.VEC3,[Es.INTEGER]:Ho.INT,[Es.FLOAT]:Ho.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Ho.VEC2,[Es.VECTOR3]:Ho.VEC3,[Es.VECTOR4]:Ho.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Xo={[Ho.BOOL]:!1,[Ho.INT]:0,[Ho.FLOAT]:0,[Ho.VEC2]:[0,0],[Ho.VEC3]:[0,0,0],[Ho.VEC4]:[0,0,0,0]};Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4;class Yo extends Fo{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Xo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Wo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var $o;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}($o||($o={}));class Jo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Es.FLOAT}are_types_matched(t,e){return e==$o.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const Zo={[Ki.ANIM]:void 0,[Ki.COP]:void 0,[Ki.EVENT]:$o.BASE,[Ki.GL]:Do.FLOAT,[Ki.JS]:Ho.FLOAT,[Ki.MANAGER]:void 0,[Ki.MAT]:void 0,[Ki.OBJ]:void 0,[Ki.POST]:void 0,[Ki.ROP]:void 0,[Ki.SOP]:void 0};function Qo(t,e,n){switch(t){case Ki.EVENT:return new Jo(e,n);case Ki.GL:return new Vo(e,n);case Ki.JS:return new Yo(e,n);default:return}}class Ko{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case Ki.EVENT:return;case Ki.GL:return zo;case Ki.JS:return qo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=Qo(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let r=t.init_value;const s=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(r=null!=o?o:null!=s?s:t.init_value,m.isArray(t.init_value))if(m.isNumber(r)){const e=new Array(t.init_value.length);e.fill(r),r=e}else m.isArray(r)&&r.length==t.init_value.length&&null!=s&&(r=t.init_value);null!=r&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(r),raw_input:b.clone(r),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class ta{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new Ko(this.node,this._context)}default_connection_type(){return Zo[this._context]}create_connection_point(t,e){return Qo(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creation_completed&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(er.INPUT),n=this.node.nodesByType(er.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;enull==t?void 0:t.type())),e=this.node.io.outputs.namedOutputConnectionPoints().map((t=>null==t?void 0:t.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=t.length)return!1;if(i.length!=e.length)return!1;for(let e=0;et.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class ea{constructor(t){this.node=t,this._connections=new Po(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Oo(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ta(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new Io(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class na{constructor(){}}class ia extends Ai{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new xs(this),this.pv=new Co,this.p=new na,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new Wi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new hr(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Mi(this)}get states(){return this._states=this._states||new Hi(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new dr(this)}get serializer(){return this._serializer=this._serializer||new As(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ts(this)}get io(){return this._io=this._io||new ea(this)}get nameController(){return this._name_controller=this._name_controller||new ji(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new So(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var r;return null===(r=this._params_controller)||void 0===r?void 0:r.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class ra extends ia{static context(){return Ki.MANAGER}}class sa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class oa{static BUTTON(t,e){return new sa(Es.BUTTON,t,e)}static BOOLEAN(t,e){return new sa(Es.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new sa(Es.COLOR,t,e)}static FLOAT(t,e){return new sa(Es.FLOAT,t,e)}static FOLDER(t=null,e){return new sa(Es.FOLDER,t,e)}static INTEGER(t,e){return new sa(Es.INTEGER,t,e)}static RAMP(t=xo.DEFAULT_VALUE,e){return new sa(Es.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new sa(Es.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new sa(Es.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new sa(Es.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new sa(Es.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new sa(Es.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new sa(Es.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new sa(Es.PARAM_PATH,t,e)}}class aa{}class la{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),r=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(sr.matchMask(r,t))return i;const s=this.findObjectByMaskInObject(t,i,r);if(s)return s}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let r of e.children){const e=this._removeTrailingOrHeadingSlash(r.name),s=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;sr.matchMask(s,t)&&n.push(r),this.objectsByMaskInObject(t,r,n,s)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ca={computeOnDirty:!1,callback:t=>{ha.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=oa.BOOLEAN(1,ca)}}}ua(aa);class ha{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var da;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(da||(da={}));const pa=[da.NONE,da.COLOR,da.TEXTURE],_a={computeOnDirty:!1,callback:t=>{fa.update(t)}};function ma(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=oa.INTEGER(pa.indexOf(da.NONE),{menu:{entries:pa.map(((t,e)=>({name:t,value:e})))},..._a}),this.bgColor=oa.COLOR([0,0,0],{visibleIf:{backgroundMode:pa.indexOf(da.COLOR)},..._a}),this.bgTexture=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:pa.indexOf(da.TEXTURE)},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,..._a})}}}ma(aa);class fa{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==pa.indexOf(da.NONE))t.background=null;else if(e.backgroundMode==pa.indexOf(da.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const ga={computeOnDirty:!1,callback:t=>{ya.update(t)}};function va(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=oa.BOOLEAN(0,ga),this.environment=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,...ga})}}}va(aa);class ya{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class xa{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new xa(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}xa.prototype.isFog=!0;class ba{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new ba(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}ba.prototype.isFogExp2=!0;const wa={computeOnDirty:!1,callback:t=>{Ma.update(t)}};var Ta;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Ta||(Ta={}));const Aa=[Ta.LINEAR,Ta.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0,wa),this.fogType=oa.INTEGER(Aa.indexOf(Ta.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Aa.map(((t,e)=>({name:t,value:e})))},...wa}),this.fogColor=oa.COLOR([1,1,1],{visibleIf:{useFog:1},...wa}),this.fogNear=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogFar=oa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogDensity=oa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.EXPONENTIAL)},...wa})}}}Ea(aa);class Ma{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Aa.indexOf(Ta.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new xa(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new ba(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Sa={computeOnDirty:!1,callback:t=>{Na.update(t)}};function Ca(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=oa.BOOLEAN(0,Sa),this.overrideMaterial=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,...Sa})}}}Ca(aa);class Na{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(Ki.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class La extends(Ca(va(Ea(ma(ua(aa)))))){}const Oa=new La;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Oa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.sceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this),this._children_controller_context=Ki.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new fr;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const r=`_____${n.renderOrder}__${n.path()}`;e.push(r),t.set(r,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Pa{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();sr.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class Ia{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const r=new As(i);e[i.graphNodeId()]=r.toJSON(t);const s=i.params.all;for(let t of s)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Fa;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(Fa||(Fa={}));const Da=[Fa.auxclick,Fa.click,Fa.contextmenu,Fa.dblclick,Fa.mousedown,Fa.mouseenter,Fa.mouseleave,Fa.mousemove,Fa.mouseover,Fa.mouseout,Fa.mouseup,Fa.pointerlockchange,Fa.pointerlockerror,Fa.select,Fa.wheel];class ka extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}}class Ba extends ia{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return Ki.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,u.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var za;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(za||(za={}));const Ua=[za.CANVAS,za.DOCUMENT];class Ga{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const r=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,r),i.set(n.type,{listener:r,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==za.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const Va={visibleIf:{active:1},callback:t=>{ja.PARAM_CALLBACK_updateRegister(t)}};class Ha extends Ba{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:Ua[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class ja extends Ha{acceptedEventTypes(){return[]}}const Wa=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=oa.BOOLEAN(0,Va),this.click=oa.BOOLEAN(0,Va),this.contextmenu=oa.BOOLEAN(0,Va),this.dblclick=oa.BOOLEAN(0,Va),this.mousedown=oa.BOOLEAN(1,Va),this.mouseenter=oa.BOOLEAN(0,Va),this.mouseleave=oa.BOOLEAN(0,Va),this.mousemove=oa.BOOLEAN(1,Va),this.mouseover=oa.BOOLEAN(0,Va),this.mouseout=oa.BOOLEAN(0,Va),this.mouseup=oa.BOOLEAN(1,Va),this.pointerlockchange=oa.BOOLEAN(0,Va),this.pointerlockerror=oa.BOOLEAN(0,Va),this.select=oa.BOOLEAN(0,Va),this.wheel=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class qa extends Ha{constructor(){super(...arguments),this.paramsConfig=Wa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Da.map((t=>new Jo(t,$o.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Xa;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Xa||(Xa={}));const Ya=[Xa.pointerdown,Xa.pointermove,Xa.pointerup];class $a extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}}const Ja=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{Za.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=oa.BOOLEAN(1,Va),this.pointermove=oa.BOOLEAN(0,Va),this.pointerup=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class Za extends Ha{constructor(){super(...arguments),this.paramsConfig=Ja}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ya.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Qa,Ka;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Qa||(Qa={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(Ka||(Ka={}));const tl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{el.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.sceneLoaded=oa.BOOLEAN(1,Va),this.play=oa.BOOLEAN(1,Va),this.pause=oa.BOOLEAN(1,Va),this.tick=oa.BOOLEAN(1,{separatorAfter:!0,...Va}),this.treachedTime=oa.BOOLEAN(0,{callback:t=>{el.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=oa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=oa.INTEGER(1,{range:[0,100]}),this.setFrame=oa.BUTTON(null,{callback:t=>{el.PARAM_CALLBACK_setFrame(t)}})}};class el extends Ha{constructor(){super(...arguments),this.paramsConfig=tl}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Qa.SET_FRAME,$o.BASE,this.onSetFrame.bind(this))]);const t=_i.map((t=>new Jo(t,$o.BASE)));t.push(new Jo(Ka.TIME_REACHED,$o.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}on_frame_update(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(Ka.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Ai(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this.on_frame_update.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t.onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var nl;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(nl||(nl={}));const il=[nl.keydown,nl.keypress,nl.keyup];class rl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}}const sl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{ol.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=oa.BOOLEAN(1,Va),this.keypress=oa.BOOLEAN(0,Va),this.keyup=oa.BOOLEAN(0,Va),this.keyCodes=oa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Va),this.ctrlKey=oa.BOOLEAN(0,Va),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class ol extends Ha{constructor(){super(...arguments),this.paramsConfig=sl}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(il.map((t=>new Jo(t,$o.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var al;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(al||(al={}));const ll=[al.resize];class cl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}}const ul=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{hl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.resize=oa.BOOLEAN(1,Va)}};class hl extends Ha{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(ll.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var dl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(dl||(dl={}));const pl=[dl.dragover];class _l extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}}var ml;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(ml||(ml={}));const fl=[ml.touchstart,ml.touchmove,ml.touchend];class gl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}}const vl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{yl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=oa.BOOLEAN(1,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class yl extends Ha{constructor(){super(...arguments),this.paramsConfig=vl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(pl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const xl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{bl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=oa.BOOLEAN(1,Va),this.touchmove=oa.BOOLEAN(0,Va),this.touchend=oa.BOOLEAN(0,Va)}};class bl extends Ha{constructor(){super(...arguments),this.paramsConfig=xl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(fl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class wl{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case ol.type():return this.keyboardEventsController;case qa.type():return this.mouseEventsController;case yl.type():return this.dragEventsController;case Za.type():return this.pointerEventsController;case el.type():return this.sceneEventsController;case bl.type():return this.touchEventsController;case hl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(ka)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(_l)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller($a)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(mi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(cl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(gl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Tl{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}set_reference_from_param(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),u.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}set_named_nodes_from_param(t){const e=t.decomposed_path.named_nodes();for(let n of e)u.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}reset_reference_from_param(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){u.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)u.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Es.OPERATOR_PATH,Es.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,r=[];for(let t of n)this._check_param(t,i,r);for(let t of r)i.set(t.node.graphNodeId(),t.node);const s=[];return i.forEach((t=>{s.push(t)})),s}_check_param(t,e,n){if(t instanceof po){const i=t.found_node(),r=t.found_param();return i&&e.set(i.graphNodeId(),i),void(r&&n.push(r))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Al;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Al||(Al={}));const El=ai.performance.performanceManager();class Ml{constructor(t){this.scene=t,this._frame=0,this._time=0,this._prev_performance_now=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._graph_node=new Ai(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(pi.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(pi.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(pi.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Al.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Al.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t()}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(Ml.START_FRAME,!0)}incrementTimeIfPlaying(){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime())}incrementTime(){if(this._realtimeState){const t=El.now(),e=(t-this._prev_performance_now)/1e3,n=this._time+e;this._prev_performance_now=t,this.setTime(n)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return Ml.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Cl{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Nl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,ai.renderersController.setRequireWebGL2())}}class Ll{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Ai(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Ol{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Rl{constructor(){this._cameras_controller=new s(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new wi(this),this._expressions_controller=new ui,this._nodes_controller=new Pa(this),this._objects_controller=new la(this),this._references_controller=new Tl(this),this._time_controller=new Ml(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=sr.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Ol}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new ci(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new wl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new hi(this)}get loadingController(){return this._loading_controller=this._loading_controller||new fi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new li}get viewersRegister(){return this._viewers_register=this._viewers_register||new Cl(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new Ia(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Pl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let r of t)i=e[r],i&&(n[r]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let r;for(let s of i)if(r=e[s],null!=r){const e=r.options,i=r.overriden_options;if(e||i){const o=r;e&&e.spare==t?null!=o.raw_input&&(n[s]={complex_data:o}):i&&(n[s]={complex_data:o})}else{const t=r;(i||null!=t)&&(n[s]={simple_data:t})}}}return n}}const Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,r;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:s}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of s)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let s of this._optimized_root_node_names){const o=e[s],a=this._node.createNode(Il);if(a){a.setName(s),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(r=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,r;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const s=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of s)(null===(r=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===r?void 0:r.active())||o++;return this.is_optimized_root_node_generic({outputs_count:s.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let r of n)if(r!=e){const n=t[r].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(r)}}return i}_create_operation_container(t,e,n,i){const r=Pl.non_spare_params_data_value(n.params),s=Dl.operation_type(n),o=this._node.create_operation_container(s,i,r);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return Dl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class kl{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:r}=Dl.child_names_by_optimized_state(e),s=[];for(let n of r){const i=e[n],r=i.type.toLowerCase(),o=Pl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(r,o);t&&(t.setName(n),s.push(t))}catch(e){console.error(`error importing node: cannot create with type ${r}`,e);const i=sr.camelCase(r);try{const t=this._node.createNode(i,o);t&&(t.setName(n),s.push(t))}catch(e){const a=`${r}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),s.push(t))}catch(e){const n=`failed to create node with type '${r}', '${i}' or '${a}'`;t.report.addWarning(n),ai.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==Ki.SOP){const t=Object.keys(e);let r;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(r=i)}if(r){const t=s.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(r)){const t=`node '${`${this._node.path()}/${r}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of s){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else ai.warn(`possible import error for node ${n.name()}`)}for(let t of s){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const Bl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class zl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;null!=e&&this._node.io.inputs.setCount(1,e),this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(ai.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const r=e.comment;r&&n.setComment(r),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new kl(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,r,s,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(r=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(l));const c=a.optimize;null!=c&&(null===(o=null===(s=this._node.flags)||void 0===s?void 0:s.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n0,r=n.toAdd&&n.toAdd.length>0;if(i||r){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Wl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of Bl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Ul extends Pl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Pl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Pl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends zl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new zl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof bo?new Vl(t):t instanceof xo?new Hl(t):new Pl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Rl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const r=e.realtimeState;null!=r&&t.timeController.setRealtimeState(r),t.setFrame(e.frame||Ml.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Yl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),r=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;r.push(i)}const s=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();s.push(`${n}/ui.json?t=${t}`)}for(let t of r)s.push(t);let o=0;const a=s.length,l=s.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),u=[];for(let t of c)u.push(await t.json());const h={root:u[0],properties:u[1]};let d=2;t.editorMode&&(h.ui=u[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=u[t+d];p[e]=n}return this.assemble(h,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{s.matrixAutoUpdate=!0},c.onComplete=()=>{s.matrixAutoUpdate=t,s.matrixAutoUpdate||s.updateMatrix()}}if(o instanceof au.a&&this._target_value instanceof au.a){const t={value:0},e=o,n=(new au.a).copy(o),i=this._target_value;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._target_value[t],i);a&&this._startTimeline(e,n,c,a)}}}_sceneGraphProps(t,e){const n=e.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=t[e];let i=null;const r=[];return m.isNumber(n)?(i=t,r.push(e)):(i=n,this._target_value instanceof d.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,au.a),{target_property:n,to_target:i,property_names:r}}{const e=t[n.shift()];if(e){const t=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(e,t)}}}_populateWithNode(t,e,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",t]);const i=t.p[this._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):ai.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Es.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Es.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Es.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Es.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Es.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated 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o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),this._startTimeline(e,n,i,r)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),this._startTimeline(e,n,i,r)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),i.w=this.withOp(t.value.w,this._target_value.w,o),this._startTimeline(e,n,i,r)}withOp(t,e,n){switch(n){case N_.SET:return 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q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0,{range:[-1,1]}),this.overridePositions=oa.BOOLEAN(0),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends iu{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new R_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ar.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends 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type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new R_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"position\\\\\\\")}};class im extends iu{constructor(){super(...arguments),this.paramsConfig=nm}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new R_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var rm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(rm||(rm={}));const sm=[rm.CUSTOM,rm.FROM_SCENE_GRAPH,rm.FROM_NODE],om=sm.indexOf(rm.CUSTOM),am=sm.indexOf(rm.FROM_SCENE_GRAPH),lm=sm.indexOf(rm.FROM_NODE);const cm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(om,{menu:{entries:sm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=oa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=oa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{um.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=oa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=oa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=oa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=oa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=oa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=oa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class um extends iu{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new R_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(sm.indexOf(t))}async _prepare_timeline_builder(t){const e=sm[this.pv.mode];switch(e){case rm.CUSTOM:return this._prepare_timebuilder_custom(t);case rm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case rm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ar.unreachable(e)}_prepare_timebuilder_custom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepare_timebuilder_from_scene_graph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof au.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const r=i.value;r&&(m.isNumber(r)||m.isVector(r))&&t.setPropertyValue(r)}static PARAM_CALLBACK_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const hm=new class extends aa{constructor(){super(...arguments),this.unlimited=oa.BOOLEAN(0),this.count=oa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=oa.FLOAT(0),this.yoyo=oa.BOOLEAN(0)}};class dm extends iu{constructor(){super(...arguments),this.paramsConfig=hm}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new R_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends iu{constructor(){super(...arguments),this.paramsConfig=pm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=oa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends iu{constructor(){super(...arguments),this.paramsConfig=bm}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ar.unreachable(t)}))}))}))}cook(t){const e=t[0]||new R_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ar.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ar.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new R_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends ia{static context(){return Ki.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Ai(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Rm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Rm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Rm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Pm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Im{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Fm=new st.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class km{constructor(t){this._mesh=new k.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class Bm extends Im{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new km(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class zm extends Im{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),r=t.state;let s,o;r.buffers.color.setMask(!1),r.buffers.depth.setMask(!1),r.buffers.color.setLocked(!0),r.buffers.depth.setLocked(!0),this.inverse?(s=0,o=1):(s=1,o=0),r.buffers.stencil.setTest(!0),r.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),r.buffers.stencil.setFunc(i.ALWAYS,s,4294967295),r.buffers.stencil.setClear(o),r.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),r.buffers.color.setLocked(!1),r.buffers.depth.setLocked(!1),r.buffers.stencil.setLocked(!1),r.buffers.stencil.setFunc(i.EQUAL,1,4294967295),r.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),r.buffers.stencil.setLocked(!0)}}class Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Z(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===Pm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===Bm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new Bm(Pm),this.clock=new Om}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e({name:Object.keys(t)[0],value:Object.values(t)[0]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends aa{constructor(){super(...arguments),this.prependRenderPass=oa.BOOLEAN(1),this.useRenderTarget=oa.BOOLEAN(1),this.tmagFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=oa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=oa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new Gm(e,t)}else n=new Gm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=ai.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Hm(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Km extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(43);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var rf=new Uint16Array(32),sf=0;sf<32;sf++)rf[sf]=28898;const of=new mo.a(rf,32,1,w.gb,w.M);class af extends ia{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new Bi(this)}static context(){return Ki.COP}static displayedInputNames(){return nf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class uf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${sr.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),m.isNumber(i)&&(i=sr.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return sr.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:sr.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return sr.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:sr.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const hf=/\\\\/+/g;class df extends ia{static context(){return Ki.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==Ki.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(hf,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return uf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return uf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const pf=new class extends aa{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const ff=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=oa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=oa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ir.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return gf.INPUT_NAME}get output_name(){return gf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(gf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Do.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Ef extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Mf={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,r;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(!s)return;const o=t.glVarName(e),a=s.type(),l=new Ef(t,a,o);n.addDefinitions(t,[l]);const c=null===(r=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===r?void 0:r.assembler;if(!c)return;const u=c.shader_config(n.current_shader_name);if(!u)return;const h=u.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of h)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==h.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Mf[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var r,s;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Ef(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const h=new Map;h.set(xf.FRAGMENT,[]),u.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(s=null===(r=t.material_node)||void 0===r?void 0:r.assemblerController)||void 0===s?void 0:s.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)u.pushOnArrayAtEntry(c,t,l),u.pushOnArrayAtEntry(h,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),h.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Cf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const Nf=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Cf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Ef(this,e,n),r=`${n} = ${uf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[r],xf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Rf{static overlay(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.max(t.width,e.width),r.height=Math.max(t.height,e.height);let s=r.getContext(\\\\\\\"2d\\\\\\\");s.drawImage(t,0,0,t.width,t.height),s.drawImage(e,0,0,e.width,e.height);const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=t,r.height=e;let s=r.getContext(\\\\\\\"2d\\\\\\\");s.beginPath(),s.rect(0,0,t,e),s.fillStyle=\\\\\\\"white\\\\\\\",s.fill();const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const r=Math.min(t.width,t.height),s=t.width/t.height;i.width=r,i.height=r;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=s>1,l=a?(t.width-r)/2:(t.height-r)/2;a?o.drawImage(t,l,0,r,r,0,0,r,r):o.drawImage(t,0,l,r,r,0,0,r,r);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),u=new Image;u.onload=()=>{e(u)},u.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var Pf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Pf||(Pf={}));class If{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Rf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,r=this._data_texture.image.data,s=4*n;for(let t=0;t0:r.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(r.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":r.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":r.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":r.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":r.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":r.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":r.uniforms[e].value=(new U.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":r.uniforms[e].value=(new A.a).fromArray(i.value);break;default:r.uniforms[e].value=i.value}}if(void 0!==t.defines&&(r.defines=t.defines),void 0!==t.vertexShader&&(r.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(r.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)r.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(r.flatShading=1===t.shading),void 0!==t.size&&(r.size=t.size),void 0!==t.sizeAttenuation&&(r.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(r.map=n(t.map)),void 0!==t.matcap&&(r.matcap=n(t.matcap)),void 0!==t.alphaMap&&(r.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(r.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(r.bumpScale=t.bumpScale),void 0!==t.normalMap&&(r.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(r.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),r.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(r.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(r.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(r.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(r.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(r.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(r.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(r.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(r.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(r.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(r.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(r.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(r.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(r.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(r.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(r.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(r.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(r.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(r.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(r.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(r.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(r.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(r.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(r.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(r.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(r.thicknessMap=n(t.thicknessMap)),r}setTextures(t){return this.textures=t,this}}class Xf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new qf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new U.a;for(let t=0;t{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(Ki.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class Jf extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,tg.handle_dependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{$f.callback(n,i)}})}}}class Zf{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const Qf=[256,256];const Kf=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2(Qf),this.useCameraRenderer=oa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new k.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new fr,this._texture_camera=new tf.a,this._children_controller_context=Ki.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return Hn.GL_TEXTURE}_create_assembler_controller(){const t=ai.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Sf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Of.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),r=t.assembler.uniforms();i&&r&&(this._fragment_shader=i,this._uniforms=r),tg.handle_dependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handle_dependencies(t,e,n){const i=t.scene(),r=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(r,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(r))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],rg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],sg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=oa.BOOLEAN(0,{...lg}),this.encoding=oa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=oa.BOOLEAN(0,{...cg}),this.mapping=oa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=oa.BOOLEAN(0,{...ug}),this.wrapS=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...ug}),this.wrapT=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...ug}),this.tminFilter=oa.BOOLEAN(0,{...hg}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...hg}),this.tmagFilter=oa.BOOLEAN(0,{...hg}),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...hg}),this.tanisotropy=oa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=oa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=oa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=oa.BOOLEAN(0,{...pg}),this.flipY=oa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=oa.BOOLEAN(0,{..._g}),this.offset=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=oa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=oa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=oa.BOOLEAN(0,{...yg}),this.tformat=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=oa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=oa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(aa)){}new bg;class wg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Ff(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const r=Object.keys(i)[0];i[r]==e[n]&&t.push(`${n}: ${r}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=oa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=oa.BUTTON(null,{cook:!1,callback:t=>{Mg.PARAM_CALLBACK_update(t)}})}}}(aa))){}const Eg=new Ag;class Mg extends af{constructor(){super(...arguments),this.paramsConfig=Eg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Tg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Sg=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=oa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),r=255*i[0],s=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t({name:t,value:e})))}})}};class Ig extends af{constructor(){super(...arguments),this.paramsConfig=Pg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(Ki.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Rg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const kg=new class extends aa{constructor(){super(...arguments),this.useCameraRenderer=oa.BOOLEAN(1),this.mode=oa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class Bg extends af{constructor(){super(...arguments),this.paramsConfig=kg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Ff(this);const e=await this._renderer_controller.renderer();if(e){const n=new wt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class zg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==s?s:w.V,this.magFilter=void 0!==r?r:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}zg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(29);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=ai.assetUrls.remapedUrl(t);if(e)return e;const n=ai.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await ai.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return ai.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,r=n.fullUrl,s=t.node;e.push(ai.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:r,node:s}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}var Wg;jg.loadingManager=Hg,function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(Wg||(Wg={}));Wg.JPEG,Wg.JPG,Wg.PNG,Wg.EXR,Wg.BASIS,Wg.HDR;class qg extends jg{constructor(t,e,n,i){super(t,i,n),this._param=e,this._node=n,this._scene=i}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=xi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),r=await this._urlToLoad();if(qg.VIDEO_EXTENSIONS.includes(i)){const t=await this._load_as_video(r);e(t)}else this.loader_for_ext(i,t).then((async t=>{t?(qg.increment_in_progress_loads_count(),await qg.wait_for_max_concurrent_loads_queue_freed(),t.load(r,(t=>{qg.decrement_in_progress_loads_count();const n=qg._onTextureLoadedCallback;n&&n(r,t),e(t)}),void 0,(t=>{qg.decrement_in_progress_loads_count(),ai.warn(\\\\\\\"error\\\\\\\",t),n()}))):n()}))}))}static module_names(t){switch(t){case Wg.EXR:return[Vn.EXRLoader];case Wg.HDR:return[Vn.RGBELoader];case Wg.BASIS:return[Vn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case Wg.EXR:return await this._exr_loader(e);case Wg.HDR:return await this._hdr_loader(e);case Wg.BASIS:return await qg._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await ai.modulesRegister.module(Vn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await ai.modulesRegister.module(Vn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await ai.modulesRegister.module(Vn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.BASISPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const s=await ai.renderersController.waitForRenderer();return s?n.detectSupport(s):ai.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}_load_as_video(t){return new Promise(((e,n)=>{const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const t=new zg(i);e(t)};const r=document.createElement(\\\\\\\"source\\\\\\\"),s=jg.extension(t);let o=qg.VIDEO_SOURCE_TYPE_BY_EXT[s];o=o||qg._default_video_source_type(t),r.setAttribute(\\\\\\\"type\\\\\\\",o),r.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(r);let a=t;a=\\\\\\\"mp4\\\\\\\"==s?qg.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):qg.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=qg.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||qg._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static _default_video_source_type(t){return`video/${jg.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.isChrome()?0:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}qg.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,qg.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,qg.VIDEO_EXTENSIONS=[\\\\\\\"mp4\\\\\\\",\\\\\\\"webm\\\\\\\",\\\\\\\"ogv\\\\\\\"],qg.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},qg.MAX_CONCURRENT_LOADS_COUNT=qg._init_max_concurrent_loads_count(),qg.CONCURRENT_LOADS_DELAY=qg._init_concurrent_loads_delay(),qg.in_progress_loads_count=0,qg._queue=[];var Xg=n(115);class Yg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{Jg.PARAM_CALLBACK_reload(t)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{Jg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=oa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Jg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(aa))){}const $g=new Yg;class Jg extends af{constructor(){super(...arguments),this.paramsConfig=$g,this.textureParamsController=new wg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){const e=await fetch(this.pv.url),n=await e.arrayBuffer(),i=await Object(Xg.parseGIF)(n);this._parsedFrames=await Object(Xg.decompressFrames)(i,!0);const r=this._parsedFrames[0];if(this._frameDelay=r.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),this._gifCanvasElement){const t=new Tg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}const Zg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\"];class Qg{static isStaticImageUrl(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\"),n=e[e.length-1];return!Zg.includes(n)}}class Kg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{ev.PARAM_CALLBACK_reload(t,e)}})}}}(aa))){}const tv=new Kg;class ev extends af{constructor(){super(...arguments),this.paramsConfig=tv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url)){const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}else this.states.error.set(\\\\\\\"input is not an image\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new qg(t,n,this,this.scene());try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var nv=n(32);const iv=.005;class rv{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new fr,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:iv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new au.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Z(this.res,this.res,{type:n}),this.progressiveLightMap2=new Z(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const r=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,r)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(r-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const s={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=s.previousShadowMap,i.uniforms.pixelOffset=s.pixelOffset,n.uniforms.previousShadowMap=s.previousShadowMap,n.uniforms.pixelOffset=s.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:iv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:iv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const sv=new class extends aa{constructor(){super(...arguments),this.update=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=oa.BOOLEAN(1),this.lightMapRes=oa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=oa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=oa.FLOAT(iv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=oa.BOOLEAN(1),this.blurAmount=oa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=oa.FLOAT(1,{range:[0,10]}),this.objectsMask=oa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=oa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class ov extends af{constructor(){super(...arguments),this.paramsConfig=sv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await ai.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new rv(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof nv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof nv.a||!n.has(e)&&e instanceof k.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const av=new aa;class lv extends af{constructor(){super(...arguments),this.paramsConfig=av}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const cv=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE];class uv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:cv}}),this.resolution=oa.VECTOR2([1024,1024]),this.useCameraRenderer=oa.BOOLEAN(1),this.render=oa.BUTTON(null,{callback:t=>{dv.PARAM_CALLBACK_render(t)}})}}}(aa))){}const hv=new uv;class dv extends af{constructor(){super(...arguments),this.paramsConfig=hv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(Ki.OBJ),this._camera_node&&cv.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const pv=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class mv extends(xg(aa)){}const fv=new mv;class gv extends af{constructor(){super(...arguments),this.paramsConfig=fv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class vv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_VIDEO]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{xv.PARAM_CALLBACK_reload(t,e)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=oa.FLOAT(0,{cook:!1}),this.setVideoTime=oa.BUTTON(null,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_time(t)}})}}}(aa))){}const yv=new vv;class xv extends af{constructor(){super(...arguments),this.paramsConfig=yv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url))this.states.error.set(\\\\\\\"input is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&wg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new qg(t,n,this,this.scene());try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class bv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=oa.VECTOR2([1024,1024])}}}(aa))){}const wv=new bv;class Tv extends af{constructor(){super(...arguments),this.paramsConfig=wv,this.textureParamsController=new wg(this)}static type(){return Lg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new zg(this._video),n=t[0];if(n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Av extends ia{static context(){return Ki.COP}cook(){this.cookController.endCook()}}class Ev extends Av{}class Mv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Av{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Rv,Pv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Rv||(Rv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Pv||(Pv={}));const Iv=new class extends aa{constructor(){super(...arguments),this.animation=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.ANIM},dependentOnFoundNode:!1}),this.play=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_pause(t)}})}};class Fv extends Ba{constructor(){super(...arguments),this.paramsConfig=Iv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Rv.START,$o.BASE,this._play.bind(this)),new Jo(Rv.STOP,$o.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Pv.START,$o.BASE),new Jo(Pv.COMPLETE,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(Ki.ANIM);if(!n)return;const i=await n.compute();i&&(this._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Pv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Pv.COMPLETE,t)}}const Dv=\\\\\\\"event\\\\\\\";const kv=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(1),this.inputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Bv extends Ba{constructor(){super(...arguments),this.paramsConfig=kv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>Dv)),this.io.connection_points.set_expected_output_types_function((()=>[$o.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill($o.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Dv,t)}}const zv=new class extends aa{constructor(){super(...arguments),this.blocking=oa.BOOLEAN(1)}};class Uv extends Ba{constructor(){super(...arguments),this.paramsConfig=zv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Uv.OUTPUT,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Uv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Gv;Uv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Gv||(Gv={}));const Vv=new class extends aa{constructor(){super(...arguments),this.dispatch=oa.BUTTON(null,{callback:t=>{Hv.PARAM_CALLBACK_execute(t)}})}};class Hv extends Ba{constructor(){super(...arguments),this.paramsConfig=Vv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(Gv.OUT,$o.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Gv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class jv extends Ba{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const r=await this.create_controls_instance(t,i),s=this._controls_by_viewer.get(e);s&&s.dispose(),this._controls_by_viewer.set(e,r);const o=ai.performance.performanceManager().now();return r.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setup_controls(r),r}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Wv=n(28);const qv=new p.a(0,0,1),Xv=new Wv.a,Yv=new au.a,$v=new au.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Jv={type:\\\\\\\"change\\\\\\\"};class Zv extends $.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new au.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},r=function(){e.screenOrientation=window.orientation||0};this.connect=function(){r(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?Ln.e(t.alpha)+e.alphaOffset:0,r=t.beta?Ln.e(t.beta):0,s=t.gamma?Ln.e(t.gamma):0,o=e.screenOrientation?Ln.e(e.screenOrientation):0;!function(t,e,n,i,r){Xv.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler(Xv),t.multiply($v),t.multiply(Yv.setFromAxisAngle(qv,-r))}(e.object.quaternion,i,r,s,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Jv))}},this.dispose=function(){e.disconnect()},this.connect()}}const Qv=new class extends aa{constructor(){super(...arguments),this.enabled=oa.BOOLEAN(1)}};class Kv extends jv{constructor(){super(...arguments),this.paramsConfig=Qv,this._controls_by_element_id=new Map}static type(){return rr.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async create_controls_instance(t,e){const n=new Zv(t);return this._controls_by_element_id.set(e.id,n),n}setup_controls(t){t.enabled=this.pv.enabled}update_required(){return!0}dispose_controls_for_html_element_id(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ty{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(Ln.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const ey={type:\\\\\\\"change\\\\\\\"},ny={type:\\\\\\\"start\\\\\\\"},iy={type:\\\\\\\"end\\\\\\\"};class ry extends $.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(ey),n.update(),r=i.NONE},this.update=function(){const e=new p.a,h=(new au.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=h.clone().invert(),_=new p.a,m=new au.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(h),o.setFromVector3(e),n.autoRotate&&r===i.NONE&&M(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;tMath.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(u||_.distanceToSquared(n.object.position)>s||8*(1-m.dot(n.object.quaternion))>s)&&(n.dispatchEvent(ey),_.copy(n.object.position),m.copy(n.object.quaternion),u=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const s=1e-6,o=new ty,a=new ty;let l=1;const c=new p.a;let u=!1;const h=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function E(){return Math.pow(.95,n.zoomSpeed)}function M(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const r=n.domElement;if(n.object.isPerspectiveCamera){const s=n.object.position;t.copy(s).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/r.clientHeight,n.object.matrix),N(2*i*o/r.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/r.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/r.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){h.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)h.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);h.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function k(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function B(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_)}function z(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,r=t.pageY-e.y,s=Math.sqrt(i*i+r*r);x.set(0,s),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),r=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&k(),n.enablePan&&D(),r=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&k(),n.enableRotate&&F(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),r=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),r=i.PAN}else{if(!1===n.enableRotate)return;P(t),r=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;P(t),r=i.ROTATE}else{if(!1===n.enablePan)return;I(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;B(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&z(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&B(t)}(t),n.update();break;default:r=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,h).multiplyScalar(n.rotateSpeed);var e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(E()):b.y<0&&R(E()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(iy),r=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(ny),function(t){t.deltaY<0?R(E()):t.deltaY>0&&O(E()),n.update()}(t),n.dispatchEvent(iy))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:M(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:M(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e{hy.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=oa.BOOLEAN(0),this.keysMode=oa.INTEGER(cy.indexOf(ly.PAN),{visibleIf:{enableKeys:1},menu:{entries:cy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=oa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.PAN)}}),this.keysRotateSpeedVertical=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}}),this.keysRotateSpeedHorizontal=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}})}};class hy extends jv{constructor(){super(...arguments),this.paramsConfig=uy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rr.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(oy,$o.BASE),new Jo(ay,$o.BASE),new Jo(\\\\\\\"end\\\\\\\",$o.BASE)])}async create_controls_instance(t,e){const n=new ry(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(oy,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(ay,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setup_controls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=cy[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}update_required(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class dy extends hy{static type(){return rr.MAP}async create_controls_instance(t,e){const n=new sy(t,e);return this._bind_listeners_to_controls_instance(n),n}}const py=new class extends aa{constructor(){super(...arguments),this.delay=oa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class _y extends Ba{constructor(){super(...arguments),this.paramsConfig=py}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new 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t!==e?(n-t)/(e-t):0},lerp:hx,damp:function(t,e,n,i){return hx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(ux(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(ix=t%2147483647),ix=16807*ix%2147483647,(ix-1)/2147483646},degToRad:function(t){return t*rx},radToDeg:function(t){return t*sx},isPowerOfTwo:dx,ceilPowerOfTwo:px,floorPowerOfTwo:_x,setQuaternionFromProperEuler:function(t,e,n,i,r){const s=Math.cos,o=Math.sin,a=s(n/2),l=o(n/2),c=s((e+i)/2),u=o((e+i)/2),h=s((e-i)/2),d=o((e-i)/2),p=s((i-e)/2),_=o((i-e)/2);switch(r){case\\\\\\\"XYX\\\\\\\":t.set(a*u,l*h,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*u,l*h,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*h,l*d,a*u,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*u,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*u,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*u,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+r)}}});class fx{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. 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Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}fx.prototype.isVector2=!0;class gx{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function vx(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;ne&&(e=t[n]);return e}gx.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function yx(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let xx;class bx{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===xx&&(xx=yx(\\\\\\\"canvas\\\\\\\")),xx.width=t.width,xx.height=t.height;const n=xx.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=xx}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let wx=0;class Tx extends nx{constructor(t=Tx.DEFAULT_IMAGE,e=Tx.DEFAULT_MAPPING,n=1001,i=1001,r=1006,s=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:wx++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=s,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new fx(0,0),this.repeat=new fx(1,1),this.center=new fx(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gx,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=lx()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t1)switch(this.wrapS){case wy:t.x=t.x-Math.floor(t.x);break;case Ty:t.x=t.x<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case wy:t.y=t.y-Math.floor(t.y);break;case Ty:t.y=t.y<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function Ax(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?bx.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}Tx.DEFAULT_IMAGE=void 0,Tx.DEFAULT_MAPPING=300,Tx.prototype.isTexture=!0;class Ex{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(cx(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Cx.prototype.isQuaternion=!0;class Nx{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(Ox.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Ox.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*s,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*s,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*s,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,o=t.z,a=t.w,l=a*e+s*i-o*n,c=a*n+o*e-r*i,u=a*i+r*n-s*e,h=-r*e-s*n-o*i;return this.x=l*a+h*-r+c*-o-u*-s,this.y=c*a+h*-s+u*-r-l*-o,this.z=u*a+h*-o+l*-s-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,s=e.x,o=e.y,a=e.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return Lx.copy(this).projectOnVector(t),this.sub(Lx)}reflect(t){return this.sub(Lx.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(cx(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}Nx.prototype.isVector3=!0;const Lx=new Nx,Ox=new Cx;class Rx{constructor(t=new Nx(1/0,1/0,1/0),e=new Nx(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.length;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.count;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Ix),Ix.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Vx),Hx.subVectors(this.max,Vx),Dx.subVectors(t.a,Vx),kx.subVectors(t.b,Vx),Bx.subVectors(t.c,Vx),zx.subVectors(kx,Dx),Ux.subVectors(Bx,kx),Gx.subVectors(Dx,Bx);let e=[0,-zx.z,zx.y,0,-Ux.z,Ux.y,0,-Gx.z,Gx.y,zx.z,0,-zx.x,Ux.z,0,-Ux.x,Gx.z,0,-Gx.x,-zx.y,zx.x,0,-Ux.y,Ux.x,0,-Gx.y,Gx.x,0];return!!qx(e,Dx,kx,Bx,Hx)&&(e=[1,0,0,0,1,0,0,0,1],!!qx(e,Dx,kx,Bx,Hx)&&(jx.crossVectors(zx,Ux),e=[jx.x,jx.y,jx.z],qx(e,Dx,kx,Bx,Hx)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Ix.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Ix).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(Px[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Px[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Px[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Px[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Px[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Px[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Px[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Px[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Px)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Rx.prototype.isBox3=!0;const Px=[new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx],Ix=new Nx,Fx=new Rx,Dx=new Nx,kx=new Nx,Bx=new Nx,zx=new Nx,Ux=new Nx,Gx=new Nx,Vx=new Nx,Hx=new Nx,jx=new Nx,Wx=new Nx;function qx(t,e,n,i,r){for(let s=0,o=t.length-3;s<=o;s+=3){Wx.fromArray(t,s);const o=r.x*Math.abs(Wx.x)+r.y*Math.abs(Wx.y)+r.z*Math.abs(Wx.z),a=e.dot(Wx),l=n.dot(Wx),c=i.dot(Wx);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Xx=new Rx,Yx=new Nx,$x=new Nx,Jx=new Nx;class Zx{constructor(t=new Nx,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Xx.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Jx.subVectors(t,this.center);const e=Jx.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Jx.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return $x.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Yx.copy(t.center).add($x)),this.expandByPoint(Yx.copy(t.center).sub($x)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Qx=new Nx,Kx=new Nx,tb=new Nx,eb=new Nx,nb=new Nx,ib=new Nx,rb=new Nx;class sb{constructor(t=new Nx,e=new Nx(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Qx)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Qx.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Qx.copy(this.direction).multiplyScalar(e).add(this.origin),Qx.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Kx.copy(t).add(e).multiplyScalar(.5),tb.copy(e).sub(t).normalize(),eb.copy(this.origin).sub(Kx);const r=.5*t.distanceTo(e),s=-this.direction.dot(tb),o=eb.dot(this.direction),a=-eb.dot(tb),l=eb.lengthSq(),c=Math.abs(1-s*s);let u,h,d,p;if(c>0)if(u=s*a-o,h=s*o-a,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+s*h+2*o)+h*(s*u+h+2*a)+l}else h=r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h=-r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h<=-p?(u=Math.max(0,-(-s*r+o)),h=u>0?-r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-a),r),d=h*(h+2*a)+l):(u=Math.max(0,-(s*r+o)),h=u>0?r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l);else h=s>0?-r:r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(tb).multiplyScalar(h).add(Kx),d}intersectSphere(t,e){Qx.subVectors(t.center,this.origin);const n=Qx.dot(this.direction),i=Qx.dot(Qx)-n*n,r=t.radius*t.radius;if(i>r)return null;const s=Math.sqrt(r-i),o=n-s,a=n+s;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Qx)}intersectTriangle(t,e,n,i,r){nb.subVectors(e,t),ib.subVectors(n,t),rb.crossVectors(nb,ib);let s,o=this.direction.dot(rb);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}eb.subVectors(this.origin,t);const a=s*this.direction.dot(ib.crossVectors(eb,ib));if(a<0)return null;const l=s*this.direction.dot(nb.cross(eb));if(l<0)return null;if(a+l>o)return null;const c=-s*eb.dot(rb);return c<0?null:this.at(c/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ob{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l,c,u,h,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=r,f[5]=s,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=u,f[14]=h,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new ob).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/ab.setFromMatrixColumn(t,0).length(),r=1/ab.setFromMatrixColumn(t,1).length(),s=1/ab.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*s,e[9]=n[9]*s,e[10]=n[10]*s,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(cb,t,ub)}lookAt(t,e,n){const i=this.elements;return pb.subVectors(t,e),0===pb.lengthSq()&&(pb.z=1),pb.normalize(),hb.crossVectors(n,pb),0===hb.lengthSq()&&(1===Math.abs(n.z)?pb.x+=1e-4:pb.z+=1e-4,pb.normalize(),hb.crossVectors(n,pb)),hb.normalize(),db.crossVectors(pb,hb),i[0]=hb.x,i[4]=db.x,i[8]=pb.x,i[1]=hb.y,i[5]=db.y,i[9]=pb.y,i[2]=hb.z,i[6]=db.z,i[10]=pb.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=ab.set(i[0],i[1],i[2]).length();const s=ab.set(i[4],i[5],i[6]).length(),o=ab.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],lb.copy(this);const a=1/r,l=1/s,c=1/o;return lb.elements[0]*=a,lb.elements[1]*=a,lb.elements[2]*=a,lb.elements[4]*=l,lb.elements[5]*=l,lb.elements[6]*=l,lb.elements[8]*=c,lb.elements[9]*=c,lb.elements[10]*=c,e.setFromRotationMatrix(lb),n.x=r,n.y=s,n.z=o,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}ob.prototype.isMatrix4=!0;const ab=new Nx,lb=new ob,cb=new Nx(0,0,0),ub=new Nx(1,1,1),hb=new Nx,db=new Nx,pb=new Nx,_b=new ob,mb=new Cx;class fb{constructor(t=0,e=0,n=0,i=fb.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],o=i[8],a=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(cx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(h,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-cx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(cx(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-s,l)):(this._y=0,this._z=Math.atan2(a,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-cx(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(a,r)):(this._x=0,this._z=Math.atan2(-s,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(cx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-cx(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(o,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return _b.makeRotationFromQuaternion(t),this.setFromRotationMatrix(_b,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return mb.setFromEuler(this),this.setFromQuaternion(mb,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new Nx(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isEuler=!0,fb.DefaultOrder=\\\\\\\"XYZ\\\\\\\",fb.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class gb{constructor(){this.mask=1}set(t){this.mask=1<1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){Rb.subVectors(i,e),Pb.subVectors(n,e),Ib.subVectors(t,e);const s=Rb.dot(Rb),o=Rb.dot(Pb),a=Rb.dot(Ib),l=Pb.dot(Pb),c=Pb.dot(Ib),u=s*l-o*o;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*a-o*c)*h,p=(s*c-o*a)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Fb),Fb.x>=0&&Fb.y>=0&&Fb.x+Fb.y<=1}static getUV(t,e,n,i,r,s,o,a){return this.getBarycoord(t,e,n,i,Fb),a.set(0,0),a.addScaledVector(r,Fb.x),a.addScaledVector(s,Fb.y),a.addScaledVector(o,Fb.z),a}static isFrontFacing(t,e,n,i){return Rb.subVectors(n,e),Pb.subVectors(t,e),Rb.cross(Pb).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return Rb.subVectors(this.c,this.b),Pb.subVectors(this.a,this.b),.5*Rb.cross(Pb).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Vb.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Vb.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return Vb.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return Vb.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Vb.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;Db.subVectors(i,n),kb.subVectors(r,n),zb.subVectors(t,n);const a=Db.dot(zb),l=kb.dot(zb);if(a<=0&&l<=0)return e.copy(n);Ub.subVectors(t,i);const c=Db.dot(Ub),u=kb.dot(Ub);if(c>=0&&u<=c)return e.copy(i);const h=a*u-c*l;if(h<=0&&a>=0&&c<=0)return s=a/(a-c),e.copy(n).addScaledVector(Db,s);Gb.subVectors(t,r);const d=Db.dot(Gb),p=kb.dot(Gb);if(p>=0&&d<=p)return e.copy(r);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(kb,o);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return Bb.subVectors(r,i),o=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(Bb,o);const f=1/(m+_+h);return s=_*f,o=h*f,e.copy(n).addScaledVector(Db,s).addScaledVector(kb,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Hb=0;class jb extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Hb++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=By,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=my,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Qy,this.stencilZFail=Qy,this.stencilZPass=Qy,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==By&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}jb.prototype.isMaterial=!0;const Wb={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},qb={h:0,s:0,l:0},Xb={h:0,s:0,l:0};function Yb(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function $b(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Jb(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Zb{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=ux(t,1),e=cx(e,0,1),n=cx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=Yb(r,i,t+1/3),this.g=Yb(r,i,t),this.b=Yb(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Wb[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=$b(t.r),this.g=$b(t.g),this.b=$b(t.b),this}copyLinearToSRGB(t){return this.r=Jb(t.r),this.g=Jb(t.g),this.b=Jb(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n65535?iw:nw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new gx).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return ow.makeRotationFromQuaternion(t),this.applyMatrix4(ow),this}rotateX(t){return ow.makeRotationX(t),this.applyMatrix4(ow),this}rotateY(t){return ow.makeRotationY(t),this.applyMatrix4(ow),this}rotateZ(t){return ow.makeRotationZ(t),this.applyMatrix4(ow),this}translate(t,e,n){return ow.makeTranslation(t,e,n),this.applyMatrix4(ow),this}scale(t,e,n){return ow.makeScale(t,e,n),this.applyMatrix4(ow),this}lookAt(t){return aw.lookAt(t),aw.updateMatrix(),this.applyMatrix4(aw.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(lw).negate(),this.translate(lw.x,lw.y,lw.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),mw.copy(n.boundingSphere),mw.applyMatrix4(r),!1===t.ray.intersectsSphere(mw))return;if(pw.copy(r).invert(),_w.copy(t.ray).applyMatrix4(pw),null!==n.boundingBox&&!1===_w.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:Nw.clone(),object:t}}(t,e,n,i,fw,gw,vw,Cw);if(p){a&&(Ew.fromBufferAttribute(a,c),Mw.fromBufferAttribute(a,u),Sw.fromBufferAttribute(a,h),p.uv=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx)),l&&(Ew.fromBufferAttribute(l,c),Mw.fromBufferAttribute(l,u),Sw.fromBufferAttribute(l,h),p.uv2=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx));const t={a:c,b:u,c:h,normal:new Nx,materialIndex:0};Vb.getNormal(fw,gw,vw,t.normal),p.face=t}return p}Lw.prototype.isMesh=!0;class Rw extends dw{constructor(t=1,e=1,n=1,i=1,r=1,s=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:s};const o=this;i=Math.floor(i),r=Math.floor(r),s=Math.floor(s);const a=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,s,p,_,m,f,g){const v=s/m,y=p/f,x=s/2,b=p/2,w=_/2,T=m+1,A=f+1;let E=0,M=0;const S=new Nx;for(let s=0;s0?1:-1,c.push(S.x,S.y,S.z),u.push(a/m),u.push(1-s/f),E+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}Dw.prototype.isShaderMaterial=!0;class kw extends Ob{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ob,this.projectionMatrix=new ob,this.projectionMatrixInverse=new ob}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}kw.prototype.isCamera=!0;class Bw extends kw{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*sx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*rx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*sx*Math.atan(Math.tan(.5*rx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,s){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*rx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const s=this.view;if(null!==this.view&&this.view.enabled){const t=s.fullWidth,o=s.fullHeight;r+=s.offsetX*i/t,e-=s.offsetY*n/o,i*=s.width/t,n*=s.height/o}const o=this.filmOffset;0!==o&&(r+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}Bw.prototype.isPerspectiveCamera=!0;const zw=90;class Uw extends Ob{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new Bw(zw,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Nx(1,0,0)),this.add(i);const r=new Bw(zw,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new Nx(-1,0,0)),this.add(r);const s=new Bw(zw,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new Nx(0,1,0)),this.add(s);const o=new Bw(zw,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new Nx(0,-1,0)),this.add(o);const a=new Bw(zw,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new Nx(0,0,1)),this.add(a);const l=new Bw(zw,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Nx(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,o,a,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class Gw extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:fy,n,i,r,s,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Gw.prototype.isCubeTexture=!0;class Vw extends Mx{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new Gw(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:Cy,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=By,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new Rw(5,5,5),r=new Dw({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:Pw(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const s=new Lw(i,r),o=e.minFilter;e.minFilter===Ly&&(e.minFilter=Cy);return new Uw(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}Vw.prototype.isWebGLCubeRenderTarget=!0;const Hw=new Nx,jw=new Nx,Ww=new gx;class qw{constructor(t=new Nx(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=Hw.subVectors(n,e).cross(jw.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Hw),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Ww.getNormalMatrix(t),i=this.coplanarPoint(Hw).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}qw.prototype.isPlane=!0;const Xw=new Zx,Yw=new Nx;class $w{constructor(t=new qw,e=new qw,n=new qw,i=new qw,r=new qw,s=new qw){this.planes=[t,e,n,i,r,s]}set(t,e,n,i,r,s){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(r),o[5].copy(s),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],s=n[2],o=n[3],a=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,u-a,_-h,v-m).normalize(),e[1].setComponents(o+i,u+a,_+h,v+m).normalize(),e[2].setComponents(o+r,u+l,_+d,v+f).normalize(),e[3].setComponents(o-r,u-l,_-d,v-f).normalize(),e[4].setComponents(o-s,u-c,_-p,v-g).normalize(),e[5].setComponents(o+s,u+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Xw.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSprite(t){return Xw.center.set(0,0,0),Xw.radius=.7071067811865476,Xw.applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)0?t.max.x:t.min.x,Yw.y=i.normal.y>0?t.max.y:t.min.y,Yw.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(Yw)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Jw(){let t=null,e=!1,n=null,i=null;function r(e,s){n(e,s),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Zw(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n}\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n}\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\treturn sheenTint * ( D * V );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float vFogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\treturn irradiance;\\\\n}\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\t#endif\\\\n}\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n#ifdef IOR\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n};\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\treturn fab;\\\\n}\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n}\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normal_pars_fragment:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_pars_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",output_fragment:\\\\\\\"#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\t}\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include \\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",depth_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main () {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshnormal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\"},tT={common:{diffuse:{value:new Zb(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gx},uv2Transform:{value:new gx},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new fx(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Zb(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Zb(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}},sprite:{diffuse:{value:new Zb(16777215)},opacity:{value:1},center:{value:new fx(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}}},eT={basic:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.fog]),vertexShader:Kw.meshbasic_vert,fragmentShader:Kw.meshbasic_frag},lambert:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshlambert_vert,fragmentShader:Kw.meshlambert_frag},phong:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},specular:{value:new Zb(1118481)},shininess:{value:30}}]),vertexShader:Kw.meshphong_vert,fragmentShader:Kw.meshphong_frag},standard:{uniforms:Iw([tT.common,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.roughnessmap,tT.metalnessmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag},toon:{uniforms:Iw([tT.common,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.gradientmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshtoon_vert,fragmentShader:Kw.meshtoon_frag},matcap:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,{matcap:{value:null}}]),vertexShader:Kw.meshmatcap_vert,fragmentShader:Kw.meshmatcap_frag},points:{uniforms:Iw([tT.points,tT.fog]),vertexShader:Kw.points_vert,fragmentShader:Kw.points_frag},dashed:{uniforms:Iw([tT.common,tT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Kw.linedashed_vert,fragmentShader:Kw.linedashed_frag},depth:{uniforms:Iw([tT.common,tT.displacementmap]),vertexShader:Kw.depth_vert,fragmentShader:Kw.depth_frag},normal:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,{opacity:{value:1}}]),vertexShader:Kw.meshnormal_vert,fragmentShader:Kw.meshnormal_frag},sprite:{uniforms:Iw([tT.sprite,tT.fog]),vertexShader:Kw.sprite_vert,fragmentShader:Kw.sprite_frag},background:{uniforms:{uvTransform:{value:new gx},t2D:{value:null}},vertexShader:Kw.background_vert,fragmentShader:Kw.background_frag},cube:{uniforms:Iw([tT.envmap,{opacity:{value:1}}]),vertexShader:Kw.cube_vert,fragmentShader:Kw.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Kw.equirect_vert,fragmentShader:Kw.equirect_frag},distanceRGBA:{uniforms:Iw([tT.common,tT.displacementmap,{referencePosition:{value:new Nx},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Kw.distanceRGBA_vert,fragmentShader:Kw.distanceRGBA_frag},shadow:{uniforms:Iw([tT.lights,tT.fog,{color:{value:new Zb(0)},opacity:{value:1}}]),vertexShader:Kw.shadow_vert,fragmentShader:Kw.shadow_frag}};function nT(t,e,n,i,r){const s=new Zb(0);let o,a,l=0,c=null,u=0,h=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,r)}return{getClearColor:function(){return s},setClearColor:function(t,e=1){s.set(t),l=e,d(s,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(s,l)},render:function(n,r){let p=!1,_=!0===r.isScene?r.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(s,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===xy)?(void 0===a&&(a=new Lw(new Rw(1,1,1),new Dw({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:Pw(eT.cube.uniforms),vertexShader:eT.cube.vertexShader,fragmentShader:eT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&u===_.version&&h===t.toneMapping||(a.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new Lw(new Qw(2,2),new Dw({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:Pw(eT.background.uniforms),vertexShader:eT.background.vertexShader,fragmentShader:eT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&u===_.version&&h===t.toneMapping||(o.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function iT(t,e,n,i){const r=t.getParameter(34921),s=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==s,a={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):s.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):s.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let s=l[e];if(void 0===s&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&r.instanceMatrix&&(s=r.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&r.instanceColor&&(s=r.instanceColor)),void 0!==s){const e=s.normalized,o=s.itemSize,l=n.get(s);if(void 0===l)continue;const c=l.buffer,u=l.type,h=l.bytesPerElement;if(s.isInterleavedBufferAttribute){const n=s.data,l=n.stride,d=s.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const s=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=r(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,u=t.getParameter(34930),h=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=h>0,y=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(36183):0}}function oT(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new qw,a=new gx,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length0){const o=t.getRenderTarget(),a=new Vw(s.height/2);return a.fromEquirectangularTexture(t,r),e.set(r,a),t.setRenderTarget(o),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}eT.physical={uniforms:Iw([eT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new fx(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new Zb(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new fx},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Zb(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Zb(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag};class lT extends kw{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=s,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,s){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,s=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,s=r+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}lT.prototype.isOrthographicCamera=!0;class cT extends Dw{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}cT.prototype.isRawShaderMaterial=!0;const uT=Math.pow(2,8),hT=[.125,.215,.35,.446,.526,.582],dT=5+hT.length,pT=20,_T={[Yy]:0,[$y]:1,[Zy]:2,3004:3,3005:4,3006:5,[Jy]:6},mT=new lT,{_lodPlanes:fT,_sizeLods:gT,_sigmas:vT}=MT(),yT=new Zb;let xT=null;const bT=(1+Math.sqrt(5))/2,wT=1/bT,TT=[new Nx(1,1,1),new Nx(-1,1,1),new Nx(1,1,-1),new Nx(-1,1,-1),new Nx(0,bT,wT),new Nx(0,bT,-wT),new Nx(wT,0,bT),new Nx(-wT,0,bT),new Nx(bT,wT,0),new Nx(-bT,wT,0)];class AT{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Nx(0,1,0);return new cT({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(pT),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){xT=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=LT(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=NT(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?uT:0,uT,uT),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===By&&e.type===Oy&&e.encoding===$y?t.value=_T[3e3]:t.value=_T[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=LT()):null==this._equirectShader&&(this._equirectShader=NT());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new Lw(fT[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),CT(e,0,0,3*uT,2*uT),n.setRenderTarget(e),n.render(r,mT)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;epT&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,mT)}}function ET(t){return void 0!==t&&t.type===Oy&&(t.encoding===Yy||t.encoding===$y||t.encoding===Jy)}function MT(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=hT[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new dw;y.setAttribute(\\\\\\\"position\\\\\\\",new ew(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new ew(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new ew(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function ST(t){const e=new Mx(3*uT,3*uT,t);return e.texture.mapping=xy,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function CT(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function NT(){const t=new fx(1,1);return new cT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function LT(){return new cT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function OT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function RT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function PT(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const 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sE(t,e){const n=Kw[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return rE(n)}const oE=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,aE=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function lE(t){return t.replace(aE,uE).replace(oE,cE)}function cE(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),uE(t,e,n,i)}function uE(t,e,n,i){let r=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(tE).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[hE(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define 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y=v+g+a,x=XA(r,35633,v+f+o),b=XA(r,35632,y);if(r.attachShader(m,x),r.attachShader(m,b),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(x).trim(),n=r.getShaderInfoLog(b).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,35714)){i=!1;const e=JA(r,x,\\\\\\\"vertex\\\\\\\"),n=JA(r,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(s=!1);s&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return r.deleteShader(x),r.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new qA(r,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let r=0;r0,C=s.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:s.type,vertexShader:A,fragmentShader:E,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==M?f(M.texture):t.outputEncoding,map:!!s.map,mapEncoding:f(s.map),matcap:!!s.matcap,matcapEncoding:f(s.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===xy||b.mapping===by),lightMap:!!s.lightMap,lightMapEncoding:f(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:f(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:1===s.normalMapType,tangentSpaceNormalMap:0===s.normalMapType,clearcoat:C,clearcoatMap:C&&!!s.clearcoatMap,clearcoatRoughnessMap:C&&!!s.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,specularIntensityMap:!!s.specularIntensityMap,specularTintMap:!!s.specularTintMap,specularTintMapEncoding:f(s.specularTintMap),alphaMap:!!s.alphaMap,alphaTest:S,gradientMap:!!s.gradientMap,sheen:s.sheen>0,transmission:s.transmission>0,transmissionMap:!!s.transmissionMap,thicknessMap:!!s.thicknessMap,combine:s.combine,vertexTangents:!!s.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatMap||s.clearcoatRoughnessMap||s.clearcoatNormalMap||s.displacementMap||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmission>0||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap||!s.displacementMap),fog:!!y,useFog:s.fog,fogExp2:y&&y.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:s.format,dithering:s.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:s.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,doubleSided:2===s.side,flipSided:1===s.side,depthPacking:void 0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||mE),r.length>1&&r.sort(e||fE),s.length>1&&s.sort(e||fE)}}}function vE(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new gE(t),e.set(n,[r])):i>=e.get(n).length?(r=new gE(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function yE(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new Nx,color:new Zb};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new Nx,direction:new Nx,color:new Zb,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new Nx,color:new Zb,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new Nx,skyColor:new Zb,groundColor:new Zb};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new Zb,position:new Nx,halfWidth:new Nx,halfHeight:new Nx}}return t[e.id]=n,n}}}let xE=0;function bE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function wE(t,e){const n=new yE,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new Nx);const s=new Nx,o=new ob,a=new ob;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(bE);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_FLOAT_1,r.rectAreaLTC2=tT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_HALF_1,r.rectAreaLTC2=tT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=xE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new TE(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class EE extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}EE.prototype.isMeshDepthMaterial=!0;class ME extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Nx,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}ME.prototype.isMeshDistanceMaterial=!0;function SE(t,e,n){let i=new $w;const r=new fx,s=new fx,o=new Ex,a=new EE({depthPacking:3201}),l=new ME,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new Dw({uniforms:{shadow_pass:{value:null},resolution:{value:new fx},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include \\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const _=new dw;_.setAttribute(\\\\\\\"position\\\\\\\",new ew(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new Lw(_,d),f=this;function g(n,i){const r=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,d.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,p,m,null)}function v(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function y(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;lu||r.y>u)&&(r.x>u&&(s.x=Math.floor(u/p.x),r.x=s.x*p.x,h.mapSize.x=s.x),r.y>u&&(s.y=Math.floor(u/p.y),r.y=s.y*p.y,h.mapSize.y=s.y)),null===h.map&&!h.isPointLightShadow&&3===this.type){const t={minFilter:Cy,magFilter:Cy,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Mx(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){const t={minFilter:Ey,magFilter:Ey,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.camera.updateProjectionMatrix()}t.setRenderTarget(h.map),t.clear();const _=h.getViewportCount();for(let t=0;t<_;t++){const e=h.getViewport(t);o.set(s.x*e.x,s.y*e.y,s.x*e.z,s.y*e.w),d.viewport(o),h.updateMatrices(c,t),i=h.getFrustum(),y(n,a,h.camera,c,this.type)}h.isPointLightShadow||3!==this.type||g(h,a),h.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,h)}}function CE(t,e,n){const i=n.isWebGL2;const r=new function(){let e=!1;const n=new Ex;let i=null;const r=new Ex(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,s,o,a){!0===a&&(e*=o,i*=o,s*=o),n.set(e,i,s,o),!1===r.equals(n)&&(t.clearColor(e,i,s,o),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}},s=new function(){let e=!1,n=null,i=null,r=null;return{setTest:function(t){t?k(2929):B(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?k(2960):B(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&r===n&&s===o||(t.stencilFunc(e,n,o),i=e,r=n,s=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},u=null,h=!1,d=null,p=null,_=null,m=null,f=null,g=null,v=null,y=!1,x=null,b=null,w=null,T=null,A=null;const E=t.getParameter(35661);let M=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),M=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),M=S>=2);let N=null,L={};const O=t.getParameter(3088),R=t.getParameter(2978),P=(new Ex).fromArray(O),I=(new Ex).fromArray(R);function F(e,n,i){const r=new Uint8Array(4),s=t.createTexture();t.bindTexture(e,s),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(r=i/Math.max(t.width,t.height)),r<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?_x:Math.floor,s=i(r*t.width),o=i(r*t.height);void 0===p&&(p=m(s,o));const a=n?m(s,o):p;a.width=s,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,s,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+s+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return t}function g(t){return dx(t.width)&&dx(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==Ey&&t.minFilter!==Cy}function y(e,n,r,s,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(r,s,o))}function x(n,i,r,s){if(!1===a)return i;if(null!==n){if(void 0!==t[n])return t[n];console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\"+n+\\\\\\\"'\\\\\\\")}let o=i;return 6403===i&&(5126===r&&(o=33326),5131===r&&(o=33325),5121===r&&(o=33321)),6407===i&&(5126===r&&(o=34837),5131===r&&(o=34843),5121===r&&(o=32849)),6408===i&&(5126===r&&(o=34836),5131===r&&(o=34842),5121===r&&(o=s===$y?35907:32856)),33325!==o&&33326!==o&&34842!==o&&34836!==o||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(t){return t===Ey||t===My||t===Sy?9728:9729}function w(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",w),function(e){const n=i.get(e);if(void 0===n.__webglInit)return;t.deleteTexture(n.__webglTexture),i.remove(e)}(n),n.isVideoTexture&&d.delete(n),o.memory.textures--}function T(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",T),function(e){const n=e.texture,r=i.get(e),s=i.get(n);if(!e)return;void 0!==s.__webglTexture&&(t.deleteTexture(s.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(r.__webglFramebuffer[e]),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer[e]);else t.deleteFramebuffer(r.__webglFramebuffer),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer),r.__webglMultisampledFramebuffer&&t.deleteFramebuffer(r.__webglMultisampledFramebuffer),r.__webglColorRenderbuffer&&t.deleteRenderbuffer(r.__webglColorRenderbuffer),r.__webglDepthRenderbuffer&&t.deleteRenderbuffer(r.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,r=n.length;e0&&r.__version!==t.version){const n=t.image;if(void 0===n)console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");else{if(!1!==n.complete)return void O(r,t,e);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(33984+e),n.bindTexture(3553,r.__webglTexture)}function M(e,r){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,r){if(6!==i.image.length)return;L(e,i),n.activeTexture(33984+r),n.bindTexture(34067,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const o=i&&(i.isCompressedTexture||i.image[0].isCompressedTexture),l=i.image[0]&&i.image[0].isDataTexture,u=[];for(let t=0;t<6;t++)u[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const h=u[0],d=g(h)||a,p=s.convert(i.format),_=s.convert(i.type),m=x(i.internalFormat,p,_,i.encoding);let b;if(N(34067,i,d),o){for(let t=0;t<6;t++){b=u[t].mipmaps;for(let e=0;e1||i.get(s).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(s.anisotropy,r.getMaxAnisotropy())),i.get(s).__currentAnisotropy=s.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",w),e.__webglTexture=t.createTexture(),o.memory.textures++)}function O(e,i,r){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=function(t){return!a&&(t.wrapS!==Ty||t.wrapT!==Ty||t.minFilter!==Ey&&t.minFilter!==Cy)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=6402,a?m=i.type===Iy?36012:i.type===Py?33190:i.type===Dy?35056:33189:i.type===Iy&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===zy&&6402===m&&i.type!==Ry&&i.type!==Py&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=Ry,_=s.convert(i.type)),i.format===Uy&&6402===m&&(m=34041,i.type!==Dy&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=Dy,_=s.convert(i.type))),n.texImage2D(3553,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let t=0,e=b.length;t0&&h){for(let t=0,e=b.length;t=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),A+=1,t},this.resetTextureUnits=function(){A=0},this.setTexture2D=E,this.setTexture2DArray=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=M,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==ky||l.type!==Iy&&l.type!==Fy||(l.format=By,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;ea+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class FE extends nx{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new Bw;w.layers.enable(1),w.viewport=new Ex;const T=new Bw;T.layers.enable(2),T.viewport=new Ex;const A=[w,T],E=new OE;E.layers.enable(1),E.layers.enable(2);let M=null,S=null;function C(t){const e=b.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function N(){b.forEach((function(t,e){t.disconnect(e)})),b.clear(),M=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),u=null,_=null,m=null,f=null,d=null,h=null,c=null,r=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=r.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function kE(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=yx(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const p=[],_=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=Yy,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const m=this;let f=!1,g=0,v=0,y=null,x=-1,b=null;const w=new Ex,T=new Ex;let A=null,E=e.width,M=e.height,S=1,C=null,N=null;const L=new Ex(0,0,E,M),O=new Ex(0,0,E,M);let R=!1;const P=[],I=new $w;let F=!1,D=!1,k=null;const B=new ob,z=new Nx,U={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function G(){return null===y?S:1}let V,H,j,W,q,X,Y,$,J,Z,Q,K,tt,et,nt,it,rt,st,ot,at,lt,ct,ut,ht=n;function dt(t,n){for(let i=0;i0&&function(t,e,n){if(null===k){const t=!0===o&&!0===H.isWebGL2;k=new(t?Sx:Mx)(1024,1024,{generateMipmaps:!0,type:null!==ct.convert(Fy)?Fy:Oy,minFilter:Ly,magFilter:Ey,wrapS:Ty,wrapT:Ty})}const i=m.getRenderTarget();m.setRenderTarget(k),m.clear();const r=m.toneMapping;m.toneMapping=0,At(t,e,n),m.toneMapping=r,X.updateMultisampleRenderTarget(k),X.updateRenderTargetMipmap(k),m.setRenderTarget(i)}(r,e,n),i&&j.viewport(w.copy(i)),r.length>0&&At(r,e,n),s.length>0&&At(s,e,n),a.length>0&&At(a,e,n)}function At(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?_[_.length-1]:null,p.pop(),h=p.length>0?p[p.length-1]:null},this.getActiveCubeFace=function(){return g},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTarget=function(t,e=0,n=0){y=t,g=e,v=n,t&&void 0===q.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=R;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(P.length!==n.length||36064!==P[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&ht.readPixels(e,n,i,r,ct.convert(a),ct.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==y?q.get(y).__webglFramebuffer:null;j.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=ct.convert(e.format);H.isWebGL2&&(6407===o&&(o=32849),6408===o&&(o=32856)),X.setTexture2D(e,0),ht.copyTexImage2D(3553,n,o,t.x,t.y,r,s,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ht.pixelStorei(37440,n.flipY),ht.pixelStorei(37441,n.premultiplyAlpha),ht.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ht.texSubImage2D(3553,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?ht.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ht.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ht.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports 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srcTexture.\\\\\\\"),ht.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,g.data)):ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g),ht.pixelStorei(3314,h),ht.pixelStorei(32878,d),ht.pixelStorei(3316,p),ht.pixelStorei(3315,_),ht.pixelStorei(32877,f),0===r&&i.generateMipmaps&&ht.generateMipmap(u),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ut.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends kE{}).prototype.isWebGL1Renderer=!0;class BE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.density=e}clone(){return new BE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}BE.prototype.isFogExp2=!0;class zE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.near=e,this.far=n}clone(){return new zE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}zE.prototype.isFog=!0;class UE extends Ob{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}UE.prototype.isScene=!0;class GE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Ky,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=lx()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;it.far||e.push({distance:a,point:qE.clone(),uv:Vb.getUV(qE,QE,KE,tM,eM,nM,iM,new fx),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function sM(t,e,n,i,r,s){$E.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(JE.x=s*$E.x-r*$E.y,JE.y=r*$E.x+s*$E.y):JE.copy($E),t.copy(e),t.x+=JE.x,t.y+=JE.y,t.applyMatrix4(ZE)}rM.prototype.isSprite=!0;const oM=new Nx,aM=new Ex,lM=new Ex,cM=new Nx,uM=new ob;class hM extends Lw{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ob,this.bindMatrixInverse=new ob}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new Ex,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);dt.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;na)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);it.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}MM.prototype.isLine=!0;const SM=new Nx,CM=new Nx;class NM extends MM{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function kM(t,e,n,i,r,s,o){const a=PM.distanceSqToPoint(t);if(ar.far)return;s.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}DM.prototype.isPoints=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:ky,this.minFilter=void 0!==s?s:Cy,this.magFilter=void 0!==r?r:Cy,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class BM extends Tx{constructor(t,e,n,i,r,s,o,a,l,c,u,h){super(null,s,o,a,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}BM.prototype.isCompressedTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){if((c=void 0!==c?c:zy)!==zy&&c!==Uy)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===zy&&(n=Ry),void 0===n&&c===Uy&&(n=Dy),super(null,i,r,s,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:Ey,this.minFilter=void 0!==a?a:Ey,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new Nx,new Nx,new Nx,new Vb;class zM{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const s=this.getPoint(i),o=this.getPoint(r),a=e||(s.isVector2?new fx:new Nx);return a.copy(o).sub(s).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Nx,i=[],r=[],s=[],o=new Nx,a=new ob;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Nx)}r[0]=new Nx,s[0]=new Nx;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],o),s[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),s[e]=s[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(cx(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(a.makeRotationAxis(o,t))}s[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(cx(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(o.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),s[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:s}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class UM extends zM{constructor(t=0,e=0,n=1,i=1,r=0,s=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=s,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new fx,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const s=Math.abs(r)i;)r-=i;r0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?o=i[(l-1)%r]:(HM.subVectors(i[0],i[1]).add(i[0]),o=HM);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2i.length-2?i.length-1:s+1],u=i[s>i.length-3?i.length-1:s+2];return n.set(YM(o,a.x,l.x,c.x,u.x),YM(o,a.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class oS extends sS{constructor(t){super(t),this.uuid=lx(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n80*n){a=c=t[0],l=u=t[1];for(let e=n;ec&&(c=h),d>u&&(u=d);p=Math.max(c-a,u-l),p=0!==p?1/p:0}return uS(s,o,n,a,l,p),o};function lS(t,e,n,i,r){let s,o;if(r===function(t,e,n,i){let r=0;for(let s=e,o=n-i;s0)for(s=e;s=e;s-=i)o=NS(s,t[s],t[s+1],o);return o&&TS(o,o.next)&&(LS(o),o=o.next),o}function cS(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!TS(i,i.next)&&0!==wS(i.prev,i,i.next))i=i.next;else{if(LS(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function uS(t,e,n,i,r,s,o){if(!t)return;!o&&s&&function(t,e,n,i){let 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wS(t.prev,t,t.next)<0?wS(t,e,t.next)>=0&&wS(t,t.prev,e)>=0:wS(t,e,t.prev)<0||wS(t,t.next,e)<0}function CS(t,e){const n=new OS(t.i,t.x,t.y),i=new OS(e.i,e.x,e.y),r=t.next,s=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,s.next=i,i.prev=s,i}function NS(t,e,n,i){const r=new OS(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function LS(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function OS(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class RS{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r2&&t[e-1].equals(t[0])&&t.pop()}function IS(t,e){for(let n=0;nNumber.EPSILON){const h=Math.sqrt(u),d=Math.sqrt(l*l+c*c),p=e.x-a/h,_=e.y+o/h,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,r=_+a*m-t.y;const f=i*i+r*r;if(f<=2)return new fx(i,r);s=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new fx(i/s,r/s)}const O=[];for(let t=0,e=M.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=M.length;t=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*p;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}US.prototype.isMeshPhysicalMaterial=!0;class GS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new Zb(16777215),this.specular=new Zb(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}GS.prototype.isMeshPhongMaterial=!0;class VS extends jb{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}VS.prototype.isMeshToonMaterial=!0;class HS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}HS.prototype.isMeshNormalMaterial=!0;class jS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}jS.prototype.isMeshLambertMaterial=!0;class WS extends jb{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new Zb(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}WS.prototype.isMeshMatcapMaterial=!0;class qS extends xM{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}qS.prototype.isLineDashedMaterial=!0;const XS={arraySlice:function(t,e,n){return XS.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let s=0,o=0;o!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[o++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let o=s[i];if(void 0!==o)if(Array.isArray(o))do{o=s[i],void 0!==o&&(e.push(s.time),n.push.apply(n,o)),s=t[r++]}while(void 0!==s);else if(void 0!==o.toArray)do{o=s[i],void 0!==o&&(e.push(s.time),o.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{o=s[i],void 0!==o&&(e.push(s.time),n.push(o)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const s=t.clone();s.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;ns.tracks[t].times[0]&&(a=s.tracks[t].times[0]);for(let t=0;t=i.times[h]){const t=h*l+a,e=t+l-a;d=XS.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(s),d=XS.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===r){(new Cx).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;te;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=XS.arraySlice(n,r,s),this.values=XS.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&XS.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=XS.arraySlice(this.times),e=XS.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Hy,r=t.length-1;let s=1;for(let o=1;o0){t[s]=t[r];for(let t=r*n,i=s*n,o=0;o!==n;++o)e[i+o]=e[t+o];++s}return s!==t.length?(this.times=XS.arraySlice(t,0,s),this.values=XS.arraySlice(e,0,s*n)):(this.times=t,this.values=e),this}clone(){const t=XS.arraySlice(this.times,0),e=XS.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}QS.prototype.TimeBufferType=Float32Array,QS.prototype.ValueBufferType=Float32Array,QS.prototype.DefaultInterpolation=Vy;class KS extends QS{}KS.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",KS.prototype.ValueBufferType=Array,KS.prototype.DefaultInterpolation=Gy,KS.prototype.InterpolantFactoryMethodLinear=void 0,KS.prototype.InterpolantFactoryMethodSmooth=void 0;class tC extends QS{}tC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class eC extends QS{}eC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class nC extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)Cx.slerpFlat(r,0,s,l-o,s,l,a);return r}}class iC extends QS{InterpolantFactoryMethodLinear(t){return new nC(this.times,this.values,this.getValueSize(),t)}}iC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",iC.prototype.DefaultInterpolation=Vy,iC.prototype.InterpolantFactoryMethodSmooth=void 0;class rC extends QS{}rC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",rC.prototype.ValueBufferType=Array,rC.prototype.DefaultInterpolation=Gy,rC.prototype.InterpolantFactoryMethodLinear=void 0,rC.prototype.InterpolantFactoryMethodSmooth=void 0;class sC extends QS{}sC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class oC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=lx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(aC(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(QS.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,s=[];for(let t=0;t1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,r){if(0!==n.length){const s=[],o=[];XS.flattenJSON(n,s,o,i),0!==s.length&&r.push(new t(e,s,o))}},i=[],r=t.name||\\\\\\\"default\\\\\\\",s=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Cx.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;Cx.multiplyQuaternionsFlat(t,s,t,e,t,n),Cx.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}const HC=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",jC=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),WC=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",qC=\\\\\\\"[^\\\\\\\"+HC.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",XC=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",WC),YC=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",qC),$C=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",WC),JC=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",WC),ZC=new RegExp(\\\\\\\"^\\\\\\\"+XC+YC+$C+JC+\\\\\\\"$\\\\\\\"),QC=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class KC{constructor(t,e,n){this.path=e,this.parsedPath=n||KC.parseTrackName(e),this.node=KC.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new KC.Composite(t,e,n):new KC(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(jC,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=ZC.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==QC.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case Xy:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=2202===n;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Wy,i.endingEnd=Wy):(i.endingStart=t?this.zeroSlopeAtStart?Wy:jy:qy,i.endingEnd=e?this.zeroSlopeAtEnd?Wy:jy:qy)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}(class extends nx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new VC(KC.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;tthis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return nN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}iN.prototype.isBox2=!0;const rN=new Nx,sN=new Nx;class oN{constructor(t=new Nx,e=new Nx){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){rN.subVectors(t,this.start),sN.subVectors(this.end,this.start);const n=sN.dot(sN);let i=sN.dot(rN)/n;return e&&(i=cx(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends Ob{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const aN=new Nx,lN=new ob,cN=new ob;function uN(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n8&&t<16&&e[n].split(t+1),0!==i&&this.subTrees.push(e[n])}return this}build(){return this.calcBox(),this.split(0),this}getRayTriangles(t,e){for(let n=0;n0)for(let t=0;t0&&i>0||n<-t.radius&&i<-t.radius)return!1;const r=Math.abs(n/(Math.abs(n)+Math.abs(i))),s=fN.copy(t.start).lerp(t.end,r);if(e.containsPoint(s))return{normal:vN.normal.clone(),point:s.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=yN.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e0)for(let t=0;t0)for(let t=0;ta&&(i=o.clone().add(t.origin),r=a,n=e[s])}}return r<1e100&&{distance:r,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new AN(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}}const MN={type:\\\\\\\"change\\\\\\\"},SN={type:\\\\\\\"lock\\\\\\\"},CN={type:\\\\\\\"unlock\\\\\\\"},NN=Math.PI/2;class LN extends EN{constructor(t,e){super(),this.camera=t,this.domElement=e,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.speed=1,this.euler=new Wv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.vec=new p.a,this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._cameraTmp=new tf.a,this.velocity=new p.a,this.direction=new p.a,this._moveForward=!1,this._moveBackward=!1,this._moveLeft=!1,this._moveRight=!1,this.prevTime=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e,this.euler.x-=.002*n,this.euler.x=Math.max(NN-this.maxPolarAngle,Math.min(NN-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this.dispatchEvent(MN)}}onPointerlockChange(){this.velocity.set(0,0,0),this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(SN),this.isLocked=!0):(this.dispatchEvent(CN),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}moveForward(t,e){this.vec.setFromMatrixColumn(t.matrix,0),this.vec.crossVectors(t.up,this.vec),t.position.addScaledVector(this.vec,e)}moveRight(t,e){this.vec.setFromMatrixColumn(t.matrix,0),t.position.addScaledVector(this.vec,e)}_copyToCameraTmp(){this._cameraTmp.position.copy(this.camera.position),this._cameraTmp.matrix.copy(this.camera.matrix),this._cameraTmp.up.copy(this.camera.up)}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}setMoveForward(t){this._moveForward=t}setMoveBackward(t){this._moveBackward=t}setMoveLeft(t){this._moveLeft=t}setMoveRight(t){this._moveRight=t}update(){const t=performance.now();if(!0===this.isLocked){const e=(t-this.prevTime)/1e3;if(this.velocity.x-=10*this.velocity.x*e,this.velocity.z-=10*this.velocity.z*e,this.velocity.y-=9.8*100*e,this.direction.z=Number(this._moveForward)-Number(this._moveBackward),this.direction.x=Number(this._moveRight)-Number(this._moveLeft),this.direction.normalize(),(this._moveForward||this._moveBackward)&&(this.velocity.z-=400*this.direction.z*e*this.speed),(this._moveLeft||this._moveRight)&&(this.velocity.x-=400*this.direction.x*e*this.speed),this._playerCollisionController){this._copyToCameraTmp(),this.moveRight(this._cameraTmp,-this.velocity.x*e),this.moveForward(this._cameraTmp,-this.velocity.z*e);const t=this._playerCollisionController.testPosition(this._cameraTmp.position);t?(this._cameraTmp.position.add(t.normal.multiplyScalar(t.depth)),this.camera.position.copy(this._cameraTmp.position)):this._applyVelocity(e)}this._applyVelocity(e)}this.prevTime=t}_applyVelocity(t){this.moveRight(this.camera,-this.velocity.x*t),this.moveForward(this.camera,-this.velocity.z*t)}}async function ON(t,e){var n;if(e.pv.collideWithGeo){const i=e.pv.collidingGeo.nodeWithContext(Ki.OBJ);if(i){await i.compute();const r=await(null===(n=i.displayNodeController)||void 0===n?void 0:n.displayNode()),s=(await(null==r?void 0:r.compute())).coreContent();if(!s)return void console.error(\\\\\\\"obj node contains invalid sop\\\\\\\");const o=s.objectsWithGeo()[0];t.setCheckCollisions(o),t.setCollisionCapsule(new mN(new p.a(0,e.pv.capsuleHeightRange.x,0),new p.a(e.pv.capsuleHeightRange.y),e.pv.capsuleRadius))}}else t.setCheckCollisions()}const RN=\\\\\\\"lock\\\\\\\",PN=\\\\\\\"change\\\\\\\",IN=\\\\\\\"unlock\\\\\\\";const FN=new class extends aa{constructor(){super(...arguments),this.lock=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_lock_controls(t)}}),this.minPolarAngle=oa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.speed=oa.FLOAT(1),this.collideWithGeo=oa.BOOLEAN(0),this.collidingGeo=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.GEO]},visibleIf:{collideWithGeo:!0}}),this.recomputeCollidingGeo=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class DN extends jv{constructor(){super(...arguments),this.paramsConfig=FN,this._controls_by_element_id=new Map}static type(){return rr.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(RN,$o.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(RN,$o.BASE),new Jo(PN,$o.BASE),new Jo(IN,$o.BASE)])}async create_controls_instance(t,e){const n=new LN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(RN,(()=>{this._createKeysEvents(t),this.dispatchEventToOutput(RN,{})})),t.addEventListener(PN,(()=>{this.dispatchEventToOutput(PN,{})})),t.addEventListener(IN,(()=>{this._removeKeysEvents(),this.dispatchEventToOutput(IN,{})}))}update_required(){return!0}setup_controls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.speed=this.pv.speed,this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}lockControls(){let t;this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t&&t.lock()}static PARAM_CALLBACK_lock_controls(t){t.lockControls()}_onKeyDown(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!0);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!0);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!0);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!0)}}_onKeyUp(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!1)}}_createKeysEvents(t){this._onKeyDownBound=e=>{this._onKeyDown(e,t)},this._onKeyUpBound=e=>{this._onKeyUp(e,t)},document.addEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound)}_removeKeysEvents(){this._onKeyDownBound&&this._onKeyUpBound&&(document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound))}}var kN,BN;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}(kN||(kN={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(BN||(BN={}));const zN=new class extends aa{constructor(){super(...arguments),this.maxCount=oa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=oa.BUTTON(null,{callback:t=>{UN.PARAM_CALLBACK_reset(t)}})}};class UN extends Ba{constructor(){super(...arguments),this.paramsConfig=zN,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(kN.TRIGGER,$o.BASE,this.process_event_trigger.bind(this)),new Jo(kN.RESET,$o.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(BN.OUT,$o.BASE),new Jo(BN.LAST,$o.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class jN{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}}const WN={rotationSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI},translationSpeed:.1},qN={type:\\\\\\\"change\\\\\\\"};class XN extends EN{constructor(t,e){super(),this._camera=t,this.domElement=e,this.translationData={direction:{x:0,y:0}},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this)},this._startCameraRotation=new Wv.a,this._velocity=new p.a,this._rotationSpeed=WN.rotationSpeed,this._rotationRange={min:WN.rotationRange.min,max:WN.rotationRange.max},this._translationSpeed=WN.translationSpeed,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this.prevTime=performance.now(),this._camTmpPost=new p.a,this._camWorldDir=new p.a,this._up=new p.a(0,1,0),this._camSideVector=new p.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),r=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${r}px`,t.style.left=`${r}px`,t}_addEvents(){var t;jN.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;jN.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}setTranslationSpeed(t){this._translationSpeed=t}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=rs.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this.dispatchEvent(qN)}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);const e=this._getTouch(t,this._translateDomElement);e&&(this._translationStartPosition.set(e.clientX,e.clientY),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect())}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationSpeed*this._translationDelta.x/this._translateDomElementRect.width,this.translationData.direction.y=this._translationSpeed*-this._translationDelta.y/this._translateDomElementRect.height,this.dispatchEvent(qN))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.y=0}update(){const t=performance.now(),e=t-this.prevTime;this.prevTime=t,this._translateCamera(this.translationData,e)}_translateCamera(t,e){this._camera.getWorldDirection(this._camWorldDir),this._camWorldDir.y=0,this._camWorldDir.normalize(),this._camSideVector.crossVectors(this._up,this._camWorldDir),this._camSideVector.normalize(),this._camSideVector.multiplyScalar(-t.direction.x),this._camWorldDir.multiplyScalar(t.direction.y),this._velocity.copy(this._camWorldDir),this._velocity.add(this._camSideVector);const n=this._camera.position.y;if(this._camTmpPost.copy(this._camera.position),this._playerCollisionController){const t=1;this._velocity.addScaledVector(this._velocity,t);const n=this._velocity.clone().multiplyScalar(e);this._camTmpPost.add(n);const i=this._playerCollisionController.testPosition(this._camTmpPost);i&&this._camTmpPost.add(i.normal.multiplyScalar(i.depth)),this._camera.position.copy(this._camTmpPost)}else this._camTmpPost.add(this._camSideVector),this._camTmpPost.add(this._camWorldDir),this._camera.position.copy(this._camTmpPost);this._camera.position.y=n}_getTouch(t,e){for(let n=0;n{QN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class QN extends jv{constructor(){super(...arguments),this.paramsConfig=ZN,this._controls_by_element_id=new Map}static type(){return rr.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(YN,$o.BASE),new Jo($N,$o.BASE),new Jo(JN,$o.BASE)])}async create_controls_instance(t,e){const n=new XN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(YN,(()=>{this.dispatchEventToOutput(YN,{})})),t.addEventListener($N,(()=>{this.dispatchEventToOutput($N,{})})),t.addEventListener(JN,(()=>{this.dispatchEventToOutput(JN,{})}))}update_required(){return!0}setup_controls(t){t.setRotationSpeed(this.pv.rotationSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle}),t.setTranslationSpeed(this.pv.translationSpeed),this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}}var KN;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(KN||(KN={}));const tL=[KN.ALL_TOGETHER,KN.BATCH];const eL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{nL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=oa.BOOLEAN(0),this.cookMode=oa.INTEGER(tL.indexOf(KN.ALL_TOGETHER),{menu:{entries:tL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=oa.INTEGER(1,{visibleIf:{cookMode:tL.indexOf(KN.BATCH)},separatorAfter:!0}),this.updateResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_print_resolve(t)}})}};class nL extends Ba{constructor(){super(...arguments),this.paramsConfig=eL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(nL.INPUT_TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(nL.OUTPUT_FIRST_NODE,$o.BASE),new Jo(nL.OUTPUT_EACH_NODE,$o.BASE),new Jo(nL.OUTPUT_ALL_NODES,$o.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=tL[this.pv.cookMode];switch(t){case KN.ALL_TOGETHER:return this._cook_nodes_all_together();case KN.BATCH:return this._cook_nodes_batch()}ar.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(nL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var iL,rL;nL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",nL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",nL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",nL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(iL||(iL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(rL||(rL={}));const sL=new class extends aa{};class oL extends Ba{constructor(){super(...arguments),this.paramsConfig=sL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(iL.TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(rL.OUT,$o.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(rL.OUT,t)}}var aL,lL=n(39),cL=n(36);class uL{constructor(t,e,n=0,i=1/0){this.ray=new lL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new cL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return dL(t,this,n,e),n.sort(hL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,r=t.length;i{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=mL[this._node.pv.intersectWith];switch(e){case _L.GEOMETRY:return this._intersect_with_geometry(t);case _L.PLANE:return this._intersect_with_plane(t)}ar.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=kr[this._node.pv.geoAttributeType],n=gL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Dr.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Dr.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ar.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Nr(t.object.constructor)){case Sr.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Sr.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const r=i.getAttribute(e);if(r){switch(n){case Dr.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(r,t.face.a),this._uvB.fromBufferAttribute(r,t.face.b),this._uvC.fromBufferAttribute(r,t.face.c),t.uv=Qr.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Dr.STRING:{const t=new ps(i).points()[0];return t?t.stringAttribValue(e):void 0}}ar.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Dr.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Dr.STRING:{const n=new ps(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ar.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(ai.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Es.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Es.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(Ki.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=SL[this._node.pv.targetType];switch(t){case ML.NODE:return this._update_target_from_node();case ML.SCENE_GRAPH:return this._update_target_from_scene_graph()}ar.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(Ki.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}gL._vA=new p.a,gL._vB=new p.a,gL._vC=new p.a,gL._uvA=new d.a,gL._uvB=new d.a,gL._uvC=new d.a,gL._hitUV=new d.a;class vL{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,r=i.renderController;if(r){if(this._render_target=this._render_target||new Z(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,s=r.resolved_scene||i.scene().threejsScene(),o=r.renderer(n);this._modify_scene_and_renderer(s,o),o.setRenderTarget(this._render_target),o.clear(),o.render(s,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(s,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==Ki.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const yL=1e3/60;var xL;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(xL||(xL={}));const bL=[xL.CPU,xL.GPU];function wL(t={}){return t.mode=bL.indexOf(xL.CPU),{visibleIf:t}}function TL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.GEOMETRY),{visibleIf:t}}function AL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.PLANE),{visibleIf:t}}function EL(t={}){return t.mode=bL.indexOf(xL.GPU),{visibleIf:t}}var ML;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(ML||(ML={}));const SL=[ML.SCENE_GRAPH,ML.NODE];const CL=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(bL.indexOf(xL.CPU),{menu:{entries:bL.map(((t,e)=>({name:t,value:e})))}}),this.mouse=oa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=oa.BOOLEAN(0),this.overrideRay=oa.BOOLEAN(0,{visibleIf:{mode:bL.indexOf(xL.CPU),overrideCamera:1}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=oa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=oa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=oa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{vL.PARAM_CALLBACK_update_material(t)},...EL()}),this.pixelValue=oa.VECTOR4([0,0,0,0],{cook:!1,...EL()}),this.hitThreshold=oa.FLOAT(.5,{cook:!1,...EL()}),this.intersectWith=oa.INTEGER(mL.indexOf(_L.GEOMETRY),{menu:{entries:mL.map(((t,e)=>({name:t,value:e})))},...wL()}),this.pointsThreshold=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...wL()}),this.planeDirection=oa.VECTOR3([0,1,0],{...AL()}),this.planeOffset=oa.FLOAT(0,{...AL()}),this.targetType=oa.INTEGER(0,{menu:{entries:SL.map(((t,e)=>({name:t,value:e})))},...TL()}),this.targetNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.NODE)})}),this.objectMask=oa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=oa.BUTTON(null,{callback:(t,e)=>{gL.PARAM_CALLBACK_print_resolve(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.traverseChildren=oa.BOOLEAN(!0,{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL(),separatorAfter:!0}),this.tpositionTarget=oa.BOOLEAN(0,{cook:!1,...wL()}),this.position=oa.VECTOR3([0,0,0],{cook:!1,...wL({tpositionTarget:0})}),this.positionTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tpositionTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.tvelocity=oa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=oa.BOOLEAN(0,{cook:!1,...wL({tvelocity:1})}),this.velocity=oa.VECTOR3([0,0,0],{cook:!1,...wL({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tvelocity:1,tvelocityTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.geoAttribute=oa.BOOLEAN(0,TL()),this.geoAttributeName=oa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...TL({geoAttribute:1})}),this.geoAttributeType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},...TL({geoAttribute:1})}),this.geoAttributeValue1=oa.FLOAT(0,{cook:!1,...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.NUMERIC)})}),this.geoAttributeValues=oa.STRING(\\\\\\\"\\\\\\\",{...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.STRING)})})}};class NL extends Ba{constructor(){super(...arguments),this.paramsConfig=CL,this.cpuController=new gL(this),this.gpuController=new vL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(NL.INPUT_TRIGGER,$o.BASE,this._process_trigger_event_throttled.bind(this)),new Jo(NL.INPUT_MOUSE,$o.MOUSE,this._process_mouse_event.bind(this)),new Jo(NL.INPUT_UPDATE_OBJECTS,$o.BASE,this._process_trigger_update_objects.bind(this)),new Jo(NL.INPUT_TRIGGER_VEL_RESET,$o.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(NL.OUTPUT_HIT,$o.BASE),new Jo(NL.OUTPUT_MISS,$o.BASE)])}trigger_hit(t){this.dispatchEventToOutput(NL.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(NL.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=ai.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),yL-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.velocity_controller.reset()}}var LL;NL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",NL.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",NL.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",NL.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",NL.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",NL.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(LL||(LL={}));const OL=[LL.SET,LL.TOGGLE];const RL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=oa.BOOLEAN(0),this.displayMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tdisplay:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.display=oa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:OL.indexOf(LL.SET)},separatorAfter:!0}),this.tbypass=oa.BOOLEAN(0),this.bypassMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tbypass:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.bypass=oa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:OL.indexOf(LL.SET)}}),this.execute=oa.BUTTON(null,{callback:t=>{PL.PARAM_CALLBACK_execute(t)}})}};class PL extends Ba{constructor(){super(...arguments),this.paramsConfig=RL}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=OL[this.pv.displayMode];switch(i){case LL.SET:return void n.set(this.pv.display);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=OL[this.pv.bypassMode];switch(i){case LL.SET:return void n.set(this.pv.bypass);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var IL;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(IL||(IL={}));const FL=[IL.BOOLEAN,IL.BUTTON,IL.NUMBER,IL.VECTOR2,IL.VECTOR3,IL.VECTOR4,IL.STRING],DL=FL.indexOf(IL.BOOLEAN),kL=FL.indexOf(IL.NUMBER),BL=FL.indexOf(IL.VECTOR2),zL=FL.indexOf(IL.VECTOR3),UL=FL.indexOf(IL.VECTOR4),GL=FL.indexOf(IL.STRING),VL=\\\\\\\"output\\\\\\\";const HL=new class extends aa{constructor(){super(...arguments),this.param=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=oa.INTEGER(kL,{menu:{entries:FL.map(((t,e)=>({name:t,value:e})))}}),this.toggle=oa.BOOLEAN(0,{visibleIf:{type:DL}}),this.boolean=oa.BOOLEAN(0,{visibleIf:{type:DL,toggle:0}}),this.number=oa.FLOAT(0,{visibleIf:{type:kL}}),this.vector2=oa.VECTOR2([0,0],{visibleIf:{type:BL}}),this.vector3=oa.VECTOR3([0,0,0],{visibleIf:{type:zL}}),this.vector4=oa.VECTOR4([0,0,0,0],{visibleIf:{type:UL}}),this.increment=oa.BOOLEAN(0,{visibleIf:[{type:kL},{type:BL},{type:zL},{type:UL}]}),this.string=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:GL}}),this.execute=oa.BUTTON(null,{callback:t=>{jL.PARAM_CALLBACK_execute(t)}})}};class jL extends Ba{constructor(){super(...arguments),this.paramsConfig=HL,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(VL,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(VL,t)}async _new_param_value(t){const e=FL[this.pv.type];switch(e){case IL.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case IL.BUTTON:return t.options.executeCallback();case IL.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Es.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case IL.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Es.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case IL.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Es.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case IL.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Es.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case IL.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ar.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const WL=new class extends aa{constructor(){super(...arguments),this.outputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class qL extends Ba{constructor(){super(...arguments),this.paramsConfig=WL}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[$o.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill($o.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n{this._timer_active&&(this.pv.count<=0||this._current_count{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class QL extends ia{static context(){return Ki.EVENT}cook(){this.cookController.endCook()}}class KL extends QL{}class tO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class eO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class nO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rO extends QL{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const oO=\\\\\\\"int\\\\\\\";const aO=new class extends aa{constructor(){super(...arguments),this.float=oa.FLOAT(0)}};class lO extends df{constructor(){super(...arguments),this.paramsConfig=aO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(oO)} = int(${uf.float(e)})`;t.addBodyLines(this,[n])}}const cO=\\\\\\\"float\\\\\\\";const uO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class hO extends df{constructor(){super(...arguments),this.paramsConfig=uO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(cO,Do.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(cO)} = float(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const dO=\\\\\\\"bool\\\\\\\";const pO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class _O extends df{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(dO,Do.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(dO)} = bool(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const mO=new class extends aa{constructor(){super(...arguments),this.bool=oa.BOOLEAN(0)}};class fO extends df{constructor(){super(...arguments),this.paramsConfig=mO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(oO)} = int(${uf.bool(e)})`;t.addBodyLines(this,[n])}}const gO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0)}};class vO extends df{constructor(){super(...arguments),this.paramsConfig=gO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(vO.OUTPUT_NAME,Do.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(vO.OUTPUT_NAME)} = ${uf.float2(e,n)}`;t.addBodyLines(this,[i])}}vO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const yO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0)}};class xO extends df{constructor(){super(...arguments),this.paramsConfig=yO}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(xO.OUTPUT_NAME,Do.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=`vec3 ${this.glVarName(xO.OUTPUT_NAME)} = ${uf.float3(e,n,i)}`;t.addBodyLines(this,[r])}}xO.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const bO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0),this.w=oa.FLOAT(0)}};class wO extends df{constructor(){super(...arguments),this.paramsConfig=bO}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(wO.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=this.variableForInputParam(this.p.w),s=`vec4 ${this.glVarName(wO.OUTPUT_NAME)} = ${uf.float4(e,n,i,r)}`;t.addBodyLines(this,[s])}}wO.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const TO=new class extends aa{};class AO extends df{constructor(){super(...arguments),this.paramsConfig=TO}}function EO(t,e){const n=e.components,i=e.param_type;return class extends AO{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Vo(t,Do.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const MO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],SO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],CO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class NO extends(EO(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Es.VECTOR2})){}class LO extends(EO(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Es.VECTOR3})){}class OO extends(EO(\\\\\\\"vec4ToFloat\\\\\\\",{components:CO,param_type:Es.VECTOR4})){}class RO extends AO{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(RO.OUTPUT_NAME_VEC3,Do.VEC3),new Vo(RO.OUTPUT_NAME_W,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR4,RO.INPUT_NAME_VEC4,CO.map((t=>0)))}setLines(t){const e=[],n=RO.INPUT_NAME_VEC4,i=RO.OUTPUT_NAME_VEC3,r=RO.OUTPUT_NAME_W,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${s}.xyz`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.w`)}t.addBodyLines(this,e)}}RO.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",RO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",RO.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class PO extends AO{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(PO.OUTPUT_NAME_VEC2,Do.VEC2),new Vo(PO.OUTPUT_NAME_Z,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR3,PO.INPUT_NAME_VEC3,SO.map((t=>0)))}setLines(t){const e=[],n=PO.INPUT_NAME_VEC3,i=PO.OUTPUT_NAME_VEC2,r=PO.OUTPUT_NAME_Z,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${s}.xy`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.z`)}t.addBodyLines(this,e)}}PO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",PO.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",PO.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class IO extends AO{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(IO.OUTPUT_NAME_VEC3,Do.VEC3)])}createParams(){this.addParam(Es.VECTOR2,IO.INPUT_NAME_VEC2,MO.map((t=>0))),this.addParam(Es.FLOAT,IO.INPUT_NAME_Z,0)}setLines(t){const e=[],n=IO.INPUT_NAME_VEC2,i=IO.INPUT_NAME_Z,r=IO.OUTPUT_NAME_VEC3,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec3 ${a} = vec3(${s}.xy, ${o})`),t.addBodyLines(this,e)}}IO.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",IO.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",IO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class FO extends AO{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(FO.OUTPUT_NAME_VEC4,Do.VEC4)])}createParams(){this.addParam(Es.VECTOR3,FO.INPUT_NAME_VEC3,SO.map((t=>0))),this.addParam(Es.FLOAT,FO.INPUT_NAME_W,0)}setLines(t){const e=[],n=FO.INPUT_NAME_VEC3,i=FO.INPUT_NAME_W,r=FO.OUTPUT_NAME_VEC4,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec4 ${a} = vec4(${s}.xyz, ${o})`),t.addBodyLines(this,e)}}FO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",FO.INPUT_NAME_W=\\\\\\\"w\\\\\\\",FO.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const DO=new class extends aa{};class kO extends df{constructor(){super(...arguments),this.paramsConfig=DO}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class BO extends kO{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Do.FLOAT]}}class zO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}}class UO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t]}}class GO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t]}}class VO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t,t]}}function HO(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||\\\\\\\"in\\\\\\\";return class extends BO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return r}_gl_output_name(t){return i}gl_method_name(){return n}}}class jO extends(HO(\\\\\\\"abs\\\\\\\")){}class WO extends(HO(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class qO extends(HO(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class XO extends(HO(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class YO extends(HO(\\\\\\\"ceil\\\\\\\")){}class $O extends(HO(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class JO extends(HO(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class ZO extends(HO(\\\\\\\"exp\\\\\\\")){}class QO extends(HO(\\\\\\\"exp2\\\\\\\")){}class KO extends(HO(\\\\\\\"floor\\\\\\\")){}class tR extends(HO(\\\\\\\"fract\\\\\\\")){}class eR extends(HO(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class nR extends(HO(\\\\\\\"log\\\\\\\")){}class iR extends(HO(\\\\\\\"log2\\\\\\\")){}class rR extends(HO(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class sR extends(HO(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class oR extends(HO(\\\\\\\"sign\\\\\\\")){}class aR extends(HO(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class lR extends(HO(\\\\\\\"sqrt\\\\\\\")){}class cR extends(HO(\\\\\\\"tan\\\\\\\")){}function uR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],s=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Tf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():s}const e=t||s||Do.FLOAT;return[e,e]}}}class hR extends(uR(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class dR extends(uR(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class pR extends(uR(\\\\\\\"max\\\\\\\")){}class _R extends(uR(\\\\\\\"min\\\\\\\")){}class mR extends(uR(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Do.FLOAT;return[t,t]}}class fR extends(uR(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class gR extends(uR(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Do.VEC3})){}class vR extends(uR(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function yR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],s=e.default||{},o=e.out_type||Do.FLOAT,a=e.functions||[];return class extends UO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return s[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class xR extends(yR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class bR extends(yR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class wR extends(yR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function TR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.operation,s=e.allowed_in_types;return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return uf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return r}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&s&&!s.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Do.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,r=[];for(let t=0;t{const n=t.name();return uf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function NR(t,e){return class extends CR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class LR extends(NR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class OR extends(NR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var RR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(RR||(RR={}));var PR,IR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(PR||(PR={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(IR||(IR={}));const FR=[PR.POSITION,PR.VELOCITY,PR.MASS,PR.FORCE],DR=[IR.POSITION,IR.VELOCITY],kR={[PR.POSITION]:[0,0,0],[PR.VELOCITY]:[0,0,0],[PR.MASS]:1,[PR.FORCE]:[0,-9.8,0]};const BR=new class extends aa{};class zR extends df{constructor(){super(...arguments),this.paramsConfig=BR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(IR.POSITION,Do.VEC3),new Vo(IR.VELOCITY,Do.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return FR[t]}_gl_output_name(t){return DR[t]}paramDefaultValue(t){return kR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Do.FLOAT,RR.DELTA_TIME),i=new Tf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const r=uf.any(this.variableForInput(PR.POSITION)),s=uf.any(this.variableForInput(PR.VELOCITY)),o=uf.float(this.variableForInput(PR.MASS)),a=uf.any(this.variableForInput(PR.FORCE)),l=this.glVarName(IR.POSITION),c=this.glVarName(IR.VELOCITY),u=`${e} ${c} = compute_velocity_from_acceleration(${[s,a,o,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,h=`${e} ${l} = compute_position_from_velocity(${[r,c,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[u,h])}}var UR,GR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n// let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n// let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n// out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n// out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n// out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n// out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n// return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s, oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s, oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s, 0.0, oc * axis.z * axis.x - axis.y * s, oc * axis.y * axis.z + axis.x * s, oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(UR||(UR={}));const VR=[UR.DIR,UR.UP],HR={[UR.DIR]:[0,0,1],[UR.UP]:[0,1,0]};class jR extends zO{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>VR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return HR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var WR;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(WR||(WR={}));const qR=[WR.LINEAR,WR.GAMMA,WR.SRGB,WR.RGBE,WR.RGBM,WR.RGBD,WR.LogLuv];const XR=new class extends aa{constructor(){super(...arguments),this.color=oa.VECTOR4([1,1,1,1]),this.from=oa.INTEGER(qR.indexOf(WR.LINEAR),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.to=oa.INTEGER(qR.indexOf(WR.GAMMA),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=oa.FLOAT(2.2)}};class YR extends df{constructor(){super(...arguments),this.paramsConfig=XR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(YR.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=qR[this.pv.from],n=qR[this.pv.to],i=this.glVarName(YR.OUTPUT_NAME),r=uf.any(this.variableForInput(YR.INPUT_NAME)),s=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(r),e==WR.GAMMA||n==WR.GAMMA){const t=uf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}s.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else s.push(`vec4 ${i} = ${r}`);t.addBodyLines(this,s)}}var $R,JR;YR.INPUT_NAME=\\\\\\\"color\\\\\\\",YR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",YR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}($R||($R={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(JR||(JR={}));const ZR=[$R.EQUAL,$R.LESS_THAN,$R.GREATER_THAN,$R.LESS_THAN_OR_EQUAL,$R.GREATER_THAN_OR_EQUAL,$R.NOT_EQUAL],QR=[JR.EQUAL,JR.LESS_THAN,JR.GREATER_THAN,JR.LESS_THAN_OR_EQUAL,JR.GREATER_THAN_OR_EQUAL,JR.NOT_EQUAL],KR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const tP=new class extends aa{constructor(){super(...arguments),this.test=oa.INTEGER(0,{menu:{entries:ZR.map(((t,e)=>({name:`${QR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Do.BOOL]))}set_test_name(t){this.p.test.set(ZR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=QR[this.pv.test],r=uf.any(this.variableForInput(this._gl_input_name(0))),s=uf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Go[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=oa.BOOLEAN(0,iP(Do.BOOL)),this.int=oa.INTEGER(0,iP(Do.INT)),this.float=oa.FLOAT(0,iP(Do.FLOAT)),this.vec2=oa.VECTOR2([0,0],iP(Do.VEC2)),this.vec3=oa.VECTOR3([0,0,0],iP(Do.VEC3)),this.vec4=oa.VECTOR4([0,0,0,0],iP(Do.VEC4))}};class sP extends df{constructor(){super(...arguments),this.paramsConfig=rP,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>sP.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=uf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=uf.integer(e.value));const r=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[r])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=ko[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Do.BOOL,this.p.bool],[Do.INT,this.p.int],[Do.FLOAT,this.p.float],[Do.VEC2,this.p.vec2],[Do.VEC3,this.p.vec3],[Do.VEC4,this.p.vec4]]);const t=ko[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(sP.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(ko.indexOf(t))}}sP.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const oP=\\\\\\\"cross\\\\\\\";const aP=new class extends aa{constructor(){super(...arguments),this.x=oa.VECTOR3([0,0,1]),this.y=oa.VECTOR3([0,1,0])}};class lP extends df{constructor(){super(...arguments),this.paramsConfig=aP}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(oP,Do.VEC3)])}setLines(t){const e=uf.float(this.variableForInputParam(this.p.x)),n=uf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(oP)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class cP extends(yR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var uP=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const hP=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR2([0,0]),this.center=oa.VECTOR2([0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class dP extends df{constructor(){super(...arguments),this.paramsConfig=hP}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}var pP=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n const float a = 4.0 / 11.0;\\\\n const float b = 8.0 / 11.0;\\\\n const float c = 9.0 / 10.0;\\\\n\\\\n const float ca = 4356.0 / 361.0;\\\\n const float cb = 35442.0 / 1805.0;\\\\n const float cc = 16061.0 / 1805.0;\\\\n\\\\n float t2 = t * t;\\\\n\\\\n return t < a\\\\n ? 7.5625 * t2\\\\n : t < b\\\\n ? 9.075 * t2 - 9.9 * t + 3.4\\\\n : t < c\\\\n ? ca * t2 - cb * t + cc\\\\n : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const _P=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],mP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n return t == 0.0 || t == 1.0\\\\n ? t\\\\n : t < 0.5\\\\n ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n float f = t < 0.5\\\\n ? 2.0 * t\\\\n : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n return t < 0.5\\\\n ? 0.5 * g\\\\n : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n float f = 1.0 - t;\\\\n return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n return t < 0.5\\\\n ? +8.0 * pow(t, 4.0)\\\\n : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n return t < 0.5\\\\n ? 4.0 * t * t * t\\\\n : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":pP,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n float p = 2.0 * t * t;\\\\n return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n return t < 0.5\\\\n ? +16.0 * pow(t, 5.0)\\\\n : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n float f = t - 1.0;\\\\n return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},fP={\\\\\\\"bounce-in\\\\\\\":[pP],\\\\\\\"bounce-in-out\\\\\\\":[pP]},gP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},vP=_P.indexOf(\\\\\\\"sine-in-out\\\\\\\");const yP=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(vP,{menu:{entries:_P.map(((t,e)=>({name:t,value:e})))}}),this.input=oa.FLOAT(0)}};class xP extends df{constructor(){super(...arguments),this.paramsConfig=yP}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.FLOAT)])}setLines(t){const e=_P[this.pv.type],n=gP[e];let i=[new Tf(this,mP[e])];const r=(fP[e]||[]).map((t=>new Tf(this,t)));r&&(i=r.concat(i));const s=uf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${s})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var bP=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const wP={srcMin:0,srcMax:1,destMin:0,destMax:1};class TP extends VO{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return wP[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const AP={srcMin:0,srcMax:1};class EP extends UO{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return AP[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const MP={destMin:0,destMax:1};class SP extends UO{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return MP[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const CP={center:.5,variance:.5};class NP extends UO{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return CP[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const LP=\\\\\\\"color\\\\\\\";const OP=new class extends aa{constructor(){super(...arguments),this.mvPosition=oa.VECTOR4([0,0,0,0]),this.baseColor=oa.COLOR([0,0,0]),this.fogColor=oa.COLOR([1,1,1]),this.near=oa.FLOAT(0),this.far=oa.FLOAT(0)}};class RP extends df{constructor(){super(...arguments),this.paramsConfig=OP}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(LP,Do.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Ef(this,Do.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const r=new Tf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),s=uf.vector4(this.variableForInputParam(this.p.mvPosition)),o=uf.vector3(this.variableForInputParam(this.p.baseColor)),a=uf.vector3(this.variableForInputParam(this.p.fogColor)),l=uf.vector3(this.variableForInputParam(this.p.near)),c=uf.vector3(this.variableForInputParam(this.p.far)),u=`vec3 ${this.glVarName(LP)} = compute_fog(${[s,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,r]),t.addBodyLines(this,[u])}}}const PP=new class extends aa{};class IP extends df{constructor(){super(...arguments),this.paramsConfig=PP}static type(){return er.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class FP extends df{constructor(){super(...arguments),this._children_controller_context=Ki.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(IP.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=0;r=0){const e=t.dest_connection_point(),i=uf.any(this.variableForInput(e.name())),r=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(r)}const s=this.pv.start,o=this.pv.max,a=this.pv.step,l=uf.float(s),c=uf.float(o),u=uf.float(a),h=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${h} = ${l}; ${h} < ${c}; ${h}+= ${u}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(BP.START_INDEX)} = ${h}`;if(n.push(p),r)for(let t of r)if(t&&t.input_index>=0){const i=t.dest_connection_point(),r=this.glVarName(i.name()),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}setLines(t){}}const VP=new class extends aa{};class HP extends df{constructor(){super(...arguments),this.paramsConfig=VP}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const jP=new class extends aa{constructor(){super(...arguments),this.hsluv=oa.VECTOR3([1,1,1])}};class WP extends df{constructor(){super(...arguments),this.paramsConfig=jP}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214 ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786 );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4 xyzToRgb(vec4 c) {return vec4( xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToXyz(vec4 c) {return vec4( rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 xyzToLuv(vec4 c) {return vec4( xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToXyz(vec4 c) {return vec4( luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToLch(vec4 c) {return vec4( luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToLuv(vec4 c) {return vec4( lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToRgb(vec4 c) {return vec4( lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToLch(vec4 c) {return vec4( rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToRgb(vec4 c) {return vec4( luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3 xyzToRgb(float x, float y, float z) {return xyzToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToXyz(float x, float y, float z) {return rgbToXyz( vec3(x,y,z) );}\\\\nvec3 xyzToLuv(float x, float y, float z) {return xyzToLuv( vec3(x,y,z) );}\\\\nvec3 luvToXyz(float x, float y, float z) {return luvToXyz( vec3(x,y,z) );}\\\\nvec3 luvToLch(float x, float y, float z) {return luvToLch( vec3(x,y,z) );}\\\\nvec3 lchToLuv(float x, float y, float z) {return lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3 lchToRgb(float x, float y, float z) {return lchToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToLch(float x, float y, float z) {return rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3 luvToRgb(float x, float y, float z) {return luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4 xyzToRgb(float x, float y, float z, float a) {return xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToXyz(float x, float y, float z, float a) {return rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4 xyzToLuv(float x, float y, float z, float a) {return xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4 luvToXyz(float x, float y, float z, float a) {return luvToXyz( vec4(x,y,z,a) );}\\\\nvec4 luvToLch(float x, float y, float z, float a) {return luvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToLuv(float x, float y, float z, float a) {return lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4 lchToRgb(float x, float y, float z, float a) {return lchToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToLch(float x, float y, float z, float a) {return rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4 luvToRgb(float x, float y, float z, float a) {return luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsluv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const qP=new class extends aa{constructor(){super(...arguments),this.hsv=oa.VECTOR3([1,1,1])}};class XP extends df{constructor(){super(...arguments),this.paramsConfig=qP}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const YP=\\\\\\\"condition\\\\\\\";const $P=new class extends aa{};class JP extends kP{constructor(){super(...arguments),this.paramsConfig=$P}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Do.BOOL],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=1;r[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const pI=\\\\\\\"mvMult\\\\\\\";const _I=new class extends aa{constructor(){super(...arguments),this.vector=oa.VECTOR3([0,0,0])}};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(pI,Do.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=uf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(pI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const fI={mult:1};var gI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(gI||(gI={}));class vI extends GO{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[gI.VALUE,gI.PRE_ADD,gI.MULT,gI.POST_ADD][t]}paramDefaultValue(t){return fI[t]}setLines(t){const e=uf.any(this.variableForInput(gI.VALUE)),n=uf.any(this.variableForInput(gI.PRE_ADD)),i=uf.any(this.variableForInput(gI.MULT)),r=uf.any(this.variableForInput(gI.POST_ADD)),s=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${s} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${r}`;t.addBodyLines(this,[a])}}class yI extends BO{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var xI;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(xI||(xI={}));const bI=[xI.CLASSIC_PERLIN_2D,xI.CLASSIC_PERLIN_3D,xI.CLASSIC_PERLIN_4D,xI.NOISE_2D,xI.NOISE_3D,xI.NOISE_4D],wI={[xI.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n vec3 Pi0 = floor(P); // Integer part for indexing\\\\n vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n vec4 Pi0 = floor(P); // Integer part for indexing\\\\n vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n',[xI.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n {\\\\n const vec4 C = vec4(0.211324865405187, // (3.0-sqrt(3.0))/6.0\\\\n 0.366025403784439, // 0.5*(sqrt(3.0)-1.0)\\\\n -0.577350269189626, // -1.0 + 2.0 * C.x\\\\n 0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n vec2 i = floor(v + dot(v, C.yy) );\\\\n vec2 x0 = v - i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n vec2 i1;\\\\n //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n //i1.y = 1.0 - i1.x;\\\\n i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n vec4 x12 = x0.xyxy + C.xxzz;\\\\n x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n i = mod289(i); // Avoid truncation effects in permutation\\\\n vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n m = m*m ;\\\\n m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n vec3 h = abs(x) - 0.5;\\\\n vec3 ox = floor(x + 0.5);\\\\n vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n vec3 g;\\\\n g.x = a0.x * x0.x + h.x * x0.y;\\\\n g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[xI.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n { \\\\n const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n vec3 i = floor(v + dot(v, C.yyy) );\\\\n vec3 x0 = v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n vec3 g = step(x0.yzx, x0.xyz);\\\\n vec3 l = 1.0 - g;\\\\n vec3 i1 = min( g.xyz, l.zxy );\\\\n vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n // x1 = x0 - i1 + 1.0 * C.xxx;\\\\n // x2 = x0 - i2 + 2.0 * C.xxx;\\\\n // x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n vec3 x1 = x0 - i1 + C.xxx;\\\\n vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n vec3 x3 = x0 - D.yyy; // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n vec4 p = permute( permute( permute( \\\\n i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n float n_ = 0.142857142857; // 1.0/7.0\\\\n vec3 ns = n_ * D.wyz - D.xzx;\\\\n\\\\n vec4 j = p - 49.0 * floor(p * ns.z * ns.z); // mod(p,7*7)\\\\n\\\\n vec4 x_ = floor(j * ns.z);\\\\n vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\\\\n\\\\n vec4 x = x_ *ns.x + ns.yyyy;\\\\n vec4 y = y_ *ns.x + ns.yyyy;\\\\n vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n vec4 b0 = vec4( x.xy, y.xy );\\\\n vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n vec3 p0 = vec3(a0.xy,h.x);\\\\n vec3 p1 = vec3(a0.zw,h.y);\\\\n vec3 p2 = vec3(a1.xy,h.z);\\\\n vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n m = m * m;\\\\n return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n dot(p2,x2), dot(p3,x3) ) );\\\\n }\\\\n\\\\\\\",[xI.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n {\\\\n const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n vec4 p,s;\\\\n\\\\n p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n s = vec4(lessThan(p, vec4(0.0)));\\\\n p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n return p;\\\\n }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n {\\\\n const vec4 C = vec4( 0.138196601125011, // (5 - sqrt(5))/20 G4\\\\n 0.276393202250021, // 2 * G4\\\\n 0.414589803375032, // 3 * G4\\\\n -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n vec4 i = floor(v + dot(v, vec4(F4)) );\\\\n vec4 x0 = v - i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n vec4 i0;\\\\n vec3 isX = step( x0.yzw, x0.xxx );\\\\n vec3 isYZ = step( x0.zww, x0.yyz );\\\\n// i0.x = dot( isX, vec3( 1.0 ) );\\\\n i0.x = isX.x + isX.y + isX.z;\\\\n i0.yzw = 1.0 - isX;\\\\n// i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n i0.y += isYZ.x + isYZ.y;\\\\n i0.zw += 1.0 - isYZ.xy;\\\\n i0.z += isYZ.z;\\\\n i0.w += 1.0 - isYZ.z;\\\\n\\\\n // i0 now contains the unique values 0,1,2,3 in each channel\\\\n vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n // x1 = x0 - i1 + 1.0 * C.xxxx\\\\n // x2 = x0 - i2 + 2.0 * C.xxxx\\\\n // x3 = x0 - i3 + 3.0 * C.xxxx\\\\n // x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n vec4 x1 = x0 - i1 + C.xxxx;\\\\n vec4 x2 = x0 - i2 + C.yyyy;\\\\n vec4 x3 = x0 - i3 + C.zzzz;\\\\n vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n vec4 j1 = permute( permute( permute( permute (\\\\n i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n vec4 p0 = grad4(j0, ip);\\\\n vec4 p1 = grad4(j1.x, ip);\\\\n vec4 p2 = grad4(j1.y, ip);\\\\n vec4 p3 = grad4(j1.z, ip);\\\\n vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4) ), 0.0);\\\\n m0 = m0 * m0;\\\\n m1 = m1 * m1;\\\\n return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n }\\\\n\\\\\\\"},TI={[xI.CLASSIC_PERLIN_2D]:Do.VEC2,[xI.CLASSIC_PERLIN_3D]:Do.VEC3,[xI.CLASSIC_PERLIN_4D]:Do.VEC4,[xI.NOISE_2D]:Do.VEC2,[xI.NOISE_3D]:Do.VEC3,[xI.NOISE_4D]:Do.VEC4},AI={[xI.CLASSIC_PERLIN_2D]:Do.FLOAT,[xI.CLASSIC_PERLIN_3D]:Do.FLOAT,[xI.CLASSIC_PERLIN_4D]:Do.FLOAT,[xI.NOISE_2D]:Do.FLOAT,[xI.NOISE_3D]:Do.FLOAT,[xI.NOISE_4D]:Do.FLOAT},EI={[xI.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[xI.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var MI;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(MI||(MI={}));const SI=[MI.NoChange,MI.Float,MI.Vec2,MI.Vec3,MI.Vec4],CI={[MI.NoChange]:\\\\\\\"Same as noise\\\\\\\",[MI.Float]:\\\\\\\"Float\\\\\\\",[MI.Vec2]:\\\\\\\"Vec2\\\\\\\",[MI.Vec3]:\\\\\\\"Vec3\\\\\\\",[MI.Vec4]:\\\\\\\"Vec4\\\\\\\"},NI={[MI.NoChange]:Do.FLOAT,[MI.Float]:Do.FLOAT,[MI.Vec2]:Do.VEC2,[MI.Vec3]:Do.VEC3,[MI.Vec4]:Do.VEC4},LI=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],OI=\\\\\\\"noise\\\\\\\",RI=bI.indexOf(xI.NOISE_3D),PI=MI.NoChange,II={amp:1,freq:1};var FI;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(FI||(FI={}));const DI=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(RI,{menu:{entries:bI.map(((t,e)=>({name:`${t} (output: ${AI[t]})`,value:e})))}}),this.outputType=oa.INTEGER(PI,{menu:{entries:SI.map((t=>{const e=SI[t];return{name:CI[e],value:e}}))}}),this.octaves=oa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class kI extends df{constructor(){super(...arguments),this.paramsConfig=DI}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(OI,Do.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>OI))}_gl_input_name(t){return[FI.AMP,FI.POSITION,FI.FREQ,FI.OFFSET][t]}paramDefaultValue(t){return II[t]}_expected_input_types(){const t=bI[this.pv.type],e=this._expected_output_types()[0],n=TI[t];return[e,n,n,n]}_expected_output_types(){const t=bI[this.pv.type],e=SI[this.pv.outputType];return e==MI.NoChange?[TI[t]]:[NI[e]]}setLines(t){const e=[],n=[],i=bI[this.pv.type],r=wI[i],s=AI[i];e.push(new Tf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Tf(this,r)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==s){const t=this.single_noise_line();n.push(t)}else{const t=Go[o],e=[],r=this.glVarName(\\\\\\\"noise\\\\\\\");for(let s=0;suf.float(1e3*s))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const s=`vec${t} ${r} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(s)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=bI[this.pv.type];return`fbm_${EI[t]}_${this.name()}`}fbm_function(){const t=bI[this.pv.type],e=EI[t],n=TI[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${uf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${uf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${uf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),r=uf.any(this.variableForInput(FI.AMP)),s=uf.any(this.variableForInput(FI.POSITION)),o=uf.any(this.variableForInput(FI.FREQ));let a=uf.any(this.variableForInput(FI.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${s}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(OI),u=`${r}*${i}(${l})`;if(e)return`float ${c}${t} = (${u}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${u}`}}class BI extends BO{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const zI=new class extends aa{};class UI extends df{constructor(){super(...arguments),this.paramsConfig=zI}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class GI{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const VI=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(ko.indexOf(Do.FLOAT),{menu:{entries:ko.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:ko.indexOf(Do.VEC3)}})}};class HI extends df{constructor(){super(...arguments),this.paramsConfig=VI,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[ko[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=ko[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=ko[this.pv.type],e=Uo[t];let n=Bo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=ko.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class jI extends kO{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const WI=\\\\\\\"SSSModel\\\\\\\";const qI=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.thickness=oa.FLOAT(.1),this.power=oa.FLOAT(2),this.scale=oa.FLOAT(16),this.distortion=oa.FLOAT(.1),this.ambient=oa.FLOAT(.4),this.attenuation=oa.FLOAT(.8)}};class XI extends df{constructor(){super(...arguments),this.paramsConfig=qI}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(WI,Do.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(WI);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${uf.vector3(this.variableForInputParam(e))};`}}class YI extends BO{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4,Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var $I;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}($I||($I={}));const JI=[$I.AXIS,$I.ANGLE],ZI={[$I.AXIS]:[0,0,1],[$I.ANGLE]:0};class QI extends zO{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>JI[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return ZI[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class KI extends BO{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class tF extends BO{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}const eF=\\\\\\\"val\\\\\\\";const nF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=oa.FLOAT(0)}};class iF extends df{constructor(){super(...arguments),this.paramsConfig=nF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Vo(eF,Do.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Do.FLOAT,n=this._uniform_name(),i=this.glVarName(eF),r=new Af(this,Do.SAMPLER_2D,n);t.addDefinitions(this,[r]);const s=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${s}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.RAMP,this.pv.name,xo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(eF)}}const rF=\\\\\\\"rand\\\\\\\";const sF=new class extends aa{constructor(){super(...arguments),this.seed=oa.VECTOR2([1,1])}};class oF extends df{constructor(){super(...arguments),this.paramsConfig=sF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(rF,Do.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=uf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(rF)} = rand(${n})`;t.addBodyLines(this,[i])}}const aF=new class extends aa{constructor(){super(...arguments),this.rgb=oa.VECTOR3([1,1,1])}};class lF extends df{constructor(){super(...arguments),this.paramsConfig=aF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"hsv\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.rgb)),r=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${r} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var cF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(cF||(cF={}));const uF=[cF.AXIS,cF.QUAT],hF={[cF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[cF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},dF={[cF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[cF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},pF={[cF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[cF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},_F={[cF.AXIS]:[Do.VEC3,Do.VEC3,Do.FLOAT],[cF.QUAT]:[Do.VEC3,Do.VEC4]},mF={vector:[0,0,1],axis:[0,1,0]};const fF=new class extends aa{constructor(){super(...arguments),this.signature=oa.INTEGER(cF.AXIS,{menu:{entries:uF.map(((t,e)=>({name:hF[t],value:e})))}})}};class gF extends df{constructor(){super(...arguments),this.paramsConfig=fF}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=uF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=uF[this.pv.signature];return dF[e][t]}paramDefaultValue(t){return mF[t]}gl_method_name(){const t=uF[this.pv.signature];return pF[t]}_expected_input_types(){const t=uF[this.pv.signature];return _F[t]}_expected_output_types(){return[Do.VEC3]}gl_function_definitions(){return[new Tf(this,GR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const vF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class yF extends BO{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=uf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],r=this.glVarName(i.name()),s=[];if(1==Go[i.type()])s.push(`${i.type()} ${r} = ${this._simple_line(n)}`);else{const t=vF.map((t=>this._simple_line(`${n}.${t}`)));s.push(`${i.type()} ${r} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,s)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const xF=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR3([0,0,0]),this.center=oa.VECTOR3([0,0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}const wF=new class extends aa{};class TF extends df{constructor(){super(...arguments),this.paramsConfig=wF}static type(){return er.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const AF=new class extends aa{};class EF extends df{constructor(){super(...arguments),this.paramsConfig=AF}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?EF.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Do.FLOAT,e=this.io.connections.inputConnections(),n=e?rs.clamp(e.length,2,16):2,i=[Do.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(SF.OUTPUT_NAME),i=this._uniform_name(),r=new Af(this,Do.SAMPLER_2D,i),s=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[r]),t.addBodyLines(this,[s])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var CF;SF.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(CF||(CF={}));const NF=[CF.POSITION,CF.DIR_VEC];const LF=new class extends aa{constructor(){super(...arguments),this.vec=oa.VECTOR3([0,0,0]),this.interpretation=oa.INTEGER(0,{menu:{entries:NF.map(((t,e)=>({name:t,value:e})))}})}};class OF extends df{constructor(){super(...arguments),this.paramsConfig=LF}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=uf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(NF[this.pv.interpretation]){case CF.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case CF.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var RF;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(RF||(RF={}));const PF=[RF.CONDITION,RF.IF_TRUE,RF.IF_FALSE];class IF extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return PF[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Do.FLOAT;return[Do.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=uf.bool(this.variableForInput(RF.CONDITION)),r=uf.any(this.variableForInput(RF.IF_TRUE)),s=uf.any(this.variableForInput(RF.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${r}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${s}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const FF=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const DF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:FF.map(((t,e)=>({name:t,value:e})))}})}};class kF extends df{constructor(){super(...arguments),this.paramsConfig=DF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[FF[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return kF.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Ef(this,this.gl_type(),e),i=this.glVarName(kF.OUTPUT_NAME),r=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[r])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(FF.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}kF.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const BF={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class zF extends(yR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t,t]}_expected_output_types(){return[Do.VEC4]}paramDefaultValue(t){return BF[t]}}const UF={start:[0,0,1],end:[1,0,0]};class GF extends(uR(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t]}_expected_output_types(){return[Do.FLOAT]}paramDefaultValue(t){return UF[t]}}const VF={only:[`${JP.context()}/${JP.type()}`,`${kP.context()}/${kP.type()}`,`${GP.context()}/${GP.type()}`]};class HF extends ia{static context(){return Ki.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends aa{};const jF=[Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4];const WF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:jF.map(((t,e)=>({name:t,value:e})))}})}};class qF extends HF{constructor(){super(...arguments),this.paramsConfig=WF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jF[this.pv.type]]))}get input_name(){return qF.INPUT_NAME}get output_name(){return qF.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(jF.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(qF.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(qF.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}qF.INPUT_NAME=\\\\\\\"export\\\\\\\",qF.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const XF=new class extends aa{};class YF extends HF{constructor(){super(...arguments),this.paramsConfig=XF}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const $F=new class extends aa{};class JF extends HF{constructor(){super(...arguments),this.paramsConfig=$F}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class ZF{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var QF;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(QF||(QF={}));class KF{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new ZF}}class tD extends KF{constructor(t,e,n){super(QF.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class eD extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Es.BOOLEAN:case Es.BUTTON:return{value:0};case Es.COLOR:return{value:new D.a(0,0,0)};case Es.FLOAT:case Es.FOLDER:case Es.INTEGER:case Es.OPERATOR_PATH:case Es.NODE_PATH:case Es.PARAM_PATH:return{value:0};case Es.RAMP:case Es.STRING:return{value:null};case Es.VECTOR2:return{value:new d.a(0,0)};case Es.VECTOR3:return{value:new p.a(0,0,0)};case Es.VECTOR4:return{value:new _.a(0,0,0,0)}}ar.unreachable(t)}}const nD=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(jo.indexOf(Ho.FLOAT),{menu:{entries:jo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:jo.indexOf(Ho.VEC3)}})}};class iD extends HF{constructor(){super(...arguments),this.paramsConfig=nD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jo[this.pv.type]]))}setLines(t){const e=[],n=jo[this.pv.type],i=this.uniform_name();e.push(new tD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=jo[this.pv.type],e=Xo[t];let n=Wo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new eD(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new eD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=jo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class rD extends ia{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return Ki.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class sD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const oD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},aD=Object.keys(oD);function lD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=oa.BOOLEAN(0),this.front=oa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=oa.BOOLEAN(0),this.shadowDoubleSided=oa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=oa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=oa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthWrite=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthTest=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.premultipliedAlpha=oa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=oa.INTEGER(w.xb,{menu:{entries:aD.map((t=>({name:t,value:oD[t]})))}}),this.dithering=oa.BOOLEAN(0),this.polygonOffset=oa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}lD(aa);class cD extends sD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let n of t){const t=r[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class uD extends(lD(aa)){constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.lineWidth=oa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const hD=new uD;class dD extends rD{constructor(){super(...arguments),this.paramsConfig=hD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new wr.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function pD(t){return class extends t{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}pD(aa);class _D extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class mD extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]);const s=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});s||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:s||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),r=this._material.uniforms[n],s=Object.keys(this._material.customMaterials);let o;for(let t of s){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(r||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(r&&$f.callback(i,r),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(ai.playerMode())return this._material}}function fD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=oa.BOOLEAN(0,{callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}})}}}fD(aa);class gD extends rD{constructor(){super(...arguments),this._children_controller_context=Ki.GL,this.persisted_config=new mD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(Ki.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function vD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}vD(aa);class yD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function xD(t){return class extends t{constructor(){super(...arguments),this.default=oa.FOLDER(null)}}}function bD(t){return class extends t{constructor(){super(...arguments),this.advanced=oa.FOLDER(null)}}}class wD extends(vD(lD(fD(bD(pD(xD(aa))))))){constructor(){super(...arguments),this.linewidth=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const TD=new wD;class AD extends gD{constructor(){super(...arguments),this.paramsConfig=TD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_LINE}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function ED(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.useVertexColors=oa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}O.a;ED(aa);class MD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function SD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}SD(aa);class CD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function ND(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function LD(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:Ki.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class OD extends sD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const r=t,s=e;await this._update_texture_on_uniforms(r,s,n,i)}if(this._update_options.directParams){const r=t,s=e;await this._update_texture_on_material(r,s,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const r=null!=e[n]&&null!=e[n].value;let s=!1;if(r){e[n].value.uuid!=i.uuid&&(s=!0)}if(!r||s){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let e of t){const t=r[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else ai.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const r=null!=e[n];let s=!1;if(r){e[n].uuid!=i.uuid&&(s=!0)}r&&!s||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,r,s,o){i.isDirty()&&await i.compute();if(i.value){r.isDirty()&&await r.compute();const i=r.value.nodeWithContext(Ki.COP);if(i){const r=(await i.compute()).texture();if(r)return void s(t,e,n,r)}}o(t,e,n)}}function RD(t){return class extends t{constructor(){super(...arguments),this.useMap=oa.BOOLEAN(0,ND(PD)),this.map=oa.NODE_PATH(gi.EMPTY,LD(PD,\\\\\\\"useMap\\\\\\\"))}}}O.a;RD(aa);class PD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function ID(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(FD)}),this.alphaMap=oa.NODE_PATH(gi.EMPTY,LD(FD,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;ID(aa);class FD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function DD(t){return class extends t{constructor(){super(...arguments),this.useAOMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(kD)}),this.aoMap=oa.NODE_PATH(gi.EMPTY,LD(kD,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;DD(aa);class kD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var BD;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(BD||(BD={}));const zD=[BD.MULT,BD.ADD,BD.MIX],UD={[BD.MULT]:w.nb,[BD.ADD]:w.c,[BD.MIX]:w.lb};function GD(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,ND(VD)),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(VD,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=oa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:zD.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}GD(aa);class VD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=UD[zD[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function HD(t){return class extends t{constructor(){super(...arguments),this.useLightMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(jD)}),this.lightMap=oa.NODE_PATH(gi.EMPTY,LD(jD,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=oa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;HD(aa);class jD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var WD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(WD||(WD={}));const qD=[WD.ROUND,WD.BUTT,WD.SQUARE];var XD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(XD||(XD={}));const YD=[XD.ROUND,XD.BEVEL,XD.MITER];function $D(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=oa.INTEGER(0,{menu:{entries:qD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=oa.INTEGER(0,{menu:{entries:YD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;$D(aa);class JD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=qD[n.wireframeLinecap],e.wireframeLinejoin=YD[n.wireframeLinejoin],e.needsUpdate=!0}}function ZD(t){return class extends t{constructor(){super(...arguments),this.textures=oa.FOLDER(null)}}}const QD={directParams:!0};class KD extends(SD($D(lD(bD(HD(GD(DD(ID(RD(ZD(ED(xD(aa))))))))))))){}const tk=new KD;class ek extends rD{constructor(){super(...arguments),this.paramsConfig=tk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,QD),aoMap:new kD(this,QD),envMap:new VD(this,QD),lightMap:new jD(this,QD),map:new PD(this,QD)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new at.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function nk(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0)}}}nk(aa);class ik extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const rk={uniforms:!0};class sk extends(vD(nk(lD(fD(bD(GD(DD(ID(RD(ZD(pD(xD(aa))))))))))))){}const ok=new sk;class ak extends gD{constructor(){super(...arguments),this.paramsConfig=ok,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,rk),aoMap:new kD(this,rk),envMap:new VD(this,rk),map:new PD(this,rk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_BASIC}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function lk(t){return class extends t{constructor(){super(...arguments),this.emissive=oa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=oa.BOOLEAN(0,ND(ck)),this.emissiveMap=oa.NODE_PATH(gi.EMPTY,LD(ck,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=oa.FLOAT(1)}}}O.a;lk(aa);class ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const uk={directParams:!0};class hk extends(SD($D(lD(bD(HD(GD(lk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))){}const dk=new hk;class pk extends rD{constructor(){super(...arguments),this.paramsConfig=dk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,uk),aoMap:new kD(this,uk),emissiveMap:new ck(this,uk),envMap:new VD(this,uk),lightMap:new jD(this,uk),map:new PD(this,uk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new br.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function _k(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=oa.BOOLEAN(0,{callback:t=>{mk.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=oa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=oa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}_k(aa);class mk extends sD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${uf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${uf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=B;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include \\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>mk.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const fk={uniforms:!0};class gk extends(_k(vD(nk(lD(fD(bD(HD(GD(lk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))){}const vk=new gk;class yk extends gD{constructor(){super(...arguments),this.paramsConfig=vk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,fk),aoMap:new kD(this,fk),emissiveMap:new ck(this,fk),envMap:new VD(this,fk),lightMap:new jD(this,fk),map:new PD(this,fk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_LAMBERT}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function xk(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(bk)}),this.bumpMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(bk,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;xk(aa);class bk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var wk;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(wk||(wk={}));const Tk=[wk.TANGENT,wk.OBJECT],Ak={[wk.TANGENT]:w.Uc,[wk.OBJECT]:w.zb};function Ek(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Mk)}),this.normalMap=oa.NODE_PATH(gi.EMPTY,LD(Mk,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=oa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:Tk.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=oa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;Ek(aa);class Mk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=Ak[Tk[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function Sk(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Ck)}),this.displacementMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;Sk(aa);class Ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function Nk(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=oa.BOOLEAN(0,ND(Lk)),this.matcapMap=oa.NODE_PATH(gi.EMPTY,LD(Lk,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;Nk(aa);class Lk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const Ok={directParams:!0};class Rk extends(SD(lD(bD(Ek(Sk(xk(ID(RD(Nk(ZD(ED(xD(aa))))))))))))){}const Pk=new Rk;class Ik extends rD{constructor(){super(...arguments),this.paramsConfig=Pk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Ok),bumpMap:new bk(this,Ok),displacementMap:new Ck(this,Ok),map:new PD(this,Ok),matcap:new Lk(this,Ok),normalMap:new Mk(this,Ok)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),this.setMaterial(this.material)}}function Fk(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=oa.BOOLEAN(0,ND(Dk)),this.specularMap=oa.NODE_PATH(gi.EMPTY,LD(Dk,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;Fk(aa);class Dk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const kk={directParams:!0};class Bk extends(SD($D(lD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){constructor(){super(...arguments),this.flatShading=oa.BOOLEAN(0)}}const zk=new Bk;class Uk extends rD{constructor(){super(...arguments),this.paramsConfig=zk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,kk),aoMap:new kD(this,kk),bumpMap:new bk(this,kk),displacementMap:new Ck(this,kk),emissiveMap:new ck(this,kk),envMap:new VD(this,kk),lightMap:new jD(this,kk),map:new PD(this,kk),normalMap:new Mk(this,kk),specularMap:new Dk(this,kk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Gk={uniforms:!0};class Vk extends(_k(vD(nk(lD(fD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const Hk=new Vk;class jk extends gD{constructor(){super(...arguments),this.paramsConfig=Hk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Gk),aoMap:new kD(this,Gk),bumpMap:new bk(this,Gk),displacementMap:new Ck(this,Gk),emissiveMap:new ck(this,Gk),envMap:new VD(this,Gk),lightMap:new jD(this,Gk),map:new PD(this,Gk),normalMap:new Mk(this,Gk),specularMap:new Dk(this,Gk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHONG}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Wk(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(qk)}),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(qk,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Wk(aa);class qk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function Xk(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.metalnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=oa.FLOAT(1),this.useRoughnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.roughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=oa.FLOAT(.5)}}}O.a;Xk(aa);class Yk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function $k(t){return class extends t{constructor(){super(...arguments),this.clearcoat=oa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatNormalMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=oa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=oa.FLOAT(0),this.useClearCoatRoughnessMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatRoughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=oa.BOOLEAN(0),this.sheen=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=oa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.reflectivity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.transmission=oa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=oa.BOOLEAN(0),this.transmissionMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.thickness=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!1]}),this.useThicknessMap=oa.BOOLEAN(0),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=oa.FLOAT(0),this.attenuationColor=oa.COLOR([1,1,1])}}}$k(aa);class Jk extends OD{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;if(this._update_options.uniforms){const e=this.node.material;e.uniforms.clearcoat.value=t.clearcoat,e.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,e.uniforms.reflectivity.value=t.reflectivity,e.uniforms.transmission.value=t.transmission,e.uniforms.thickness.value=t.thickness,e.uniforms.attenuationDistance.value=t.attenuationDistance,e.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.uniforms.sheen.value=t.sheen,e.uniforms.sheenRoughness.value=t.sheenRoughness,e.uniforms.sheenTint.value=this._sheenColorClone):e.uniforms.sheen.value=0}if(this._update_options.directParams){const e=this.node.material;e.clearcoat=t.clearcoat,e.clearcoatNormalScale.copy(t.clearcoatNormalScale),e.clearcoatRoughness=t.clearcoatRoughness,e.reflectivity=t.reflectivity,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.sheen=t.sheen,e.sheenRoughness=t.sheenRoughness,e.sheenTint=this._sheenColorClone):e.sheen=0,e.transmission=t.transmission,e.thickness=t.thickness,e.attenuationDistance=t.attenuationDistance,e.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Zk={directParams:!0};class Qk extends(SD($D(lD(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))))){}const Kk=new Qk;class tB extends rD{constructor(){super(...arguments),this.paramsConfig=Kk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Zk),aoMap:new kD(this,Zk),bumpMap:new bk(this,Zk),displacementMap:new Ck(this,Zk),emissiveMap:new ck(this,Zk),envMap:new qk(this,Zk),lightMap:new jD(this,Zk),map:new PD(this,Zk),metalnessRoughnessMap:new Yk(this,Zk),normalMap:new Mk(this,Zk),physical:new Jk(this,Zk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const eB={uniforms:!0};class nB extends(function(t){return class extends(_k(vD(nk(lD(fD(t)))))){}}(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa))))))))))))))))){}const iB=new nB;class rB extends gD{constructor(){super(...arguments),this.paramsConfig=iB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,eB),aoMap:new kD(this,eB),bumpMap:new bk(this,eB),displacementMap:new Ck(this,eB),emissiveMap:new ck(this,eB),envMap:new qk(this,eB),lightMap:new jD(this,eB),map:new PD(this,eB),metalnessRoughnessMap:new Yk(this,eB),normalMap:new Mk(this,eB),physical:new Jk(this,eB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHYSICAL}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const sB={directParams:!0};class oB extends(SD($D(lD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){}const aB=new oB;class lB extends rD{constructor(){super(...arguments),this.paramsConfig=aB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,sB),aoMap:new kD(this,sB),bumpMap:new bk(this,sB),displacementMap:new Ck(this,sB),emissiveMap:new ck(this,sB),envMap:new qk(this,sB),lightMap:new jD(this,sB),map:new PD(this,sB),metalnessRoughnessMap:new Yk(this,sB),normalMap:new Mk(this,sB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new xr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const cB={uniforms:!0};class uB extends(_k(vD(nk(lD(fD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const hB=new uB;class dB extends gD{constructor(){super(...arguments),this.paramsConfig=hB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,cB),aoMap:new kD(this,cB),bumpMap:new bk(this,cB),displacementMap:new Ck(this,cB),emissiveMap:new ck(this,cB),envMap:new qk(this,cB),lightMap:new jD(this,cB),map:new PD(this,cB),metalnessRoughnessMap:new Yk(this,cB),normalMap:new Mk(this,cB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_STANDARD}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const pB=z.meshphong_frag.slice(0,z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),_B=z.meshphong_frag.slice(z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),mB={uniforms:I.merge([V.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",z.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",pB,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",_B.replace(\\\\\\\"#include \\\\\\\",(fB=z.lights_fragment_begin,gB=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",vB=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fB.split(gB).join(vB)))].join(\\\\\\\"\\\\n\\\\\\\")};var fB,gB,vB;function yB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function xB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const bB={uniforms:!0};class wB extends(SD(nk(lD(bD(ID(RD(ZD(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=oa.COLOR([1,1,1],{...yB(\\\\\\\"diffuse\\\\\\\")}),this.shininess=oa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{nodeSelection:{context:Ki.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=oa.COLOR([.5,.3,0],{...yB(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=oa.FLOAT(.1,{...xB(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=oa.FLOAT(.4,{...xB(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=oa.FLOAT(.8,{...xB(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=oa.FLOAT(2,{range:[0,10],...xB(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=oa.FLOAT(16,{range:[0,100],...xB(\\\\\\\"thicknessScale\\\\\\\")})}}}(xD(aa)))))))))){}const TB=new wB;class AB extends rD{constructor(){super(...arguments),this.paramsConfig=TB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,bB),map:new PD(this,bB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(mB.uniforms),e=new F({uniforms:t,vertexShader:mB.vertexShader,fragmentShader:mB.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();CD.update(this),ik.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(Ki.COP);n||(this.material.uniforms[e].value=null);const i=n,r=await i.compute();this.material.uniforms[e].value=r.texture()}}function EB(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=oa.BOOLEAN(0,ND(MB)),this.gradientMap=oa.NODE_PATH(gi.EMPTY,LD(MB,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;EB(aa);class MB extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const SB={directParams:!0};class CB extends(SD($D(lD(bD(Ek(HD(EB(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))){}const NB=new CB;class LB extends rD{constructor(){super(...arguments),this.paramsConfig=NB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,SB),aoMap:new kD(this,SB),bumpMap:new bk(this,SB),displacementMap:new Ck(this,SB),emissiveMap:new ck(this,SB),gradientMap:new MB(this,SB),lightMap:new jD(this,SB),map:new PD(this,SB),normalMap:new Mk(this,SB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const OB={directParams:!0};class RB extends(vD(lD(bD(ID(RD(ZD(ED(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1),this.sizeAttenuation=oa.BOOLEAN(1)}}}(xD(aa)))))))))){}const PB=new RB;class IB extends rD{constructor(){super(...arguments),this.paramsConfig=PB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,OB),map:new PD(this,OB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new yr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),yD.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class FB extends(vD(lD(fD(bD(pD(xD(aa))))))){}const DB=new FB;class kB extends gD{constructor(){super(...arguments),this.paramsConfig=DB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return Hn.GL_POINTS}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class BB extends(lD(ED(aa))){}const zB=new BB;class UB extends rD{constructor(){super(...arguments),this.paramsConfig=zB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new Bf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),this.setMaterial(this.material)}}class GB extends k.a{constructor(){const t=GB.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}GB.prototype.isSky=!0,GB.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const VB=new class extends aa{constructor(){super(...arguments),this.turbidity=oa.FLOAT(2,{range:[0,20]}),this.rayleigh=oa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=oa.FLOAT(.005),this.mieDirectional=oa.FLOAT(.8),this.inclination=oa.FLOAT(.5),this.azimuth=oa.FLOAT(.25),this.up=oa.VECTOR3([0,1,0])}};class HB extends rD{constructor(){super(...arguments),this.paramsConfig=VB}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new GB).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var jB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include \\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",WB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const qB={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};var XB=n(16);function YB(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.stepSize=oa.FLOAT(.01),this.density=oa.FLOAT(1),this.shadowDensity=oa.FLOAT(1),this.lightDir=oa.VECTOR3([-1,-1,-1])}}}YB(aa);class $B{constructor(t){this.node=t}static render_hook(t,e,n,i,r,s,o){if(o){this._object_bbox.setFromObject(o);const t=r;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}$B._object_bbox=new XB.a;class JB extends(YB(aa)){}const ZB=new JB;class QB extends rD{constructor(){super(...arguments),this.paramsConfig=ZB,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:jB,fragmentShader:WB,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(qB)});return fs.add_user_data_render_hook(t,$B.render_hook.bind($B)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class KB extends(fD(YB(aa))){}const tz=new KB;class ez extends gD{constructor(){super(...arguments),this.paramsConfig=tz,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return Hn.GL_VOLUME}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class nz extends ia{static context(){return Ki.MAT}cook(){this.cookController.endCook()}}class iz extends nz{}class rz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class az extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lz extends nz{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var uz=n(87);const hz=\\\\\\\"parent object\\\\\\\",dz=[hz,hz,hz,hz];var pz;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(pz||(pz={}));class _z extends ia{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return Ki.OBJ}static displayedInputNames(){return dz}_create_children_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new Q.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class mz extends _z{constructor(){super(...arguments),this.flags=new Fi(this),this.renderOrder=pz.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const fz=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1)}};class gz extends mz{constructor(){super(...arguments),this.paramsConfig=fz}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new uz.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class vz extends nv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}vz.prototype.isRectAreaLight=!0;var yz,xz=n(60);class bz{static init(){const 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Float32Array(t),i=new Float32Array(e);G.LTC_FLOAT_1=new mo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_FLOAT_2=new mo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const r=new Uint16Array(t.length);t.forEach((function(t,e){r[e]=xz.a.toHalfFloat(t)}));const s=new Uint16Array(e.length);e.forEach((function(t,e){s[e]=xz.a.toHalfFloat(t)})),G.LTC_HALF_1=new mo.a(r,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_HALF_2=new mo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1)}}!function(t){t.OBJECTS=\\\\\\\"objects\\\\\\\",t.GEOMETRIES=\\\\\\\"geometries\\\\\\\"}(yz||(yz={}));const wz=[yz.GEOMETRIES,yz.OBJECTS];var Tz;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(Tz||(Tz={}));const Az=[Tz.XYZ,Tz.XZY,Tz.YXZ,Tz.YZX,Tz.ZXY,Tz.ZYX],Ez=Tz.XYZ;class Mz{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new au.a,this._translation_matrix_s=new 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set_params_from_object(t,e){t.position.toArray(this.set_params_from_object_position_array),t.rotation.toArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.multiplyScalar(180/Math.PI),this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_object_position_array),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_object_rotation_array)}))}translation_matrix(t){return this._translation_matrix.compose(t,this._translation_matrix_q,this._translation_matrix_s),this._translation_matrix}matrix(t,e,n,i,r){return this._matrix_euler.set(Object(Ln.e)(e.x),Object(Ln.e)(e.y),Object(Ln.e)(e.z),r),this._matrix_q.setFromEuler(this._matrix_euler),this._matrix_s.copy(n).multiplyScalar(i),this._matrix.compose(t,this._matrix_q,this._matrix_s),this._matrix}rotate_geometry(t,e,n){this._rotate_geometry_vec_dest.copy(n),this._rotate_geometry_vec_dest.normalize(),this._rotate_geometry_q.setFromUnitVectors(e,this._rotate_geometry_vec_dest),this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q),t.applyMatrix4(this._rotate_geometry_m)}static decompose_matrix(t){t.matrix.decompose(t.position,t.quaternion,t.scale)}}function Sz(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=oa.FOLDER(),this.keepPosWhenParenting=oa.BOOLEAN(0),this.rotationOrder=oa.INTEGER(Az.indexOf(Tz.XYZ),{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3([0,0,0]),this.r=oa.VECTOR3([0,0,0]),this.s=oa.VECTOR3([1,1,1]),this.scale=oa.FLOAT(1),this.matrixAutoUpdate=oa.BOOLEAN(n?1:0),this.updateTransformFromObject=oa.BUTTON(null,{callback:t=>{Nz.PARAM_CALLBACK_update_transform_from_object(t)}})}}}Mz.set_params_from_matrix_position=new p.a,Mz.set_params_from_matrix_quaternion=new au.a,Mz.set_params_from_matrix_scale=new p.a,Mz.set_params_from_matrix_euler=new Wv.a,Mz.set_params_from_matrix_rotation=new p.a,Mz.set_params_from_matrix_t=[0,0,0],Mz.set_params_from_matrix_r=[0,0,0],Mz.set_params_from_matrix_s=[0,0,0],Mz.set_params_from_object_position_array=[0,0,0],Mz.set_params_from_object_rotation_deg=new p.a,Mz.set_params_from_object_rotation_array=[0,0,0];Sz(aa);const Cz=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class Nz{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new Mz,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(Cz)||this.node.dirtyController.addPostDirtyHook(Cz,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,Az[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){Mz.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),Mz.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),Mz.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class Lz{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&Nz.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){Lz.on_input_updated(this.node)}}Sz(aa);class Oz extends mz{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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t.matrixAutoUpdate=!1,bz.initialized||(bz.init(),bz.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var Gz=n(72);const Vz=new p.a,Hz=new tf.a;class jz extends Tr.a{constructor(t){const e=new S.a,n=new wr.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],s={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),u=new D.a(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===s[t]&&(s[t]=[]),s[t].push(i.length/3-1)}h(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),h(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),h(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),h(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),h(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),h(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),h(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",u),h(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",u),h(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",u),h(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",u),h(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",u),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(r,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=s,this.update()}update(){const t=this.geometry,e=this.pointMap;Hz.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),Wz(\\\\\\\"c\\\\\\\",e,t,Hz,0,0,-1),Wz(\\\\\\\"t\\\\\\\",e,t,Hz,0,0,1),Wz(\\\\\\\"n1\\\\\\\",e,t,Hz,-1,-1,-1),Wz(\\\\\\\"n2\\\\\\\",e,t,Hz,1,-1,-1),Wz(\\\\\\\"n3\\\\\\\",e,t,Hz,-1,1,-1),Wz(\\\\\\\"n4\\\\\\\",e,t,Hz,1,1,-1),Wz(\\\\\\\"f1\\\\\\\",e,t,Hz,-1,-1,1),Wz(\\\\\\\"f2\\\\\\\",e,t,Hz,1,-1,1),Wz(\\\\\\\"f3\\\\\\\",e,t,Hz,-1,1,1),Wz(\\\\\\\"f4\\\\\\\",e,t,Hz,1,1,1),Wz(\\\\\\\"u1\\\\\\\",e,t,Hz,.7,1.1,-1),Wz(\\\\\\\"u2\\\\\\\",e,t,Hz,-.7,1.1,-1),Wz(\\\\\\\"u3\\\\\\\",e,t,Hz,0,2,-1),Wz(\\\\\\\"cf1\\\\\\\",e,t,Hz,-1,0,1),Wz(\\\\\\\"cf2\\\\\\\",e,t,Hz,1,0,1),Wz(\\\\\\\"cf3\\\\\\\",e,t,Hz,0,-1,1),Wz(\\\\\\\"cf4\\\\\\\",e,t,Hz,0,1,1),Wz(\\\\\\\"cn1\\\\\\\",e,t,Hz,-1,0,-1),Wz(\\\\\\\"cn2\\\\\\\",e,t,Hz,1,0,-1),Wz(\\\\\\\"cn3\\\\\\\",e,t,Hz,0,-1,-1),Wz(\\\\\\\"cn4\\\\\\\",e,t,Hz,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function Wz(t,e,n,i,r,s,o){Vz.set(r,s,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e.9&&o<.1&&(e<.2&&(s[t+0]+=1),n<.2&&(s[t+2]+=1),i<.2&&(s[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(r.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(s,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new Kz(t.vertices,t.indices,t.radius,t.details)}}class tU extends Kz{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new tU(t.radius,t.detail)}}class eU extends Dz{constructor(){super(...arguments),this._geometry=new tU(1),this._quat=new au.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new k.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e0)&&d.push(e,r,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new pU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class _U extends pU{constructor(t=1,e=1,n=8,i=1,r=!1,s=0,o=2*Math.PI){super(0,t,e,n,i,r,s,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:s,thetaLength:o}}static fromJSON(t){return new _U(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class mU{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;dU.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new _U(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new k.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class fU extends(Sz(aa)){constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.angle=oa.FLOAT(45,{range:[0,180]}),this.penumbra=oa.FLOAT(.1),this.decay=oa.FLOAT(.1,{range:[0,1]}),this.distance=oa.FLOAT(100,{range:[0,100]}),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=oa.FOLDER(),this.castShadow=oa.BOOLEAN(1),this.shadowAutoUpdate=oa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=oa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=oa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=oa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=oa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=oa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=oa.FOLDER(),this.tvolumetric=oa.BOOLEAN(0),this.volAttenuation=oa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=oa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const gU=new fU;class vU extends Oz{constructor(){super(...arguments),this.paramsConfig=gU,this._helperController=new Rz(this,dU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new mU(this)}static type(){return Xz.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new hU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let yU;const xU=function(){return void 0===yU&&(yU=new(window.AudioContext||window.webkitAudioContext)),yU},bU=new p.a,wU=new au.a,TU=new p.a,AU=new p.a;class EU extends Q.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=xU(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(bU,wU,TU),AU.set(0,0,-1).applyQuaternion(wU),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(bU.x,t),e.positionY.linearRampToValueAtTime(bU.y,t),e.positionZ.linearRampToValueAtTime(bU.z,t),e.forwardX.linearRampToValueAtTime(AU.x,t),e.forwardY.linearRampToValueAtTime(AU.y,t),e.forwardZ.linearRampToValueAtTime(AU.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(bU.x,bU.y,bU.z),e.setOrientation(AU.x,AU.y,AU.z,n.x,n.y,n.z)}}class MU extends(Sz(aa)){}const SU=new MU;class CU extends _z{constructor(){super(...arguments),this.paramsConfig=SU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new EU;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class NU extends Tr.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new wr.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var LU;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(LU||(LU={}));const OU=[LU.TOGETHER,LU.SEPARATELY];const RU=new class extends aa{constructor(){super(...arguments),this.object0=oa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.object1=oa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.mode=oa.INTEGER(OU.indexOf(LU.TOGETHER),{menu:{entries:OU.map(((t,e)=>({name:t,value:e})))}}),this.blend=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class PU extends _z{constructor(){super(...arguments),this.paramsConfig=RU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._t0=new p.a,this._q0=new au.a,this._s0=new p.a,this._t1=new p.a,this._q1=new au.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(Ki.OBJ),e=this.p.object1.found_node_with_context(Ki.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=OU[this.pv.mode];switch(n){case LU.TOGETHER:return this._blend_together(t,e);case LU.SEPARATELY:return this._blend_separately(t,e)}ar.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var IU={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const FU={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},DU=1/256e3;class kU{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(IU),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(FU),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new st.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new k.a(t)}applyBlur(t,e,n,i){const r=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*r*DU,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*r*DU,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var BU;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(BU||(BU={}));const zU=[BU.ON_RENDER,BU.MANUAL];class UU extends(Sz(aa)){constructor(){super(...arguments),this.shadow=oa.FOLDER(),this.dist=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=oa.VECTOR2([1,1]),this.shadowRes=oa.VECTOR2([256,256]),this.blur=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=oa.BOOLEAN(1),this.blur2=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=oa.FLOAT(1),this.opacity=oa.FLOAT(1),this.showHelper=oa.BOOLEAN(0),this.updateMode=oa.INTEGER(zU.indexOf(BU.ON_RENDER),{callback:t=>{HU.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:zU.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:zU.indexOf(BU.MANUAL)}}),this.scene=oa.FOLDER(),this.include=oa.STRING(\\\\\\\"\\\\\\\"),this.exclude=oa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const GU=new UU,VU=new d.a(256,256);class HU extends _z{constructor(){super(...arguments),this.paramsConfig=GU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._renderTarget=this._createRenderTarget(VU),this._coreRenderBlur=this._createCoreRenderBlur(VU),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new Nz(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new kU(t)}createObject(){const t=new In.a;this._shadowGroup=new In.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new at.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new k.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new st.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new jz(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Mn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,r=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=r,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=zU[this.pv.updateMode];switch(t){case BU.ON_RENDER:return this._addRenderHook();case BU.MANUAL:return this._removeRenderHook()}ar.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,r,s){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=ai.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const jU=\\\\\\\"display\\\\\\\";class WU{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(jU),i=this.node.params.boolean(jU),r=this.node.usedInScene(),s=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return r&&s&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class qU extends(Sz(aa)){constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.renderOrder=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const XU=new qU;class YU extends _z{constructor(){super(...arguments),this.paramsConfig=XU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class $U extends(Sz(aa)){}const JU=new $U;class ZU extends _z{constructor(){super(...arguments),this.paramsConfig=JU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._helper=new NU(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const QU=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR3([0,0,0]),this.longitude=oa.FLOAT(0,{range:[0,360]}),this.latitude=oa.FLOAT(0,{range:[-180,180]}),this.depth=oa.FLOAT(1,{range:[0,10]})}},KU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",tG=new p.a(0,1,0),eG=new p.a(-1,0,0);class nG extends _z{constructor(){super(...arguments),this.paramsConfig=QU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new au.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(KU)||this.dirtyController.addPostDirtyHook(KU,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(tG,Object(Ln.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(eG,Object(Ln.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class iG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=hG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||Ml.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=sr.escapeLineBreaks(t)}}class rG{constructor(t){this._node=t}data(t={}){var e,n,i,r,s,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(r=this._node.flags.display)||void 0===r?void 0:r.active())),this._node.flags.hasOptimize()&&(null===(s=this._node.flags.optimize)||void 0===s?void 0:s.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=hG.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=iG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const r=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const s=r.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[s])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=hG.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=hG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class sG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class oG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class aG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class lG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class cG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class uG extends rG{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class hG{static dispatch_node(t){return t.polyNodeController?new uG(t):new rG(t)}static dispatch_param(t){return t instanceof no?new oG(t):t instanceof po?new aG(t):t instanceof bo?new lG(t):t instanceof xo?new cG(t):new sG(t)}}class dG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=ai.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new dG;if(this._objects){const e=[];for(let t of this._objects)e.push(vs.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new vs(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=ps.pointsCount(t.geometry)),{type:Nr(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new ps(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new ps(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=ps.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=sr.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new dG;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>sr.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new XB.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case Vr.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=dG.geometryFromObject(n);if(i){new ps(i).renameAttrib(t,e)}}));break;case Vr.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new vs(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=sr.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(sr.matchMask(t,i))n.push(t);else{t==Wr.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=Wr.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new In.a;return t.children.forEach((t=>{const n=vs.clone(t);e.add(n)})),e}}class pG extends Fl{static context(){return Ki.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new dG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Sr.MESH){const n=pG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return pG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Cr[e])(t,n=n||Vr.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){pG.createIndexIfNone(t)}static createIndexIfNone(t){hs.createIndexIfNone(t)}}var _G;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(_G||(_G={}));const mG=\\\\\\\"input geometry\\\\\\\",fG=[mG,mG,mG,mG];class gG extends ia{constructor(){super(...arguments),this.flags=new zi(this)}static context(){return Ki.SOP}static displayedInputNames(){return fG}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,_G.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],_G.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,_G.FROM_SET_OBJECTS)}setGeometry(t,e=Sr.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],_G.FROM_SET_GEOMETRY)}setGeometries(t,e=Sr.MESH){const n=[];let i;for(let r of t)i=this.createObject(r,e),n.push(i);this._setContainerObjects(n,_G.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new dG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return pG.createObject(t,e,n)}createObject(t,e,n){return gG.createObject(t,e,n)}static createIndexIfNone(t){pG.createIndexIfNone(t)}_createIndexIfNone(t){gG.createIndexIfNone(t)}}const vG=new class extends aa{};class yG extends gG{constructor(){super(...arguments),this.paramsConfig=vG}static type(){return er.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){this.setCoreGroup(t[0])}}class xG extends gG{constructor(){super(...arguments),this.childrenDisplayController=new wG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const bG={dependsOnDisplayNode:!0};class wG{constructor(t,e=bG){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(yG.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Ai(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function TG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends xG{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new MG(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const AG=TG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.SOP,inputs:[0,4]});class EG extends AG{}class MG{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Xl({}),i=new zl(this.node);i.create_nodes(n,t);const r=this._definition.ui;r&&i.process_nodes_ui_data(n,r),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=hG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),r={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(r))}}static createNodeClass(t,e,n){switch(e){case Ki.SOP:return TG(t,n);case Ki.OBJ:return SG(t,n)}}}function SG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends _z{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this.polyNodeController=new MG(this,e)}static type(){return t}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const CG=SG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.OBJ});class NG extends CG{}class LG extends Q.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t{t.load(e,(function(t){n(t)}))}))}}var UG;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(UG||(UG={}));const GG=[UG.LINEAR,UG.INVERSE,UG.EXPONENTIAL];class VG extends(Sz(aa)){constructor(){super(...arguments),this.audio=oa.FOLDER(),this.listener=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=oa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Ls.AUDIO]}}),this.volume=oa.FLOAT(1),this.loop=oa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=oa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=oa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=oa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=oa.INTEGER(GG.indexOf(UG.LINEAR),{menu:{entries:GG.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=oa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=oa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0),this.play=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_pause(t)}})}}const HG=new VG;class jG extends _z{constructor(){super(...arguments),this.paramsConfig=HG,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(GG[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.update()))}_createHelper(t){const e=new DG(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(Ki.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new FG(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new zG(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var WG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(WG||(WG={}));const qG=[WG.ON_RENDER,WG.MANUAL];const XG=new class extends aa{constructor(){super(...arguments),this.object=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{YG.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=oa.INTEGER(0,{range:[0,100]}),this.updateMode=oa.INTEGER(qG.indexOf(WG.ON_RENDER),{callback:t=>{YG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:qG.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{YG.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:qG.indexOf(WG.MANUAL)}})}};class YG extends _z{constructor(){super(...arguments),this.paramsConfig=XG,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=qG[this.pv.updateMode];switch(t){case WG.ON_RENDER:return this._add_render_hook();case WG.MANUAL:return this._remove_render_hook()}ar.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,r,s){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==Ki.OBJ&&t.type()==YU.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class $G extends(Ca(Ea(va(ma(ua(aa)))))){}const JG=new $G;class ZG extends _z{constructor(){super(...arguments),this.paramsConfig=JG,this.hierarchyController=new Lz(this),this.SceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.SceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new fr;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class QG{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._update_required=this._controls_node.update_required()}update_required(){return this._update_required}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const KG=\\\\\\\"controls\\\\\\\";class tV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(KG)?this.node.params.get(KG):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(pr.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let r=!1,s=this._applied_controls_by_element_id.get(e.id);if(s&&s.get(i)&&(r=!0),!r){s=new Map,this._applied_controls_by_element_id.set(e.id,s),s.set(i,n);const r=await n.apply_controls(this.node.object,t);if(!r)return;const o=new QG(this.node.graphNodeId(),n,r);return this.set_controls_events(r),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.dispose_controls_for_html_element_id(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=qV[this.node.pv.updateFromControlsMode];switch(e){case WV.ON_END:return this._set_controls_events_to_update_on_end(t);case WV.ALWAYS:return this._set_controls_events_to_update_always(t);case WV.NEVER:return this._reset(t)}ar.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function eV(t){return class extends t{constructor(){super(...arguments),this.layer=oa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class nV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const iV={callback:t=>{eH.PARAM_CALLBACK_reset_effects_composer(t)}};function rV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=oa.BOOLEAN(0),this.postProcessNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[tr.POST]},...iV})}}}class sV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,r=this.node.p.postProcessNode.value.node();if(r){if(r.type()==tr.POST){const s=r,o=this.node.renderController.canvas_resolution(t);return s.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class oV extends ia{constructor(){super(...arguments),this.flags=new Oi(this)}static context(){return Ki.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var aV,lV,cV,uV;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(aV||(aV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(lV||(lV={})),(uV=cV||(cV={}))[uV.Linear=w.U]=\\\\\\\"Linear\\\\\\\",uV[uV.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",uV[uV.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",uV[uV.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",uV[uV.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",uV[uV.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",uV[uV.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",uV[uV.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const hV=[lV.Linear,lV.sRGB,lV.Gamma,lV.RGBE,lV.LogLuv,lV.RGBM7,lV.RGBM16,lV.RGBD],dV=[cV.Linear,cV.sRGB,cV.Gamma,cV.RGBE,cV.LogLuv,cV.RGBM7,cV.RGBM16,cV.RGBD],pV=cV.sRGB;var _V,mV,fV;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(_V||(_V={})),(fV=mV||(mV={}))[fV.No=w.vb]=\\\\\\\"No\\\\\\\",fV[fV.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",fV[fV.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",fV[fV.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",fV[fV.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const gV=[_V.No,_V.Linear,_V.Reinhard,_V.Cineon,_V.ACESFilmic],vV=[mV.No,mV.Linear,mV.Reinhard,mV.Cineon,mV.ACESFilmic],yV=mV.ACESFilmic,xV=gV.map(((t,e)=>({name:t,value:vV[e]})));var bV;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(bV||(bV={}));const wV=[bV.HIGH,bV.MEDIUM,bV.LOW];var TV;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(TV||(TV={}));const AV=[TV.HIGH,TV.LOW,TV.DEFAULT];var EV,MV,SV;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(EV||(EV={})),(SV=MV||(MV={}))[SV.Basic=w.k]=\\\\\\\"Basic\\\\\\\",SV[SV.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",SV[SV.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",SV[SV.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const CV=[EV.Basic,EV.PCF,EV.PCFSoft,EV.VSM],NV=[MV.Basic,MV.PCF,MV.PCFSoft,MV.VSM],LV=(w.k,w.Fb,w.Gb,w.gd,MV.PCFSoft),OV={alpha:!1,precision:bV.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:TV.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const RV=new class extends aa{constructor(){super(...arguments),this.tprecision=oa.BOOLEAN(0),this.precision=oa.INTEGER(wV.indexOf(bV.HIGH),{visibleIf:{tprecision:1},menu:{entries:wV.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=oa.BOOLEAN(0),this.powerPreference=oa.INTEGER(AV.indexOf(TV.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:AV.map(((t,e)=>({value:e,name:t})))}}),this.alpha=oa.BOOLEAN(1),this.premultipliedAlpha=oa.BOOLEAN(1),this.antialias=oa.BOOLEAN(1),this.stencil=oa.BOOLEAN(1),this.depth=oa.BOOLEAN(1),this.logarithmicDepthBuffer=oa.BOOLEAN(0),this.toneMapping=oa.INTEGER(yV,{menu:{entries:xV}}),this.toneMappingExposure=oa.FLOAT(1,{range:[0,2]}),this.outputEncoding=oa.INTEGER(pV,{menu:{entries:hV.map(((t,e)=>({name:t,value:dV[e]})))}}),this.physicallyCorrectLights=oa.BOOLEAN(1),this.sortObjects=oa.BOOLEAN(1),this.tpixelRatio=oa.BOOLEAN(0),this.pixelRatio=oa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=oa.BOOLEAN(1),this.shadowMapAutoUpdate=oa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=oa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=oa.INTEGER(LV,{visibleIf:{tshadowMap:1},menu:{entries:CV.map(((t,e)=>({name:t,value:NV[e]})))}})}};class PV extends oV{constructor(){super(...arguments),this.paramsConfig=RV,this._renderers_by_canvas_id={}}static type(){return aV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(OV);let r;for(r of i)n[r]=OV[r];if(this.pv.tprecision){const t=wV[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=AV[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const s=ai.renderersController.createWebGLRenderer(n);return ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),ai.renderersController.printDebugMessage({params:n})),this._update_renderer(s),this._renderers_by_canvas_id[t.id]=s,s}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:FV.defaultPixelRatio();ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),ai.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function IV(t){return class extends t{constructor(){super(...arguments),this.render=oa.FOLDER(),this.setScene=oa.BOOLEAN(0),this.scene=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]}}),this.setRenderer=oa.BOOLEAN(0),this.renderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:Ki.ROP,types:[PV.type()]}}),this.setCSSRenderer=oa.BOOLEAN(0),this.CSSRenderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:Ki.ROP,types:[aV.CSS2D,aV.CSS3D]}})}}}class FV{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,this.node.object)}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(Ki.OBJ,ZG.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(Ki.ROP,aV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(Ki.ROP,[aV.CSS2D,aV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=ai.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=FV._createDefaultRenderer(t,n)),ai.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return Zf.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=ai.renderersController.createWebGLRenderer(n),r=this.defaultPixelRatio();return i.setPixelRatio(r),i.shadowMap.enabled=!0,i.shadowMap.type=LV,i.physicallyCorrectLights=!0,i.toneMapping=yV,i.toneMappingExposure=1,i.outputEncoding=pV,ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),ai.renderersController.printDebugMessage({params:n,pixelRatio:r})),i}delete_renderer(t){const e=this.renderer(t);e&&ai.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class DV{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(){this._config&&this._controls&&this._config.update_required()&&this._controls.update()}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Ai(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.viewer.controlsController.create_controls()})),t}}class kV{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class BV{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const zV=\\\\\\\"hovered\\\\\\\";class UV{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=UV._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new kV(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Ga(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new BV(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(zV)}setContainerClassHovered(){this.domElement().classList.add(zV)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}UV._next_viewer_id=0;class GV extends UV{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new DV(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t;if(this._do_render){if(this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(this._scene.timeController.incrementTimeIfPlaying(),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t();this.render(),null===(t=this._controls_controller)||void 0===t||t.update()}}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.delete_renderer(this._canvas)}render(){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let t of this._onBeforeRenderCallbacks)t();const t=this.camerasController.size,e=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,t,e),this._onAfterRenderCallbacks)for(let t of this._onAfterRenderCallbacks)t()}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const VV={type:\\\\\\\"change\\\\\\\"},HV=1,jV=100;var WV;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(WV||(WV={}));const qV=[WV.ON_END,WV.ALWAYS,WV.NEVER];function XV(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=oa.BUTTON(null,{callback:(t,e)=>{tH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function YV(t){return class extends t{constructor(){super(...arguments),this.camera=oa.FOLDER(),this.controls=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.EVENT}}),this.updateFromControlsMode=oa.INTEGER(qV.indexOf(WV.ON_END),{menu:{entries:qV.map(((t,e)=>({name:t,value:e})))}}),this.near=oa.FLOAT(HV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=oa.FLOAT(jV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0)}}}var $V;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}($V||($V={}));const JV=[$V.DEFAULT,$V.COVER,$V.CONTAIN];function ZV(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=oa.INTEGER(JV.indexOf($V.DEFAULT),{menu:{entries:JV.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=oa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:JV.indexOf($V.COVER)},{fovAdjustMode:JV.indexOf($V.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}XV(aa);rV(IV(Sz(eV(YV(XV(aa))))));class QV extends _z{constructor(){super(...arguments),this.renderOrder=pz.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(VV),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){Mz.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class KV extends QV{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new tV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new nV(this)}get renderController(){return this._render_controller=this._render_controller||new FV(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new sV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(VV),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new GV(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new jz(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class tH extends QV{}class eH extends KV{PARAM_CALLBACK_update_effects_composer(t){}}const nH=-.5,iH=.5,rH=.5,sH=-.5;class oH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1)}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const aH=new oH;class lH extends KV{constructor(){super(...arguments),this.paramsConfig=aH}static type(){return nr.ORTHOGRAPHIC}createObject(){return new st.a(2*nH,2*iH,2*rH,2*sH,HV,jV)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=nH*e*1,this._object.right=iH*e*1,this._object.top=rH*t*1,this._object.bottom=sH*t*1}}const cH=50;class uH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.fov=oa.FLOAT(cH,{range:[0,100]})}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const hH=new uH;class dH extends KV{constructor(){super(...arguments),this.paramsConfig=hH}static type(){return nr.PERSPECTIVE}createObject(){return new K.a(cH,1,HV,jV)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}}}class pH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=oa.FOLDER(),this.resolution=oa.INTEGER(256),this.excludedObjects=oa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_printResolve(t)}}),this.near=oa.FLOAT(1),this.far=oa.FLOAT(100),this.render=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_render(t)}}),this.renderTarget=oa.FOLDER(),this.tencoding=oa.BOOLEAN(0),this.encoding=oa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(Sz(aa))){}const _H=new pH;class mH extends _z{constructor(){super(...arguments),this.paramsConfig=_H,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new NU(1)}static type(){return Ng.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new In.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,r=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&r==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new it(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new et(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=ai.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class fH extends _z{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class gH extends fH{}class vH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class yH extends vH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER}}class xH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class bH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class wH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TH extends fH{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const EH=[\\\\\\\"input pass\\\\\\\"];const MH={cook:!1,callback:function(t,e){SH.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class SH extends ia{constructor(){super(...arguments),this.flags=new ki(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return Ki.POST}static displayedInputNames(){return EH}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const CH={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var NH={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class LH extends Im{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===Pm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Pm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===CH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(CH.uniforms),vertexShader:CH.vertexShader,fragmentShader:CH.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===NH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(NH.uniforms),vertexShader:NH.vertexShader,fragmentShader:NH.fragmentShader,blending:w.ub}),this.fsQuad=new km(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Z(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Z(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Z(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new at.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const OH=new class extends aa{constructor(){super(...arguments),this.adaptive=oa.BOOLEAN(1,{...MH}),this.averageLuminance=oa.FLOAT(.7,{...MH}),this.midGrey=oa.FLOAT(.04,{...MH}),this.maxLuminance=oa.FLOAT(16,{range:[0,20],...MH}),this.adaptiveRange=oa.FLOAT(2,{range:[0,10],...MH})}};class RH extends SH{constructor(){super(...arguments),this.paramsConfig=OH}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new LH(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const PH={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class IH extends Im{constructor(t=.96){super(),void 0===PH&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=PH,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new km(this.shaderMaterial);const e=new at.a;this.copyFsQuad=new km(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const FH=new class extends aa{constructor(){super(...arguments),this.damp=oa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...MH})}};class DH extends SH{constructor(){super(...arguments),this.paramsConfig=FH}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new IH;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const kH={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const BH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...MH})}};class zH extends SH{constructor(){super(...arguments),this.paramsConfig=BH}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new Bm(kH);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const UH={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const GH=new class extends aa{constructor(){super(...arguments),this.brightness=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.contrast=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class VH extends SH{constructor(){super(...arguments),this.paramsConfig=GH}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new Bm(UH);return console.log(\\\\\\\"brightness\\\\\\\",e),e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class HH extends Im{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const jH=new class extends aa{};class WH extends SH{constructor(){super(...arguments),this.paramsConfig=jH}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new HH;return this.updatePass(e),e}updatePass(t){}}const qH=new class extends aa{};class XH extends SH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const YH={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const $H=new class extends aa{constructor(){super(...arguments),this.pow=oa.VECTOR3([2,2,2],{...MH}),this.mult=oa.COLOR([1,1,1],{...MH}),this.add=oa.COLOR([0,0,0],{...MH})}};class JH extends SH{constructor(){super(...arguments),this.paramsConfig=$H}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new Bm(YH);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const ZH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class QH extends SH{constructor(){super(...arguments),this.paramsConfig=ZH}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new Bm(Pm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const KH={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth; //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4 HS0 = vec4( 1.0, 0.0, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS1 = vec4( 0.309016994, 0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS2 = vec4(-0.809016994, 0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS3 = vec4(-0.809016994,-0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS4 = vec4( 0.309016994,-0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS5 = vec4( 0.0 ,0.0 , 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x, 0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0, 0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x, 0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0, wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0; kernel[1] = 2.0/16.0; kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0; kernel[4] = 4.0/16.0; kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0; kernel[7] = 2.0/16.0; kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},tj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class ej{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const r={};let s;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(s=a,s.uniforms)for(let t of Object.keys(n))s.uniforms[t].value=n[t].value}else s=ps.markedAsInstance(i)?e:t;s&&(r[o.uuid]=o.material,o.material=s)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=r[e.uuid])}}));for(let t of Object.keys(r))delete r[t]}}class nj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new fr,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new ej(this._scene);const r=3,s=4;this._processing_camera=new st.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Z(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Z(this._resolution.x,this._resolution.y,o);var a=KH;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:r,SAMPLES:s}}),this._quad=new k.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=tj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=rj.smoothstep(e.near,e.far,t.pv.focalDepth),i=rj.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const ij=new class extends aa{constructor(){super(...arguments),this.focalDepth=oa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...MH}),this.fStep=oa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...MH}),this.maxBlur=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.vignetting=oa.BOOLEAN(0,{...MH}),this.depthBlur=oa.BOOLEAN(0,{...MH}),this.threshold=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...MH}),this.gain=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...MH}),this.bias=oa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...MH}),this.fringe=oa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...MH}),this.noise=oa.BOOLEAN(0,{...MH}),this.dithering=oa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...MH}),this.pentagon=oa.BOOLEAN(0,{...MH}),this.rings=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...MH}),this.samples=oa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...MH}),this.clearColor=oa.COLOR([1,1,1],{...MH})}};class rj extends SH{constructor(){super(...arguments),this.paramsConfig=ij}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new nj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Ai(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const sj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const oj=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR2([.5,.5],{...MH}),this.angle=oa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...MH}),this.scale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH})}};class aj extends SH{constructor(){super(...arguments),this.paramsConfig=oj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new Bm(sj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const lj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class cj extends Im{constructor(t,e,n,i){super(),void 0===lj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const r=lj;this.uniforms=I.clone(r.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:r.vertexShader,fragmentShader:r.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new km(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const uj=new class extends aa{constructor(){super(...arguments),this.noiseIntensity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.scanlinesIntensity=oa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.scanlinesCount=oa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...MH}),this.grayscale=oa.BOOLEAN(1,{...MH})}};class hj extends SH{constructor(){super(...arguments),this.paramsConfig=uj}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new cj(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const dj={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const pj=new class extends aa{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(1,MH)}};class _j extends SH{constructor(){super(...arguments),this.paramsConfig=pj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new Bm(dj);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const mj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const fj=new class extends aa{};class gj extends SH{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new Bm(mj);return this.updatePass(e),e}updatePass(t){}}const vj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class yj extends SH{constructor(){super(...arguments),this.paramsConfig=vj}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new Bm(IU);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const xj=new class extends aa{constructor(){super(...arguments),this.map=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP},...MH}),this.darkness=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH}),this.offset=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class bj extends SH{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new Bm(bj._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==Ki.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const wj={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},Tj=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Aj=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class Ej extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(wj),this.material=new F({uniforms:this.uniforms,vertexShader:Tj,fragmentShader:Aj,transparent:!0}),this.fsQuad=new km(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const Mj=new class extends aa{};class Sj extends SH{constructor(){super(...arguments),this.paramsConfig=Mj}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=new Gm(e,i),o=new Gm(e,r);s.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=s,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new Ej(s,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const Cj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH}),this.inverse=oa.BOOLEAN(0,MH)}};class Nj extends SH{constructor(){super(...arguments),this.paramsConfig=Cj}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new zm(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const Lj=new class extends aa{};class Oj extends SH{constructor(){super(...arguments),this.paramsConfig=Lj}static type(){return\\\\\\\"null\\\\\\\"}}class Rj extends Im{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const r={minFilter:w.V,magFilter:w.V,format:w.Ib},s=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new at.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Mn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Z(s,o,r),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Z(s,o,r),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Z(s,o,r),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(s,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(s/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Pm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Pm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new km(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,r&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,r&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tuniform mat4 textureMatrix;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvPosition = mvPosition;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D depthTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 cameraNearFar;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getEdgeDetectionMaterial(){return new F({uniforms:{maskTexture:{value:null},texSize:{value:new d.a(.5,.5)},visibleEdgeColor:{value:new p.a(1,1,1)},hiddenEdgeColor:{value:new p.a(1,1,1)}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getSeperableBlurMaterial(t){return new F({defines:{MAX_RADIUS:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)},kernelRadius:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}Rj.BlurDirectionX=new d.a(1,0),Rj.BlurDirectionY=new d.a(0,1);const Pj=new class extends aa{constructor(){super(...arguments),this.objectsMask=oa.STRING(\\\\\\\"*outlined*\\\\\\\",{...MH}),this.refreshObjects=oa.BUTTON(null,{...MH}),this.printObjects=oa.BUTTON(null,{cook:!1,callback:t=>{Ij.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=oa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.edgeThickness=oa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...MH}),this.edgeGlow=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.pulsePeriod=oa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...MH}),this.visibleEdgeColor=oa.COLOR([1,1,1],{...MH}),this.hiddenEdgeColor=oa.COLOR([.2,.1,.4],{...MH})}};class Ij extends SH{constructor(){super(...arguments),this.paramsConfig=Pj,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new Rj(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const Fj={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Dj=new class extends aa{constructor(){super(...arguments),this.pixelSize=oa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...MH})}};class kj extends SH{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new Bm(Fj);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const Bj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH})}};class zj extends SH{constructor(){super(...arguments),this.paramsConfig=Bj}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(Ki.OBJ);if(e&&(e.type()==nr.PERSPECTIVE||e.type()==nr.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(Ki.OBJ);if(e&&e.type()==ZG.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const Uj={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Gj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.angle=oa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...MH})}};class Vj extends SH{constructor(){super(...arguments),this.paramsConfig=Gj}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new Bm(Uj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const Hj={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const jj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...MH})}};class Wj extends SH{constructor(){super(...arguments),this.paramsConfig=jj}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new Bm(Hj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const qj=new class extends aa{};class Xj extends SH{constructor(){super(...arguments),this.paramsConfig=qj}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const Yj=I.clone(IU.uniforms);Yj.delta={value:new d.a};const $j={uniforms:Yj,vertexShader:IU.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const Jj=new class extends aa{constructor(){super(...arguments),this.delta=oa.VECTOR2([2,2],{...MH})}};class Zj extends SH{constructor(){super(...arguments),this.paramsConfig=Jj}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new Bm($j);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const Qj={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Kj extends Im{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const r={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let s=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Z(s,o,r),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}Kj.BlurDirectionX=new d.a(1,0),Kj.BlurDirectionY=new d.a(0,1);const tW=new class extends aa{constructor(){super(...arguments),this.strength=oa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...MH}),this.radius=oa.FLOAT(1,{...MH}),this.threshold=oa.FLOAT(0,{...MH})}};class eW extends SH{constructor(){super(...arguments),this.paramsConfig=tW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new Kj(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const nW=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class iW extends SH{constructor(){super(...arguments),this.paramsConfig=nW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new Bm(FU);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const rW={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const sW=new class extends aa{constructor(){super(...arguments),this.offset=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.darkness=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class oW extends SH{constructor(){super(...arguments),this.paramsConfig=sW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new Bm(rW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class aW extends ia{static context(){return Ki.POST}cook(){this.cookController.endCook()}}class lW extends aW{}class cW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class pW extends aW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _W extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mW extends ia{static context(){return Ki.ROP}cook(){this.cookController.endCook()}}class fW extends mW{}class gW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class vW extends Q.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof vW&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return Q.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class yW{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof vW){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,r=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=r,i.style.transform=r,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=rs.fit(t,this._fog_near,this._fog_far,0,1),r=rs.clamp(1-n,0,1);i.style.opacity=`${r}`,0==r&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var s=0,o=t.children.length;s{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class wW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class EW extends mW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class MW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class SW extends pG{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let r=[];const s=[];let o;for(let t=0;t0&&(s.push(t-1),s.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(r,r.length);const t=s[s.length-1];s.push(t),s.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s);const l=this.createObject(a,Sr.LINE_SEGMENTS);e.push(l)}}SW.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const CW=SW.DEFAULT_PARAMS;const NW=new class extends aa{constructor(){super(...arguments),this.createPoint=oa.BOOLEAN(CW.createPoint),this.pointsCount=oa.INTEGER(CW.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=oa.VECTOR3(CW.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=oa.BOOLEAN(CW.connectInputPoints),this.connectToLastPoint=oa.BOOLEAN(CW.connectToLastPoint)}};class LW extends gG{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new SW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const OW=new class extends aa{};class RW extends gG{constructor(){super(...arguments),this.paramsConfig=OW}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],r=n.animations;r?(i.animations=r.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class PW{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,o=new Array(s),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(s),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,s=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=r,a[1]=r+n,l[0]=t/s,l[1]=e/s,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=n===w.db;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(n===w.cb){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}var IW=n(71),FW=n(66);class DW{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){au.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;au.a.multiplyQuaternionsFlat(t,s,t,e,t,n),au.a.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}var kW=n(63);class BW extends $.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new DW(FW.a.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t{UW.PARAM_CALLBACK_reset(t,e)}})}};class UW extends gG{constructor(){super(...arguments),this.paramsConfig=zW}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(Ki.ANIM)){return new BW(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class GW extends pG{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,r=n.mult,s=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class QW extends gG{constructor(){super(...arguments),this.paramsConfig=ZW}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState($W.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>qW[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new $W(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class KW extends pG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,r=i.attribNamesMatchingMask(e.name);for(let t of r)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var r;const s=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>s.length)null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let u=t.getAttribute(c);if(u)this._fill_dest_array(u,o,i),u.needsUpdate=!0;else{const e=o.array.slice(0,l*s);t.setAttribute(c,new C.c(e,s))}}else null===(s=this.states)||void 0===s||s.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,r=e.array,s=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new KW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class iq extends pG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Ir[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=kr[n.type];switch(t){case Pr.VERTEX:return void await this.add_point_attribute(i,e,n);case Pr.OBJECT:return void await this.add_object_attribute(i,e,n)}ar.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(Ir[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=kr[this.pv.type];switch(t){case Pr.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Pr.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ar.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t{r[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let s;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a{i[a][t.index()]=e}));else{const t=s.value;for(let e of n)r[e.index()*this.pv.size+a]=t}for(let t=0;t{t.setAttribValue(this.pv.name,e)}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components,n={};const i=this._vector_by_attrib_size(this.pv.size);if(i){for(let e of t)n[e.index()]=i;for(let i=0;i{n[t.index()][s]=e}));else for(let e of t){n[e.index()][s]=r.value}}for(let e=0;e{i[t.index()]=e}));const r=Wr.arrayToIndexedArrays(i),s=t.coreGeometry();s&&s.setIndexedAttribute(this.pv.name,r.values,r.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.compute_expression_for_objects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Pr.VERTEX:this.delete_vertex_attribute(e,t);case Pr.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new ps(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new vs(n,t).deleteAttribute(e),t++}))}}}class uq{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,r=t.mult,s=this._data_from_texture(t.texture);if(!s)return;const{data:o,resx:a,resy:l}=s,c=o.length/(a*l),u=e.getAttribute(t.uvAttribName).array,h=u.length/2,d=new Array(h*n);let p,_,m,f,g,v,y,x,b;const w=rs.clamp;for(v=0;v{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new hq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var mq;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(mq||(mq={}));const fq=[mq.MIN_MAX_TO_01,mq.VECTOR_TO_LENGTH_1];class gq extends pG{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),r=sr.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of r){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(fq[n.mode]){case mq.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case mq.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,r=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n({name:t,value:e})))}}),this.name=oa.STRING(vq.name),this.changeName=oa.BOOLEAN(vq.changeName),this.newName=oa.STRING(vq.newName,{visibleIf:{changeName:1}})}};class xq extends gG{constructor(){super(...arguments),this.paramsConfig=yq}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(gq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(fq.indexOf(t))}cook(t){this._operation=this._operation||new gq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var bq;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(bq||(bq={}));class wq extends pG{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=sr.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Pr.VERTEX:return this.find_values_from_points(t,e);case Pr.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let r=0;r{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Fr.filter((t=>t.value==this.pv.classFrom))[0].name,e=Fr.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new wq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Sq=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(),this.ramp=oa.RAMP(),this.changeName=oa.BOOLEAN(0),this.newName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class Cq extends gG{constructor(){super(...arguments),this.paramsConfig=Sq}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let r=new Array(e.length);this._get_remaped_values(n,i,r);let s=this.pv.name;this.pv.changeName&&(s=this.pv.newName,t.hasAttrib(s)||t.addNumericVertexAttrib(s,n,0));let o=0;for(let t of r){e[o].setAttribValue(s,t),o++}}_get_remaped_values(t,e,n){switch(t){case zr.FLOAT:return this._get_normalized_float(e,n);case zr.VECTOR2:return this._get_normalized_vector2(e,n);case zr.VECTOR3:return this._get_normalized_vector3(e,n);case zr.VECTOR4:return this._get_normalized_vector4(e,n)}ar.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var Oq=n(18);class Rq{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new Rq(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new XB.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class Pq{constructor(t){this._root=new Rq(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new Oq.a(t,e);let r=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,r),null==n||r.length>n&&(r=f.sortBy(r,(e=>e.position().distanceTo(t))),r=r.slice(0,n)),r}}class Iq{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const Fq=new class extends aa{constructor(){super(...arguments),this.srcGroup=oa.STRING(),this.destGroup=oa.STRING(),this.name=oa.STRING(),this.maxSamplesCount=oa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=oa.FLOAT(1),this.blendWidth=oa.FLOAT(0)}};class Dq extends gG{constructor(){super(...arguments),this.paramsConfig=Fq}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new Pq(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new Iq;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,r){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,r);default:return this._interpolate_with_multiple_points(t,e,n,i,r)}}static _interpolate_with_1_point(t,e,n,i,r){const s=t.position(),o=e.position(),a=s.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,r):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,r,s){if(i<=r)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,r,s);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,r){const s=e.map((e=>this._interpolate_with_1_point(t,e,n,i,r)));return f.max(s)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const kq=new class extends aa{constructor(){super(...arguments),this.stepSize=oa.FLOAT(.1)}};class Bq extends gG{constructor(){super(...arguments),this.paramsConfig=kq}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),r=i.min,s=i.max,o=[];for(let t=r.x;t<=s.x;t+=n)for(let e=r.y;e<=s.y;e+=n)for(let i=r.z;i<=s.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Sr.POINTS)}}const zq=new class extends aa{constructor(){super(...arguments),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.blend=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class Uq extends gG{constructor(){super(...arguments),this.paramsConfig=zq}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),r=n.objects();let s,o;for(let t=0;tt.flip())),t}}Gq.fromPolygons=function(t){let e=new Gq;return e.polygons=t,e};class Vq{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new Vq(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(Hq.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,r=e.z,s=t.x,o=t.y,a=t.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let Hq=new Vq,jq=new Vq;class Wq{constructor(t,e,n,i){this.pos=(new Vq).copy(t),this.normal=(new Vq).copy(e),this.uv=(new Vq).copy(n),this.uv.z=0,i&&(this.color=(new Vq).copy(i))}clone(){return new Wq(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new Wq(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class qq{constructor(t,e){this.normal=t,this.w=e}clone(){return new qq(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,r){let s=0,o=[];for(let e=0;eqq.EPSILON?1:0;s|=i,o.push(i)}switch(s){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:r.push(t);break;case 3:let s=[],a=[];for(let e=0;e=3&&i.push(new Xq(s,t.shared)),a.length>=3&&r.push(new Xq(a,t.shared))}}}qq.EPSILON=1e-5,qq.fromPoints=function(t,e,n){let i=Hq.copy(e).sub(t).cross(jq.copy(n).sub(t)).normalize();return new qq(i.clone(),i.dot(t))};class Xq{constructor(t,e){this.vertices=t,this.shared=e,this.plane=qq.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new Xq(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class Yq{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new Yq;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;tnew Xq(t.vertices.map((t=>new Wq(t.pos,t.normal,t.uv))),t.shared))))},Gq.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,r=t.vertices,s=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),Qq=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var Kq;Gq.toMesh=function(t,e,n){let i,r,s=t.polygons;{let t=0;s.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=Zq(3*t*3),o=Zq(3*t*3),a=Qq(2*t*3),l=[];if(s.forEach((i=>{let r=i.vertices,s=r.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),s&&void 0!==r[0].color&&(e||(e=Zq(3*t*3)));for(let t=3;t<=s;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(r[0].pos),n.write(r[t-2].pos),n.write(r[t-1].pos),o.write(r[0].normal),o.write(r[t-2].normal),o.write(r[t-1].normal),a.write(r[0].uv),a.write(r[t-2].uv),a.write(r[t-1].uv),e&&(e.write(r[0].color)||e.write(r[t-2].color)||e.write(r[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n({name:t,value:e})))}}),this.useBothMaterials=oa.BOOLEAN(nX.useBothMaterials)}};class rX extends gG{constructor(){super(...arguments),this.paramsConfig=iX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(eX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>tX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(tX.indexOf(t))}async cook(t){this._operation=this._operation||new eX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class sX extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),r=i.max.clone().sub(i.min),s=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(r.x,r.y,r.z,n,n,n),a=this._core_transform.translation_matrix(s);return o.applyMatrix4(a),o}}sX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},sX.INPUT_CLONED_STATE=Qi.NEVER;const oX=sX.DEFAULT_PARAMS;const aX=new class extends aa{constructor(){super(...arguments),this.size=oa.FLOAT(oX.size),this.divisions=oa.INTEGER(oX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=oa.VECTOR3(oX.center)}};class lX extends gG{constructor(){super(...arguments),this.paramsConfig=aX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(sX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new sX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class cX extends C.a{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}cX.prototype.isInstancedBufferAttribute=!0;const uX=new A.a,hX=new A.a,dX=[],pX=new k.a;class _X extends k.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new cX(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(pX.geometry=this.geometry,pX.material=this.material,void 0!==pX.material)for(let r=0;rt.far||e.push({distance:a,point:fX.clone(),uv:Qr.a.getUV(fX,wX,TX,AX,EX,MX,SX,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function NX(t,e,n,i,r,s){yX.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(xX.x=s*yX.x-r*yX.y,xX.y=r*yX.x+s*yX.y):xX.copy(yX),t.copy(e),t.x+=xX.x,t.y+=xX.y,t.applyMatrix4(bX)}CX.prototype.isSprite=!0;var LX=n(94),OX=n(81),RX=n(46);class PX{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}PX.prototype.isSphericalHarmonics3=!0;class IX extends nv.a{constructor(t=new PX,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}IX.prototype.isLightProbe=!0;var FX=n(62),DX=n(44);class kX extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}kX.prototype.isInstancedBufferGeometry=!0;class BX extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],s=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],r=new Uint32Array(i).buffer;return n[e]=r,r}(t,r.buffer),o=Object(Pt.c)(r.type,s),a=new as.a(o,r.stride);return a.uuid=r.uuid,e[i]=a,a}const r=t.isInstancedBufferGeometry?new kX:new S.a,s=t.data.index;if(void 0!==s){const t=Object(Pt.c)(s.type,s.array);r.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let s;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);s=new ls.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(Pt.c)(n.type,n.array);s=new(n.isInstancedBufferAttribute?cX:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(s.name=n.name),void 0!==n.usage&&s.setUsage(n.usage),void 0!==n.updateRange&&(s.updateRange.offset=n.updateRange.offset,s.updateRange.count=n.updateRange.count),r.setAttribute(e,s)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],s=[];for(let e=0,r=n.length;eNumber.EPSILON){const h=Math.sqrt(u),p=Math.sqrt(l*l+c*c),_=e.x-a/h,m=e.y+o/h,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,r=m+a*f-t.y;const g=i*i+r*r;if(g<=2)return new d.a(i,r);s=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new d.a(i/s,r/s)}const P=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);r=new FX.a(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new FX.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{dY.PARAM_CALLBACK_reset(t,e)}})}};class dY extends gG{constructor(){super(...arguments),this.paramsConfig=hY}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new aY,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const pY=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE],_Y={direction:new p.a(0,1,0)},mY=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],fY=new p.a(0,0,1);const gY=new class extends aa{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:pY}}),this.direction=oa.VECTOR3(_Y.direction),this.offset=oa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=oa.BOOLEAN(!0),this.stepSize=oa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{vY.PARAM_CALLBACK_update(t)}})}};class vY extends gG{constructor(){super(...arguments),this.paramsConfig=gY,this._plane=new X.a,this._raycaster=new uL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new Mz,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(Ki.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!pY.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of mY){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),r=this._planeCorners[0].distanceTo(this._planeCorners[1]),s=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(r,s)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,fY,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class yY extends pG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),r=new Array(3*i.length);r.fill(0);for(let t=0;t0&&(s.push(t-1),s.push(t));s.push(e-1),s.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s),a}}const TY=new p.a(0,0,1);class AY extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=wY.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e,Sr.LINE_SEGMENTS)}_create_disk(t){const e=new zX(t.radius,t.segments);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e)}}AY.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const EY=AY.DEFAULT_PARAMS;const MY=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(EY.radius),this.segments=oa.INTEGER(EY.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=oa.BOOLEAN(EY.open),this.arcAngle=oa.FLOAT(EY.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=oa.VECTOR3(EY.direction)}};class SY extends gG{constructor(){super(...arguments),this.paramsConfig=MY}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new AY(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var CY;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(CY||(CY={}));const NY=[CY.SEGMENTS_COUNT,CY.SEGMENTS_LENGTH];var LY;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(LY||(LY={}));const OY=[LY.ABC,LY.ACB,LY.AB,LY.AC,LY.BC];class RY{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let r=0;for(r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r);this.params.full&&(n.push(r-1),n.push(0));const s=new S.a;if(s.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),s.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&s.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=s}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case CY.SEGMENTS_COUNT:return this.params.segmentsCount+1;case CY.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}ar.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case LY.ABC:case LY.ACB:case LY.AB:case LY.AC:return this.x.copy(this.an);case LY.BC:return this.x.copy(this.bn)}ar.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case LY.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case LY.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case LY.AB:return void(this.angle=this.angle_ab);case LY.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case LY.BC:return void(this.angle=this.angle_bc)}ar.unreachable(t)}}const PY=new class extends aa{constructor(){super(...arguments),this.arc=oa.BOOLEAN(1),this.pointsCountMode=oa.INTEGER(NY.indexOf(CY.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:NY.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=oa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=oa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=oa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=oa.INTEGER(OY.indexOf(LY.ABC),{visibleIf:{arc:1,full:0},menu:{entries:OY.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=oa.BOOLEAN(1),this.addIdnAttribute=oa.BOOLEAN(1),this.center=oa.BOOLEAN(0)}};class IY extends gG{constructor(){super(...arguments),this.paramsConfig=PY,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new RY({arc:this.pv.arc,center:this.pv.center,pointsCountMode:NY[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:OY[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Sr.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Sr.POINTS)),this.setObjects(n)}}class FY extends pG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}FY.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const DY=new D.a(1,1,1),kY=\\\\\\\"color\\\\\\\",BY=FY.DEFAULT_PARAMS;const zY=new class extends aa{constructor(){super(...arguments),this.fromAttribute=oa.BOOLEAN(BY.fromAttribute),this.attribName=oa.STRING(BY.attribName,{visibleIf:{fromAttribute:1}}),this.color=oa.COLOR(BY.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=oa.BOOLEAN(BY.asHsv,{visibleIf:{fromAttribute:0}})}};class UY extends gG{constructor(){super(...arguments),this.paramsConfig=zY,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(kY).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,DY);const n=e.points(),i=e.attribSize(this.pv.attribName),r=e.geometry(),s=r.getAttribute(this.pv.attribName).array,o=r.getAttribute(kY).array;switch(i){case 1:for(let t=0;t{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=s;return a}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(jY.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):jY.PSCALE;this._point_scale.multiplyScalar(i);const r=this._matrices.scale;r.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const s=this._matrices.translate;if(s.makeTranslation(n.x,n.y,n.z),e.multiply(s),this._do_rotate_matrices){const n=this._matrices.rotate,i=jY.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(jY.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(r)}static create_instance_buffer_geo(t,e,n){const i=e.points(),r=new kX;r.copy(t),r.instanceCount=1/0;const s=i.length,o=new Float32Array(3*s),a=new Float32Array(3*s),l=new Float32Array(3*s),c=new Float32Array(4*s),u=e.hasAttrib(XY),h=new p.a(0,0,0),d=new au.a,_=new p.a(1,1,1),f=new JY(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(h,d,_),h.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(u?t.attribValue(XY,this._point_color):WY).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*s);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):qY).toArray(t,i)})),r.setAttribute(YY.UV,new cX(t,2))}r.setAttribute(YY.POSITION,new cX(o,3)),r.setAttribute(YY.SCALE,new cX(l,3)),r.setAttribute(YY.ORIENTATION,new cX(c,4)),r.setAttribute(YY.COLOR,new cX(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(s*n);i.forEach(((e,i)=>{const r=e.attribValue(t);m.isNumber(r)?o[i]=r:r.toArray(o,i*n)})),r.setAttribute(t,new cX(o,n))}));return new ps(r).markAsInstance(),r}}JY._point_color=new p.a,JY._point_uv=new d.a;class ZY extends ru{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}($Y||($Y={}));const QY=[$Y.OBJECT,$Y.GEOMETRY],KY=[{name:\\\\\\\"object\\\\\\\",value:$Y.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:$Y.GEOMETRY}];const t$=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=oa.BOOLEAN(0),this.transformMode=oa.INTEGER(0,{menu:{entries:KY}}),this.copyAttributes=oa.BOOLEAN(0),this.attributesToCopy=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=oa.BOOLEAN(0)}};class e$ extends gG{constructor(){super(...arguments),this.paramsConfig=t$,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.ALWAYS,Qi.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new JY(e).matrices();const r=new p.a,s=new au.a,o=new p.a;i[0].decompose(r,s,o),this._attribute_names_to_copy=sr.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=vs.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const r=e.attribValue(n);new vs(t,i).addAttribute(n,r)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new ZY(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const n$=\\\\\\\"id\\\\\\\",i$=\\\\\\\"class\\\\\\\",r$=\\\\\\\"html\\\\\\\";class s$ extends pG{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(n$):e.className,r=e.useClassAttrib?t.attribValue(i$):e.className,s=e.useHtmlAttrib?t.attribValue(r$):e.html,o=s$.create_css_object({id:n,className:r,html:s}),a=o.element;if(e.copyAttributes){const n=sr.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return s$.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new vW(e);return n.matrixAutoUpdate=!1,n}}s$.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},s$.INPUT_CLONED_STATE=Qi.FROM_NODE;const o$=s$.DEFAULT_PARAMS;const a$=new class extends aa{constructor(){super(...arguments),this.useIdAttrib=oa.BOOLEAN(o$.useIdAttrib),this.id=oa.STRING(o$.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=oa.BOOLEAN(o$.useClassAttrib),this.className=oa.STRING(o$.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=oa.BOOLEAN(o$.useHtmlAttrib),this.html=oa.STRING(o$.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=oa.BOOLEAN(o$.copyAttributes),this.attributesToCopy=oa.STRING(o$.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class l$ extends gG{constructor(){super(...arguments),this.paramsConfig=a$}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new s$(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c${constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Dr.STRING:Dr.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class u${constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new ps(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=sr.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const r=Wr.remapName(n);let s=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Dr.STRING)if(this._options.doConvert&&sr.matchesOneMask(n,i)){const e=s.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(r,new C.c(e,a))}else{const t=Wr.arrayToIndexedArrays(s);e.setIndexedAttribute(r,t.values,t.indices)}else{const e=s;t.setAttribute(r,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=sr.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const r=[n,t].join(\\\\\\\":\\\\\\\"),s=i[n];sr.matchesOneMask(r,e)||(this._attribute_datas_by_name[r]=c$.from_value(s))}else sr.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=c$.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const h$=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const d$=new class extends aa{constructor(){super(...arguments),this.data=oa.STRING(h$)}};class p$ extends gG{constructor(){super(...arguments),this.paramsConfig=d$}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new u$;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Sr.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class _$ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new u$(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const r=this._parser.createObject();t(r)})).catch((t=>{ai.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const m$=\\\\\\\"position\\\\\\\";class f${constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(f$.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>Wr.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(f$.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=oa.STRING(y$,{fileBrowse:{type:[Ls.JSON]}}),this.jsonDataKeysPrefix=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.skipEntries=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convert=oa.BOOLEAN(0,{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convertToNumeric=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON),convert:1}}),this.readAttribNamesFromFile=oa.BOOLEAN(1,{visibleIf:{dataType:v$.indexOf(g$.CSV)}}),this.attribNames=oa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.CSV),readAttribNamesFromFile:0}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{b$.PARAM_CALLBACK_reload(t,e)}})}};class b$ extends gG{constructor(){super(...arguments),this.paramsConfig=x$}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(v$[this.pv.dataType]){case g$.JSON:return this._load_json();case g$.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new _$(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Sr.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new f$(t),n=await e.load(this._url());n?this.setGeometry(n,Sr.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class w$ extends S.a{constructor(t,e,n,i){super();const r=[],s=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function u(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new T$(n.clone(),i.clone()))}function h(t,e){const n=[],r=.5*Math.abs(i.dot(e));for(let i=0;i0,h=t[i+1].position.dot(e)-r>0,p=t[i+2].position.dot(e)-r>0;switch(c=(u?1:0)+(h?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(u&&(s=t[i+1],o=t[i+2],a=d(t[i],s,e,r),l=d(t[i],o,e,r)),h){s=t[i],o=t[i+2],a=d(t[i+1],s,e,r),l=d(t[i+1],o,e,r),n.push(a),n.push(o.clone()),n.push(s.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(s=t[i],o=t[i+1],a=d(t[i+2],s,e,r),l=d(t[i+2],o,e,r)),n.push(s.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:u||(s=t[i].clone(),o=d(s,t[i+1],e,r),a=d(s,t[i+2],e,r),n.push(s),n.push(o),n.push(a)),h||(s=t[i+1].clone(),o=d(s,t[i+2],e,r),a=d(s,t[i],e,r),n.push(s),n.push(o),n.push(a)),p||(s=t[i+2].clone(),o=d(s,t[i],e,r),a=d(s,t[i+1],e,r),n.push(s),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const r=t.position.dot(n)-i,s=r/(r-(e.position.dot(n)-i));return new T$(new p.a(t.position.x+s*(e.position.x-t.position.x),t.position.y+s*(e.position.y-t.position.y),t.position.z+s*(e.position.z-t.position.z)),new p.a(t.normal.x+s*(e.normal.x-t.normal.x),t.normal.y+s*(e.normal.y-t.normal.y),t.normal.z+s*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class L${constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,r)=>{n==e&&(t[i]=r,i++)})),t}}}var O$;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(O$||(O$={}));const R$=[O$.EQUAL,O$.LESS_THAN,O$.EQUAL_OR_LESS_THAN,O$.EQUAL_OR_GREATER_THAN,O$.GREATER_THAN,O$.DIFFERENT],P$={[O$.EQUAL]:(t,e)=>t==e,[O$.LESS_THAN]:(t,e)=>tt<=e,[O$.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[O$.GREATER_THAN]:(t,e)=>t>e,[O$.DIFFERENT]:(t,e)=>t!=e},I$=R$.map(((t,e)=>({name:t,value:e})));class F${constructor(t){this.node=t}evalForEntities(t){const e=kr[this.node.pv.attribType];switch(e){case Dr.NUMERIC:return void this._eval_for_numeric(t);case Dr.STRING:return void this._eval_for_string(t)}ar.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Ur[this.node.pv.attribSize-1];switch(e){case zr.FLOAT:return this._eval_for_points_numeric_float(t);case zr.VECTOR2:return this._eval_for_points_numeric_vector2(t);case zr.VECTOR3:return this._eval_for_points_numeric_vector3(t);case zr.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ar.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const r=R$[this.node.pv.attribComparisonOperator],s=P$[r];for(let r of t)i=r.attribValue(e),s(i,n)&&this.node.entitySelectionHelper.select(r)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}}class D${constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class k${constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new XB.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class B${constructor(t){this.node=t}eval_for_objects(t){const e=Lr[this.node.pv.objectType];for(let n of t){Nr(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class z${constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const U$=new p.a(0,1,0),G$=new p.a(0,-1,0);class V${constructor(t){this.node=t,this._matDoubleSideTmpSetter=new z$,this._point_position=new p.a,this._raycaster=new uL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const r=n.geometry;r.computeBoundingBox();const s=r.boundingBox;for(let e of t)e.getPosition(this._point_position),s.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,U$)&&this._isPositionInObject(this._point_position,i,G$)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const r=this._raycaster.intersectObject(e,!1,this._intersections);if(!r)return!1;if(0==r.length)return!1;const s=null===(i=r[0].face)||void 0===i?void 0:i.normal;if(!s)return!1;return this._raycaster.ray.direction.dot(s)>=0}}const H$=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Ir.indexOf(Pr.VERTEX),{menu:{entries:Fr}}),this.invert=oa.BOOLEAN(0),this.byObjectType=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}}),this.objectType=oa.INTEGER(Lr.indexOf(Sr.MESH),{menu:{entries:Or},visibleIf:{class:Ir.indexOf(Pr.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=oa.BOOLEAN(0),this.expression=oa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=oa.BOOLEAN(0),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},visibleIf:{byAttrib:1}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=oa.INTEGER(1,{range:Gr,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC)}}),this.attribComparisonOperator=oa.INTEGER(R$.indexOf(O$.EQUAL),{menu:{entries:I$},visibleIf:{byAttrib:!0,attribType:kr.indexOf(Dr.NUMERIC),attribSize:zr.FLOAT}}),this.attribValue1=oa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:1}}),this.attribValue2=oa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:2}}),this.attribValue3=oa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:3}}),this.attribValue4=oa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:4}}),this.attribString=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.STRING)},separatorAfter:!0}),this.byBbox=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.bboxSize=oa.VECTOR3([1,1,1],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0}}),this.bboxCenter=oa.VECTOR3([0,0,0],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.keepPoints=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}})}};class j$ extends gG{constructor(){super(...arguments),this.paramsConfig=H$,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new L$(this),this.byExpressionHelper=new D$(this),this.byAttributeHelper=new F$(this),this.byObjectTypeHelper=new B$(this),this.byBboxHelper=new k$(this),this.byBoundingObjectHelper=new V$(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Pr.VERTEX:await this._eval_for_points(e,n);break;case Pr.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,r=[];for(let t=0;t0){const e=ps.geometryFromPoints(a,Nr(t.constructor));e&&(t.geometry=e,r.push(t))}}}this.setObjects(r)}_point_object(t){const e=t.points(),n=ps.geometryFromPoints(e,Sr.POINTS);if(n)return this.createObject(n,Sr.POINTS)}}const W$=new class extends aa{constructor(){super(...arguments),this.start=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=oa.BOOLEAN(0),this.count=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class q$ extends gG{constructor(){super(...arguments),this.paramsConfig=W$}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class X${constructor(){this.pluginCallbacks=[],this.register((function(t){return new bJ(t)})),this.register((function(t){return new wJ(t)})),this.register((function(t){return new TJ(t)})),this.register((function(t){return new AJ(t)})),this.register((function(t){return new EJ(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new xJ,r=[];for(let t=0,e=this.pluginCallbacks.length;t0&&(this.options.trs=!0),this.processInput(t);const i=this;Promise.all(this.pending).then((function(){const t=i.buffers,n=i.json,r=i.options,s=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(s);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===r.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=vJ(t.result),r=new DataView(new ArrayBuffer(8));r.setUint32(0,i.byteLength,!0),r.setUint32(4,5130562,!0);const s=vJ(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let 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i=this,r=i.cache,s=i.json,o=i.options,a=i.pending;r.images.has(t)||r.images.set(t,{});const l=r.images.get(t),c=e===By?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",u=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[u])return l[u];s.images||(s.images=[]);const h={mimeType:c};if(o.embedImages){const r=yJ=yJ||document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.min(t.width,o.maxTextureSize),r.height=Math.min(t.height,o.maxTextureSize);const s=r.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(s.translate(0,r.height),s.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)s.drawImage(t,0,0,r.width,r.height);else{e!==By&&e!==ky&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===By)for(let e=0;e1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const s=n.materials.push(i)-1;return e.materials.set(t,s),s}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e0){const n=[],i=[],r={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)r[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===s.groups.length)return null;const m=_?t.material:[t.material],f=_?s.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=u),null!==s.index){let i=this.getUID(s.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(s.index,s,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(r,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:mx.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const r=(t=X$.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,s=[],o=[];for(let t=0;t0){const e=[];for(let i=0,r=t.children.length;i0&&(r.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,r)}));const s=e.nodes.push(r)-1;return i.set(t,s),s}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const r=[];for(let e=0,i=t.children.length;e0&&(i.nodes=r),this.serializeUserData(t,i)}processObjects(t){const e=new UE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(s.range=t.distance)):t.isSpotLight&&(s.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(s.range=t.distance),s.spot={},s.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,s.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),r[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},r[this.name]=!0);const o=i.extensions[this.name].lights;o.push(s),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class wJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class TJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,r={};e.pbrMetallicRoughness.baseColorFactor&&(r.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const s=[1,1,1];if(t.specular.toArray(s,0),r.specularFactor=s,r.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(r.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),r.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class AJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),r.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class EJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),r.thicknessTexture=e}r.attenuationDistance=t.attenuationDistance,r.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}function MJ(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}X$.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),r=new t.TimeBufferType(t.times.length+1),s=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){r[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){r.set(t.times.slice(0,l+1),0),r[l+1]=e,r.set(t.times.slice(l+1),l+2),s.set(t.values.slice(0,(l+1)*i),0),s.set(o.evaluate(e),(l+1)*i),s.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=r,t.values=s,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},r=t.tracks;for(let t=0;t{CJ.PARAM_CALLBACK_export(t)}})}};class CJ extends gG{constructor(){super(...arguments),this.paramsConfig=SJ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new fr;for(let t of n)i.add(t);(new X$).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",MJ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){MJ(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const NJ=new class extends aa{constructor(){super(...arguments),this.makeFacesUnique=oa.BOOLEAN(0),this.addFaceCenterAttribute=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=oa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class LJ extends gG{constructor(){super(...arguments),this.paramsConfig=NJ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),r=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],s=n.itemSize,o=new Float32Array(r*s);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e{const a=t.object(),l=t.coreGeometry();if(a.isMesh&&l){i=l.faces(),l.hasAttrib(e)||l.addNumericAttrib(e,3,-1);for(let t=0;t{const n=t.object(),i=t.coreGeometry();if(n.isMesh&&i){const t=i.faces();i.hasAttrib(e)||i.addNumericAttrib(e,1,-1);for(let n=0;n{const c=t.object(),u=t.coreGeometry();if(c.isMesh&&u){s=u.faces(),u.hasAttrib(e)||u.addNumericAttrib(e,3,-1);for(let t=0;t{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==OJ.JSON&&this._options.format==OJ.AUTO)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new aY(this.loadingManager).parse(n,(e=>{IJ.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{IJ.decrement_in_progress_loads_count(),e(t)}));else{const r=await this._loaderForFormat();if(r)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),r.load(n,(e=>{this.on_load_success(e).then((e=>{IJ.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{ai.warn(\\\\\\\"error loading\\\\\\\",n,t),IJ.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==OJ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case PJ.PDB:return this.on_load_succes_pdb(t);case PJ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case PJ.DRC:return this.on_load_succes_drc(i);default:return[new k.a(i)]}const r=t;if(null!=r.scene)switch(e){case PJ.GLTF:case PJ.GLB:return this.on_load_succes_gltf(r);default:return[n]}const s=t;if(s.geometryAtoms||s.geometryBonds)switch(e){case PJ.PDB:return this.on_load_succes_pdb(s);default:return[]}return[]}on_load_succes_drc(t){return[new k.a(t,IJ._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new gr.a(t.geometryAtoms,IJ._default_mat_point),new Tr.a(t.geometryBonds,IJ._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case OJ.AUTO:return this.moduleNamesFromExt(e);case OJ.DRC:return[Vn.DRACOLoader];case OJ.FBX:return[Vn.FBXLoader];case OJ.JSON:return[];case OJ.GLTF:return[Vn.GLTFLoader];case OJ.GLTF_WITH_DRACO:return[Vn.GLTFLoader,Vn.DRACOLoader];case OJ.OBJ:return[Vn.OBJLoader];case OJ.PDB:return[Vn.PDBLoader];case OJ.PLY:return[Vn.PLYLoader];case OJ.STL:return[Vn.STLLoader]}ar.unreachable(t)}static moduleNamesFromExt(t){switch(t){case PJ.DRC:return[Vn.DRACOLoader];case PJ.FBX:return[Vn.FBXLoader];case PJ.GLTF:return[Vn.GLTFLoader];case PJ.GLB:return[Vn.GLTFLoader,Vn.DRACOLoader];case PJ.OBJ:return[Vn.OBJLoader];case PJ.PDB:return[Vn.PDBLoader];case PJ.PLY:return[Vn.PLYLoader];case PJ.STL:return[Vn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case OJ.AUTO:return this._loaderForExt();case OJ.DRC:return this.loader_for_drc(this._node);case OJ.FBX:return this.loader_for_fbx();case OJ.JSON:return;case OJ.GLTF:return this.loader_for_gltf();case OJ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case OJ.OBJ:return this.loader_for_obj();case OJ.PDB:return this.loader_for_pdb();case OJ.PLY:return this.loader_for_ply();case OJ.STL:return this.loader_for_stl()}ar.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case PJ.DRC:return this.loader_for_drc(this._node);case PJ.FBX:return this.loader_for_fbx();case PJ.GLTF:return this.loader_for_gltf();case PJ.GLB:return this.loader_for_glb(this._node);case PJ.OBJ:return this.loader_for_obj();case PJ.PDB:return this.loader_for_pdb();case PJ.PLY:return this.loader_for_ply();case PJ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=ai.modulesRegister.module(Vn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=ai.modulesRegister.module(Vn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=ai.modulesRegister.module(Vn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.DRACOPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return IJ.loader_for_drc(t)}static async loader_for_glb(t){const e=ai.modulesRegister.module(Vn.GLTFLoader),n=ai.modulesRegister.module(Vn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=ai.libs.root(),r=ai.libs.DRACOGLTFPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return IJ.loader_for_glb(t)}loader_for_obj(){const t=ai.modulesRegister.module(Vn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=ai.modulesRegister.module(Vn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=ai.modulesRegister.module(Vn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=ai.modulesRegister.module(Vn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}IJ._default_mat_mesh=new br.a,IJ._default_mat_point=new yr.a,IJ._default_mat_line=new wr.a,IJ.MAX_CONCURRENT_LOADS_COUNT=IJ._init_max_concurrent_loads_count(),IJ.CONCURRENT_LOADS_DELAY=IJ._init_concurrent_loads_delay(),IJ.in_progress_loads_count=0,IJ._queue=[];const FJ=`${Gg}/models/wolf.obj`;class DJ extends pG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new IJ({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}DJ.DEFAULT_PARAMS={url:FJ,format:OJ.AUTO};const kJ=DJ.DEFAULT_PARAMS;const BJ=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(kJ.url,{fileBrowse:{type:[Ls.GEOMETRY]}}),this.format=oa.STRING(kJ.format,{menuString:{entries:RJ.map((t=>({name:t,value:t})))}}),this.reload=oa.BUTTON(null,{callback:t=>{zJ.PARAM_CALLBACK_reload(t)}})}};class zJ extends gG{constructor(){super(...arguments),this.paramsConfig=BJ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return IJ.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new DJ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const UJ=new class extends aa{constructor(){super(...arguments),this.dist=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class GJ extends gG{constructor(){super(...arguments),this.paramsConfig=UJ}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,r={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");r[n]=r[n]||[],r[n].push(t)}const s=[];if(Object.keys(r).forEach((t=>{s.push(r[t][0])})),e.geometry.dispose(),s.length>0){const t=ps.geometryFromPoints(s,Nr(e.constructor));return t&&(e.geometry=t),e}}}class VJ{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,r=Math.floor(this._param_size.x/n),s=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==s||s==r-1?0==s?a.push([s+1+(t-1)*r,s+(t-1)*r,s+t*r]):a.push([s+t*r,s+(t-1)*r,s-1+t*r]):(a.push([s+t*r,s+(t-1)*r,s-1+t*r]),a.push([s+t*r,s+1+(t-1)*r,s+(t-1)*r])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const HJ=new p.a(0,1,0);const jJ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2([1,1]),this.hexagonRadius=oa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=oa.VECTOR3([0,1,0]),this.pointsOnly=oa.BOOLEAN(0)}};class WJ extends gG{constructor(){super(...arguments),this.paramsConfig=jJ,this._core_transform=new Mz}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new VJ(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,HJ,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Sr.POINTS):this.setGeometry(t)}else this.setObjects([])}}var qJ;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(qJ||(qJ={}));const XJ=[qJ.ADD_PARENT,qJ.REMOVE_PARENT,qJ.ADD_CHILD];class YJ extends pG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=XJ[e.mode];switch(i){case qJ.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ar.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new In.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=oa.INTEGER(JJ.levels,{range:[0,5],visibleIf:[{mode:XJ.indexOf(qJ.ADD_PARENT)},{mode:XJ.indexOf(qJ.REMOVE_PARENT)}]}),this.objectMask=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}}),this.debugObjectMask=oa.BOOLEAN(0,{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}})}};class QJ extends gG{constructor(){super(...arguments),this.paramsConfig=ZJ}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(YJ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=XJ[this.pv.mode];return $J.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new YJ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const KJ=new class extends aa{constructor(){super(...arguments),this.texture=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP}}),this.mult=oa.FLOAT(1)}};class tZ extends gG{constructor(){super(...arguments),this.paramsConfig=KJ}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==Ki.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:r,resx:s,resy:o}=i,a=r.length/(s*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,u=l.getAttribute(\\\\\\\"uv\\\\\\\"),h=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=u.array,p=h.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((s-1)*f),y=Math.floor((o-1)*(1-g)),x=y*s+v,b=r[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Rf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function eZ(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class nZ extends S.a{constructor(t,e,n,i,r){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const s=[],o=[],a=new Map;function l(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const o=t.clone().lerp(n,i/r),a=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?o:o.clone().lerp(a,t/l)}for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const xZ=new class extends aa{constructor(){super(...arguments),this.length=oa.FLOAT(1,{range:[0,10]}),this.pointsCount=oa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,1,0])}};class bZ extends gG{constructor(){super(...arguments),this.paramsConfig=xZ}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r)}const r=new S.a;r.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),r.setIndex(n),this.setGeometry(r,Sr.LINE_SEGMENTS)}}const wZ=new class extends aa{constructor(){super(...arguments),this.distance0=oa.FLOAT(1),this.distance1=oa.FLOAT(2),this.autoUpdate=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{TZ.PARAM_CALLBACK_update(t)}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class TZ extends gG{constructor(){super(...arguments),this.paramsConfig=wZ,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}_create_LOD(){const t=new Mr;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${s.states.error.message()}')`)}else null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!vs.isInGroup(n.group,t))return;const i=n.cloneMat?fs.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const r=t;this._old_mat_by_old_new_id.set(i.uuid,r.material),r.material=i,fs.apply_render_hook(t,i),fs.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}AZ.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new vi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},AZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const EZ=AZ.DEFAULT_PARAMS;const MZ=new class extends aa{constructor(){super(...arguments),this.group=oa.STRING(EZ.group),this.assignMat=oa.BOOLEAN(EZ.assignMat),this.material=oa.NODE_PATH(EZ.material.path(),{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=oa.BOOLEAN(EZ.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=oa.BOOLEAN(EZ.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=oa.BOOLEAN(EZ.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=oa.BOOLEAN(EZ.swapCurrentTex),this.texSrc0=oa.STRING(EZ.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=oa.STRING(EZ.texDest0,{visibleIf:{swapCurrentTex:1}})}};class SZ extends gG{constructor(){super(...arguments),this.paramsConfig=MZ}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(AZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new AZ(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CZ=n(88);class NZ{static sleep(t){return new Promise(((e,n)=>{setTimeout((()=>{e()}),t)}))}}const LZ=[Lg.VIDEO,Lg.WEB_CAM];const OZ=new class extends aa{constructor(){super(...arguments),this.webcam=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.COP,types:LZ}}),this.scale=oa.FLOAT(2,{range:[0,2],rangeLocked:[!0,!1]}),this.selfieMode=oa.BOOLEAN(0),this.minDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.maxDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.frames=oa.INTEGER(1,{range:[1,100],rangeLocked:[!0,!1]}),this.fps=oa.INTEGER(24,{range:[1,60],rangeLocked:[!0,!1]}),this.capture=oa.BUTTON(null,{callback:t=>{RZ.PARAM_CALLBACK_capture(t)}}),this.showAllMeshes=oa.BOOLEAN(1)}};class RZ extends gG{constructor(){super(...arguments),this.paramsConfig=OZ,this._faceMesh=this._createFaceMesh(),this._currentCapturedFrame=9999999,this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._faceMeshObjects=[]}static type(){return\\\\\\\"mediapipeFaceMesh\\\\\\\"}initializeNode(){this._forceTimeDependent()}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.scene(),\\\\\\\"facemesh_update_object\\\\\\\"),this._graph_node.addGraphInput(this.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this.setDirty.bind(this)))}async cook(){if(this.pv.showAllMeshes)this.setObjects(this._faceMeshObjects);else{const t=this.scene().frame()%this._faceMeshObjects.length,e=this._faceMeshObjects[t];e?this.setObject(e):this.setObjects([])}}_createFaceMesh(){const t=new CZ.FaceMesh({locateFile:t=>`https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4/${t}`});return t.onResults(this._onResults.bind(this)),t}async _getHTMLVideoElement(){const t=this.pv.webcam.nodeWithContext(Ki.COP);if(!t)return this.states.error.set(\\\\\\\"node is not a COP node\\\\\\\"),void this.cookController.endCook();if(!LZ.includes(t.type()))return this.states.error.set(`node type '${t.type()}' is not accepted by MediapipeFaceMesh (${LZ.join(\\\\\\\", \\\\\\\")})`),void this.cookController.endCook();const e=t;await e.compute();const n=e.HTMLVideoElement();if(n)return{videoElement:n};console.log(\\\\\\\"no video element found\\\\\\\")}_videoSnapshotCanvas(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.videoWidth,e.height=t.videoHeight;return e.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,e.width,e.height),e}static _canvasToTexture(t,e){return new Promise((n=>{const i=t.toDataURL(\\\\\\\"image/png\\\\\\\"),r=new Image;console.log(`start ${e}`),r.onload=()=>{const t=new J.a(r);t.name=`facemesh-texture-${e}`,t.encoding=w.ld,t.needsUpdate=!0,console.log(`done ${e}`),n({texture:t,image:r})},r.src=i}))}async _initActiveHTMLVideoElement(){const t=await this._getHTMLVideoElement();if(!t)return;const{videoElement:e}=t;for(;e.paused;)console.log(\\\\\\\"video is paused\\\\\\\"),await NZ.sleep(500);this._activeHTMLVideoElement=e}_captureAllowed(){return this._currentCapturedFrame{this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._captureSingleWebCamSnapshot(t)}))}async _captureSingleWebCamSnapshot(t){if(this._activeHTMLVideoElement)if(this._captureAllowed()){const e=this._videoSnapshotCanvas(this._activeHTMLVideoElement);this._webcamSnapshotCanvases.push(e),this._currentCapturedFrame++,console.log(\\\\\\\"capture\\\\\\\",this._currentCapturedFrame),setTimeout((()=>{this._captureSingleWebCamSnapshot(t)}),1e3/this.pv.fps)}else{console.log(\\\\\\\"converting snapshots to images and textures...\\\\\\\");let e=0;for(let t of this._webcamSnapshotCanvases){const n=await RZ._canvasToTexture(t,e);this._webcamSnapshots.push(n),e++}t()}else console.log(\\\\\\\"no video found\\\\\\\")}_initFaceMeshObjects(){this._faceMeshObjects=[];const t=this.pv.frames;for(let e=0;e{this._onSendToFaceMeshSingleResolve=e,this._faceMesh.send({image:t.image})}))}_onResults(t){if(!this._captureAllowed())return;console.log(this._currentCapturedFrame);const e=t.multiFaceLandmarks[0];if(!e)return console.error(`no landmark found (${this._currentCapturedFrame})`),void console.log(\\\\\\\"results\\\\\\\",t);const n=this._faceMeshObjects[this._currentCapturedFrame];let i=0;const r=n.geometry.getAttribute(\\\\\\\"position\\\\\\\"),s=n.geometry.getAttribute(\\\\\\\"uv\\\\\\\"),o=r.array,a=s.array,l=this.pv.scale;for(let t of e)o[3*i+0]=(1-t.x)*l,o[3*i+1]=(1-t.y)*l,o[3*i+2]=t.z*l,a[2*i+0]=t.x,a[2*i+1]=1-t.y,i++;r.needsUpdate=!0,s.needsUpdate=!0,this._updateMaterial(this._currentCapturedFrame),this._currentCapturedFrame++,this._onSendToFaceMeshSingleResolve&&this._onSendToFaceMeshSingleResolve()}_updateMaterial(t){const e=this._faceMeshObjects[t].material;if(!m.isArray(e)){const n=e,i=this._webcamSnapshots[t].texture;n.map=i,n.needsUpdate=!0}}_createFaceMeshObject(t){const e=new S.a,n=[],i=[];for(let t=0;t<468;t++)n.push(t),n.push(t),n.push(t),i.push(t),i.push(t);const r=[],s=CZ.FACEMESH_TESSELATION.length/3;for(let t=0;t{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._make_compact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_make_compact(t){const e=new Map,n=new Map,i=[];for(let r of t)r.traverse((t=>{if(t instanceof In.a)return;const r=t;if(r.geometry){const t=Nr(r.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,r.material),u.pushOnArrayAtEntry(n,t,r)}}}));const r=[];return i.forEach((t=>{var i,s;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const s=ps.mergeGeometries(n);if(s){const n=e.get(t),i=this.createObject(s,t,n);r.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(s=this.states)||void 0===s||s.error.set(t.message)}}})),r}}PZ.DEFAULT_PARAMS={compact:!1},PZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const IZ=\\\\\\\"geometry to merge\\\\\\\",FZ=PZ.DEFAULT_PARAMS;const DZ=new class extends aa{constructor(){super(...arguments),this.compact=oa.BOOLEAN(FZ.compact),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{kZ.PARAM_CALLBACK_setInputsCount(t)}})}};class kZ extends gG{constructor(){super(...arguments),this.paramsConfig=DZ}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[IZ,IZ,IZ,IZ]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(PZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new PZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}class BZ{constructor(t=Math){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.grad4=[[0,1,1,1],[0,1,1,-1],[0,1,-1,1],[0,1,-1,-1],[0,-1,1,1],[0,-1,1,-1],[0,-1,-1,1],[0,-1,-1,-1],[1,0,1,1],[1,0,1,-1],[1,0,-1,1],[1,0,-1,-1],[-1,0,1,1],[-1,0,1,-1],[-1,0,-1,1],[-1,0,-1,-1],[1,1,0,1],[1,1,0,-1],[1,-1,0,1],[1,-1,0,-1],[-1,1,0,1],[-1,1,0,-1],[-1,-1,0,1],[-1,-1,0,-1],[1,1,1,0],[1,1,-1,0],[1,-1,1,0],[1,-1,-1,0],[-1,1,1,0],[-1,1,-1,0],[-1,-1,1,0],[-1,-1,-1,0]],this.p=[];for(let e=0;e<256;e++)this.p[e]=Math.floor(256*t.random());this.perm=[];for(let t=0;t<512;t++)this.perm[t]=this.p[255&t];this.simplex=[[0,1,2,3],[0,1,3,2],[0,0,0,0],[0,2,3,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,3,0],[0,2,1,3],[0,0,0,0],[0,3,1,2],[0,3,2,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,3,2,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,0,3],[0,0,0,0],[1,3,0,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,3,0,1],[2,3,1,0],[1,0,2,3],[1,0,3,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,3,1],[0,0,0,0],[2,1,3,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,1,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,0,1,2],[3,0,2,1],[0,0,0,0],[3,1,2,0],[2,1,0,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,1,0,2],[0,0,0,0],[3,2,0,1],[3,2,1,0]]}dot(t,e,n){return t[0]*e+t[1]*n}dot3(t,e,n,i){return t[0]*e+t[1]*n+t[2]*i}dot4(t,e,n,i,r){return t[0]*e+t[1]*n+t[2]*i+t[3]*r}noise(t,e){let n,i,r;const s=(t+e)*(.5*(Math.sqrt(3)-1)),o=Math.floor(t+s),a=Math.floor(e+s),l=(3-Math.sqrt(3))/6,c=(o+a)*l,u=t-(o-c),h=e-(a-c);let d,p;u>h?(d=1,p=0):(d=0,p=1);const _=u-d+l,m=h-p+l,f=u-1+2*l,g=h-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-u*u-h*h;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],u,h));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let E=.5-f*f-g*g;return E<0?r=0:(E*=E,r=E*E*this.dot(this.grad3[w],f,g)),70*(n+i+r)}noise3d(t,e,n){let i,r,s,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),u=Math.floor(n+a),h=1/6,d=(l+c+u)*h,p=t-(l-d),_=e-(c-d),m=n-(u-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),E=s[A][0]>=3?1:0,M=s[A][1]>=3?1:0,S=s[A][2]>=3?1:0,C=s[A][3]>=3?1:0,N=s[A][0]>=2?1:0,L=s[A][1]>=2?1:0,O=s[A][2]>=2?1:0,R=s[A][3]>=2?1:0,P=s[A][0]>=1?1:0,I=s[A][1]>=1?1:0,F=s[A][2]>=1?1:0,D=s[A][3]>=1?1:0,k=x-E+l,B=b-M+l,z=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-R+2*l,W=x-P+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,rt=o[K+E+o[tt+M+o[et+S+o[nt+C]]]]%32,st=o[K+N+o[tt+L+o[et+O+o[nt+R]]]]%32,ot=o[K+P+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(r[it],x,b,w,T));let ct=.6-k*k-B*B-z*z-U*U;ct<0?u=0:(ct*=ct,u=ct*ct*this.dot4(r[rt],k,B,z,U));let ut=.6-G*G-V*V-H*H-j*j;ut<0?h=0:(ut*=ut,h=ut*ut*this.dot4(r[st],G,V,H,j));let ht=.6-W*W-q*q-X*X-Y*Y;ht<0?d=0:(ht*=ht,d=ht*ht*this.dot4(r[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(r[at],$,J,Z,Q)),27*(c+u+h+d+p)}}var zZ;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(zZ||(zZ={}));const UZ=[zZ.ADD,zZ.SET,zZ.MULT,zZ.SUBSTRACT,zZ.DIVIDE];const GZ=new class extends aa{constructor(){super(...arguments),this.amplitude=oa.FLOAT(1),this.tamplitudeAttrib=oa.BOOLEAN(0),this.amplitudeAttrib=oa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=oa.VECTOR3([1,1,1]),this.offset=oa.VECTOR3([0,0,0]),this.octaves=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10]}),this.seed=oa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=oa.BOOLEAN(0),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=oa.BOOLEAN(0),this.restP=oa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=oa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=oa.INTEGER(UZ.indexOf(zZ.ADD),{menu:{entries:UZ.map((t=>({name:t,value:UZ.indexOf(t)})))}}),this.computeNormals=oa.BOOLEAN(1)}};class VZ extends gG{constructor(){super(...arguments),this.paramsConfig=GZ,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}setOperation(t){this.p.operation.set(UZ.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=Dr.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const r=this._get_simplex(),s=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=UZ[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,u=this.pv.tamplitudeAttrib;let h,f,g=new p.a;for(let t=0;t{r[3*t.index()+0]=e}));else{let t;for(let e=0;e{r[3*t.index()+1]=e}));else{let t;for(let e=0;e{r[3*t.index()+2]=e}));else{let t;for(let e=0;e{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}JZ.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},JZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const ZZ=JZ.DEFAULT_PARAMS;const QZ=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(ZZ.applyToChildren,{separatorAfter:!0}),this.tname=oa.BOOLEAN(ZZ.tname),this.name=oa.STRING(ZZ.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=oa.BOOLEAN(ZZ.trenderOrder),this.renderOrder=oa.INTEGER(ZZ.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=oa.BOOLEAN(ZZ.tfrustumCulled),this.frustumCulled=oa.BOOLEAN(ZZ.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=oa.BOOLEAN(ZZ.tmatrixAutoUpdate),this.matrixAutoUpdate=oa.BOOLEAN(ZZ.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=oa.BOOLEAN(ZZ.tvisible),this.visible=oa.BOOLEAN(ZZ.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=oa.BOOLEAN(ZZ.tcastShadow),this.castShadow=oa.BOOLEAN(ZZ.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=oa.BOOLEAN(ZZ.treceiveShadow),this.receiveShadow=oa.BOOLEAN(ZZ.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class KZ extends gG{constructor(){super(...arguments),this.paramsConfig=QZ}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(JZ.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new JZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const tQ=new class extends aa{};class eQ extends gG{constructor(){super(...arguments),this.paramsConfig=tQ,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class nQ extends cf{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),r=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const s=i.type(),o=`${s} ${r} = ${this.read_attribute(t,s,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Sf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const r=this._texture_allocations_controller.variable(n);if(!r)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=r.component(),n=r.allocation();if(n){const r=n.textureName(),s=new Af(t,Do.SAMPLER_2D,r);i.addDefinitions(t,[s]);return`texture2D( ${r}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Do.VEC2,nQ.UV_ATTRIB),i=new Ef(t,Do.VEC2,nQ.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${nQ.UV_VARYING} = ${nQ.UV_ATTRIB}`],xf.VERTEX)}}nQ.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",nQ.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",nQ.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class iQ{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new nQ(nQ.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,fs.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),t>this._last_simulated_frame&&this._compute_simulation(t-this._last_simulated_frame))}_compute_simulation(t=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();for(let e=0;et)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await ai.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new sQ(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var r=this._gpu_compute.init();null!==r&&(console.error(r),this.node.states.error.set(r))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[RR.TIME]={value:this.node.scene().time()},t.uniforms[RR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const r=t.param_uniform_pairs;for(let t of r){const r=t[0],s=t[1],o=this.node.params.get(r),a=e[s];for(let t of n)t.uniforms[s]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[s])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[RR.TIME].value=this.node.scene().time(),t.material.uniforms[RR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));r++);const s=nQ.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?cX:C.a;n.setAttribute(s,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const r=i.variables();if(!r)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const s=e.image.data;for(let t of r){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let r=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);s[r]=t}else e.attribValue(n).toArray(s,r);r+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const r=[];for(let t of i)for(let e of t.points())r.push(e);return r}return[]}}class cQ{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new cQ(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const r=t.graph.nodeFromId(i);if(r){const t=r.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class uQ{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ir.ATTRIBUTE?[gf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new uQ;for(let n of t){const t=cQ.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const hQ=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class dQ{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==UI.type())),n=t.filter((t=>t.type()!=UI.type())),i=n.map((t=>t.name())).sort(),r=new Map;for(let t of n)r.set(t.name(),t);for(let t of i){const n=r.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=dQ._sortNodes(t),e=dQ._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case UI.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new cQ(i.name(),Go[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,r=i.connected_input_node(),s=i.connected_input_connection_point();if(r&&s){const e=new cQ(i.attribute_name,Go[s.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case HP.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(hQ.includes(t)){const r=i.io.outputs.namedOutputConnectionPointsByName(t);if(r){const i=r.type(),s=new cQ(t,Go[i]);s.addGraphNodeId(e),n.push(s)}}}break}case gf.type():{const i=t,r=i.output_connection_point();if(r){const t=new cQ(i.attribute_name,Go[r.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)u.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;et.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new dQ;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class pQ extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],r=new F,s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]),r.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(r,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){ai.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return dQ.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const _Q=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.FLOAT(Ml.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=oa.BOOLEAN(1),this.maxTexturesSize=oa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=oa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=oa.INTEGER(0,{menu:{entries:oQ.map(((t,e)=>({value:e,name:t})))}}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{mQ.PARAM_CALLBACK_reset(t)}}),this.material=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1})}};class mQ extends gG{constructor(){super(...arguments),this.paramsConfig=_Q,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new pQ(this),this.globals_handler=new nQ(nQ.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new lQ(this),this.renderController=new iQ(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=Ki.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return Hn.GL_PARTICLES}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.reset_gpu_compute_and_set_dirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.reset_gpu_compute(),this.gpuController.reset_particle_groups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class fQ extends pG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,r;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(r of(i=new ps(n),i.points())){const t=r.normal(),n=r.position().clone().add(t.multiplyScalar(e.amount));r.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}fQ.DEFAULT_PARAMS={amount:0};const gQ=fQ.DEFAULT_PARAMS;const vQ=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(gQ.amount,{range:[-1,1]})}};class yQ extends gG{constructor(){super(...arguments),this.paramsConfig=vQ}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){this._operation=this._operation||new fQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const xQ=new p.a(0,0,1),bQ=new p.a(0,0,1),wQ=new p.a(0,1,0);class TQ extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,xQ,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),r=this._create_plane(i,e);this._core_transform.rotate_geometry(r,bQ,wQ);const s=this._core_transform.translation_matrix(this._center);return r.applyMatrix4(s),this.createCoreGroupFromGeometry(r)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}TQ.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},TQ.INPUT_CLONED_STATE=Qi.NEVER;const AQ=TQ.DEFAULT_PARAMS;const EQ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2(AQ.size),this.useSegmentsCount=oa.BOOLEAN(AQ.useSegmentsCount),this.stepSize=oa.FLOAT(AQ.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2(AQ.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=oa.VECTOR3(AQ.direction),this.center=oa.VECTOR3(AQ.center)}};class MQ extends gG{constructor(){super(...arguments),this.paramsConfig=EQ}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(TQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new TQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const SQ=\\\\\\\"position\\\\\\\";const CQ=new class extends aa{constructor(){super(...arguments),this.updateX=oa.BOOLEAN(0),this.x=oa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=oa.BOOLEAN(0),this.y=oa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=oa.BOOLEAN(0),this.z=oa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=oa.BOOLEAN(1)}};class NQ extends gG{constructor(){super(...arguments),this.paramsConfig=CQ,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t{u[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=oa.VECTOR3(kQ.center.toArray()),this.longitude=oa.FLOAT(kQ.longitude,{range:[0,360]}),this.latitude=oa.FLOAT(kQ.latitude,{range:[-180,180]}),this.depth=oa.FLOAT(kQ.depth,{range:[0,10]})}};class zQ extends gG{constructor(){super(...arguments),this.paramsConfig=BQ}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(DQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new DQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class UQ{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,r=null;for(let s=0;s{s[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{s[t].push(e)}))})),o>0&&(r.push(o-1),r.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=s[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(r),o}static line_segment_to_geometries(t){var e;const n=[],i=new ps(t),r=i.attribNames(),s=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(s,i,r,e),n.push(t)}))}return n}}class GQ{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new ps(this.geometry0),e=new ps(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const r=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+h),p.push(t[1]),p.push(n[1]+h),p.push(n[0]+h)}));f.intersection(s.attribNames(),o.attribNames()).forEach((t=>{const e=s.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const VQ=new p.a(0,0,0),HQ=new p.a(1,1,1);const jQ=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.segmentsRadial=oa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=oa.BOOLEAN(0)}};class WQ extends gG{constructor(){super(...arguments),this.paramsConfig=jQ,this._core_transform=new Mz,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof Tr.a&&this._create_tube(t);const n=ps.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Sr.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new ps(n).points(),r=null===(e=n.getIndex())||void 0===e?void 0:e.array,s=UQ.accumulated_curve_point_indices(r);for(let t of s){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=wY.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,r=this._core_transform.matrix(e,VQ,HQ,1,Ez),s=n.clone();s.applyMatrix4(r),i.push(s)}for(let t=0;t0){const e=i[t],n=i[t-1],r=this._skin(n,e);this._geometries.push(r)}}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}const qQ=\\\\\\\"dist\\\\\\\";class XQ extends pG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new z$,this._raycaster=new uL,this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,r;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(qQ)||t.addNumericVertexAttrib(qQ,1,-1));const s=t.points();for(let t of s)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),r=this._raycaster.intersectObjects(e.objects(),!0)[0],r){if(n.transformPoints&&t.setPosition(r.point),n.addDistAttribute){const e=this._pointPos.distanceTo(r.point);console.log(e),t.setAttribValue(qQ,e)}n.transferFaceNormals&&r.face&&t.setNormal(r.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}XQ.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},XQ.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.ALWAYS];const YQ=XQ.DEFAULT_PARAMS;const $Q=new class extends aa{constructor(){super(...arguments),this.useNormals=oa.BOOLEAN(YQ.useNormals),this.direction=oa.VECTOR3(YQ.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=oa.BOOLEAN(YQ.transformPoints),this.transferFaceNormals=oa.BOOLEAN(YQ.transferFaceNormals),this.addDistAttribute=oa.BOOLEAN(YQ.addDistAttribute)}};class JQ extends gG{constructor(){super(...arguments),this.paramsConfig=$Q}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.ALWAYS])}cook(t){this._operation=this._operation||new XQ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const ZQ={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},QQ=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",KQ=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",tK={minFilter:w.V,magFilter:w.V,format:w.ic};class eK extends k.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new X.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new K.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Z(n,i,tK),Object(Ln.i)(n)&&Object(Ln.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new kU(new d.a(n,i)),this.material=new F({uniforms:I.clone(ZQ),fragmentShader:KQ,vertexShader:QQ}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,r,s){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,u=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=u,t.setRenderTarget(l);var h=o.viewport;void 0!==h&&t.state.viewport(h),this.visible=!0}}}class nK extends pG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],r=await ai.renderersController.firstRenderer();if(!r)return this.createCoreGroupFromObjects(i);const s=n.objectsWithGeo();for(let t of s){const n=new eK(t.geometry,{clipBias:e.clipBias,renderer:r,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}nK.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},nK.INPUT_CLONED_STATE=Qi.NEVER;const iK=nK.DEFAULT_PARAMS;const rK=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(iK.active),this.clipBias=oa.FLOAT(iK.clipBias),this.color=oa.COLOR(iK.color.toArray()),this.opacity=oa.FLOAT(iK.opacity),this.pixelRatio=oa.INTEGER(iK.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=oa.BOOLEAN(iK.tblur),this.blur=oa.FLOAT(iK.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=oa.FLOAT(iK.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=oa.BOOLEAN(iK.tblur2,{visibleIf:{tblur:1}}),this.blur2=oa.FLOAT(iK.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=oa.FLOAT(iK.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class sK extends gG{constructor(){super(...arguments),this.paramsConfig=rK}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(nK.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new nK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oK=n(85);var aK;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(aK||(aK={}));const lK=[aK.POINTS_COUNT,aK.SEGMENT_LENGTH];var cK;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(cK||(cK={}));const uK=[cK.CENTRIPETAL,cK.CHORDAL,cK.CATMULLROM];const hK=new class extends aa{constructor(){super(...arguments),this.method=oa.INTEGER(lK.indexOf(aK.POINTS_COUNT),{menu:{entries:lK.map(((t,e)=>({name:t,value:e})))}}),this.curveType=oa.INTEGER(uK.indexOf(cK.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:uK.map(((t,e)=>({name:t,value:e})))}}),this.tension=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=oa.INTEGER(100,{visibleIf:{method:lK.indexOf(aK.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=oa.FLOAT(1,{visibleIf:{method:lK.indexOf(aK.SEGMENT_LENGTH)}})}};class dK extends gG{constructor(){super(...arguments),this.paramsConfig=hK}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;ei[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=cs(o);return this.createObject(a,Sr.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=uK[this.pv.curveType],i=this.pv.tension,r=new oK.a(e,!1,n,i),s=this._get_points_from_curve(r);let o=[];const a=[];for(let t=0;t0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=lK[this.pv.method];switch(e){case aK.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case aK.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}ar.unreachable(e)}}class pK extends pG{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}pK.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const _K=pK.DEFAULT_PARAMS;const mK=new class extends aa{constructor(){super(...arguments),this.tposition=oa.BOOLEAN(_K.tposition),this.position=oa.STRING(_K.position,{visibleIf:{tposition:!0}}),this.restP=oa.STRING(_K.restP,{visibleIf:{tposition:!0}}),this.tnormal=oa.BOOLEAN(_K.tnormal),this.normal=oa.STRING(_K.normal,{visibleIf:{tnormal:!0}}),this.restN=oa.STRING(_K.restN,{visibleIf:{tnormal:!0}})}};class fK extends gG{constructor(){super(...arguments),this.paramsConfig=mK}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new pK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const gK=new p.a;function vK(t,e,n,i,r,s){const o=2*Math.PI*r/4,a=Math.max(s-2*r,0),l=Math.PI/4;gK.copy(e),gK[i]=0,gK.normalize();const c=.5*o/(o+a),u=1-gK.angleTo(t)/l;if(1===Math.sign(gK[n]))return u*c;return a/(o+a)+c+c*(1-u)}class yK extends N{constructor(t=1,e=1,n=1,i=2,r=.1){if(i=2*i+1,r=Math.min(t/2,e/2,n/2,r),super(1,1,1,i,i,i),1===i)return;const s=this.toNonIndexed();this.index=null,this.attributes.position=s.attributes.position,this.attributes.normal=s.attributes.normal,this.attributes.uv=s.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(r),c=this.attributes.position.array,u=this.attributes.normal.array,h=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,s=0;io.get(t.index())||-1));let l=0;for(let t of a)s+=o.get(t.index()),r[l]=s,l++;const c=[];let u=[];e.transferAttributes&&(u=n.attribNamesMatchingMask(e.attributesToTransfer));const h=new Map,d=new Map;for(let t of u)h.set(t,[]),d.set(t,n.attribSize(t));const p=new Iq,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rs.randFloat(_+t)*s;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Sr.POINTS);return this.createCoreGroupFromObjects([v])}}AK.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},AK.INPUT_CLONED_STATE=Qi.FROM_NODE;const EK=AK.DEFAULT_PARAMS;const MK=new class extends aa{constructor(){super(...arguments),this.pointsCount=oa.INTEGER(EK.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=oa.INTEGER(EK.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=oa.BOOLEAN(EK.transferAttributes),this.attributesToTransfer=oa.STRING(EK.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=oa.BOOLEAN(EK.addIdAttribute),this.addIdnAttribute=oa.BOOLEAN(EK.addIdnAttribute)}};class SK extends gG{constructor(){super(...arguments),this.paramsConfig=MK}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this._operation=this._operation||new AK(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CK;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(CK||(CK={}));const NK=[CK.MATRIX,CK.AXIS];var LK;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(LK||(LK={}));const OK=[LK.BBOX_CENTER,LK.BBOX_CENTER_OFFSET,LK.CUSTOM];class RK extends pG{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new X.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=NK[e.mode];switch(n){case CK.MATRIX:return this._applyMatrixShear(t,e);case CK.AXIS:return this._applyAxisShear(t,e)}ar.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new ps(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=OK[e.centerMode];switch(n){case LK.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case LK.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case LK.CUSTOM:return this._getAxisModeCenterCustom(e)}ar.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var PK;RK.DEFAULT_PARAMS={mode:NK.indexOf(CK.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:OK.indexOf(LK.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},RK.INPUT_CLONED_STATE=Qi.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(PK||(PK={}));const IK=RK.DEFAULT_PARAMS;const FK=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(IK.mode,{menu:{entries:NK.map(((t,e)=>({name:t,value:e})))}}),this.xy=oa.FLOAT(IK.xy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.xz=oa.FLOAT(IK.xz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yx=oa.FLOAT(IK.yx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yz=oa.FLOAT(IK.yz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zx=oa.FLOAT(IK.zx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zy=oa.FLOAT(IK.zy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.centerMode=oa.INTEGER(IK.centerMode,{visibleIf:{mode:NK.indexOf(CK.AXIS)},menu:{entries:OK.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=oa.VECTOR3(IK.centerOffset.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.BBOX_CENTER_OFFSET)}}),this.center=oa.VECTOR3(IK.center.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.CUSTOM)}}),this.planeAxis=oa.VECTOR3(IK.planeAxis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axis=oa.VECTOR3(IK.axis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axisAmount=oa.FLOAT(IK.axisAmount,{range:[-1,1],visibleIf:{mode:NK.indexOf(CK.AXIS)}})}};class DK extends gG{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return PK.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(RK.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set(NK.indexOf(t))}cook(t){this._operation=this._operation||new RK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const kK=new class extends aa{};class BK extends gG{constructor(){super(...arguments),this.paramsConfig=kK}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=UQ.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const r=e[n-1],s=this._skin(r,t);i.push(s)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),r=this._get_line_segments(n),s=f.sortBy([i,r],(t=>-t.length)),o=s[0],a=s[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,r=this._skin(e,i);l.push(r)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}var zK;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(zK||(zK={}));const UK=[zK.X,zK.Y,zK.Z];class GK extends pG{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new vs(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const r=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const s=UK[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),s){case zK.X:o=this._pointPos.x;break;case zK.Y:o=this._pointPos.y;break;case zK.Z:o=this._pointPos.z}this._positions[a]=o,u.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const h=f.uniq(l);for(let t of h){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(r.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=oa.BOOLEAN(VK.invert)}};class jK extends gG{constructor(){super(...arguments),this.paramsConfig=HK}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new GK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const WK=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.INTEGER(Ml.START_FRAME)}};class qK extends xG{constructor(){super(...arguments),this.paramsConfig=WK,this._last_simulated_frame=null,this.childrenDisplayController=new wG(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e({name:t,value:JK[t]})))}}),this.radius=oa.FLOAT(KK.radius,{visibleIf:{type:JK.default}}),this.resolution=oa.VECTOR2(KK.resolution,{visibleIf:{type:JK.default}}),this.open=oa.BOOLEAN(KK.open,{visibleIf:{type:JK.default}}),this.phiStart=oa.FLOAT(KK.phiStart,{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.phiLength=oa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaStart=oa.FLOAT(KK.thetaStart,{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaLength=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.detail=oa.INTEGER(KK.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:JK.isocahedron}}),this.center=oa.VECTOR3(KK.center)}};class e0 extends gG{constructor(){super(...arguments),this.paramsConfig=t0}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(QK.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new QK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const n0=new class extends aa{constructor(){super(...arguments),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\")}};class i0 extends gG{constructor(){super(...arguments),this.paramsConfig=n0,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const r=t.object(),s=e.pointsFromGeometry(),o=s[0];if(o){if(o.attribSize(n)!=zr.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of s)t=e.indexedAttribValue(n),u.pushOnArrayAtEntry(i,t,e);else for(let e of s)t=e.attribValue(n),u.pushOnArrayAtEntry(i,t,e)}const a=Nr(r.constructor);i.forEach(((t,e)=>{const i=ps.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);vs.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const r0=new A.a,s0=new Q.a,o0=new p.a;class a0 extends $.a{constructor(){super(),this.uuid=Ln.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new U.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e0)for(let t=0;t0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t=0;t--){const e=s[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,r),_=c(_,3,s),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),s){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(u(e.normal)),a){const t=e.vertexNormals;n.push(u(t[0]),u(t[1]),u(t[2]))}if(l&&n.push(h(e.color)),p){const t=e.vertexColors;n.push(h(t[0]),h(t[1]),h(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=s),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new a0).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],r=t.morphTargets[n];for(let t=0,e=r.length;t0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}a0.prototype.isGeometry=!0;class l0{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,r;const s=t.faces;for(i=0;i0,s=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){h=[];for(let t=0;t0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,r,s){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(r),s[t].edges.push(o),s[e].edges.push(o)}function i(t,e,n,i,r){t.push(new c0(e,n,i,void 0,void 0,r))}function r(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function s(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}u0.prototype.smooth=function(o){var a,l,c,u,h,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,E,M,S,C,N,L,O,R,P,I,F,D,k,B=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(B)for(var z=0;z3&&(O=3/(8*_)),R=1-_*O,P=O,_<=2&&2==_&&(R=3/4,P=1/8),k=D.clone().multiplyScalar(R),w.set(0,0,0),z=0;z<_;z++)A=(I=F[z]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(P),k.add(w),b.push(k)}u=b.concat(x);var U,G,V,H,j,W,q,X=b.length;h=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{v0.PARAM_CALLBACK_reset(t)}})}};class v0 extends gG{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return er.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.input_graph_node(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var y0=n(82);class x0 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new y0.a(this.loadingManager),r=await this._urlToLoad();i.load(r,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new y0.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new In.a;for(let t=0;t{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}w0.DEFAULT_PARAMS={url:b0,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const T0=w0.DEFAULT_PARAMS;const A0=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(T0.url,{fileBrowse:{type:[Ls.SVG]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{E0.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=oa.BOOLEAN(T0.drawFillShapes),this.fillShapesWireframe=oa.BOOLEAN(T0.fillShapesWireframe),this.drawStrokes=oa.BOOLEAN(T0.drawStrokes),this.strokesWireframe=oa.BOOLEAN(T0.strokesWireframe)}};class E0 extends gG{constructor(){super(...arguments),this.paramsConfig=A0}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Vn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new w0(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const M0=\\\\\\\"geometry to switch to\\\\\\\";const S0=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class C0 extends gG{constructor(){super(...arguments),this.paramsConfig=S0}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[M0,M0,M0,M0]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class N0 extends nZ{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const L0=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.detail=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=oa.BOOLEAN(0),this.center=oa.VECTOR3([0,0,0])}};class O0 extends gG{constructor(){super(...arguments),this.paramsConfig=L0}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new N0(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Sr.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class R0 extends YX{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var P0=n(48);class I0 extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new F0(t)}}class F0{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),r=e/n.resolution,s=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,o=[];let a=0,l=0;for(let t=0;t{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await ai.modulesRegister.module(Vn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await ai.modulesRegister.module(Vn.SVGLoader);if(t)return t}}var B0;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(B0||(B0={}));const z0=[B0.MESH,B0.FLAT,B0.LINE,B0.STROKE],U0=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const G0=new class extends aa{constructor(){super(...arguments),this.font=oa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Ls.FONT]}}),this.text=oa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=oa.INTEGER(0,{menu:{entries:z0.map(((t,e)=>({name:t,value:e})))}}),this.size=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=oa.FLOAT(.1,{visibleIf:{type:z0.indexOf(B0.MESH)}}),this.segments=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:z0.indexOf(B0.MESH)}}),this.strokeWidth=oa.FLOAT(.02,{visibleIf:{type:z0.indexOf(B0.STROKE)}})}};class V0 extends gG{constructor(){super(...arguments),this.paramsConfig=G0,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(z0[this.pv.type]){case B0.MESH:return this._create_geometry_from_type_mesh(t);case B0.FLAT:return this._create_geometry_from_type_flat(t);case B0.LINE:return this._create_geometry_from_type_line(t);case B0.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new R0(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(U0)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new KX(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let r=0;r0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||r||(r=n[e.textureName]))}));if(r)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=r;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=r)}n.needsUpdate=!0}}));return n}}H0.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},H0.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const j0=H0.DEFAULT_PARAMS;const W0=new class extends aa{constructor(){super(...arguments),this.textureName=oa.STRING(j0.textureName)}};class q0 extends gG{constructor(){super(...arguments),this.paramsConfig=W0}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(H0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new H0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class X0 extends pG{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const r=i.map((t=>this._update_object(t,e)));return await Promise.all(r),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await ai.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}X0.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Xm,tmagFilter:!1,magFilter:qm},X0.INPUT_CLONED_STATE=Qi.FROM_NODE;const Y0=X0.DEFAULT_PARAMS;const $0=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(Y0.applyToChildren,{separatorAfter:!0}),this.tencoding=oa.BOOLEAN(Y0.tencoding),this.encoding=oa.INTEGER(Y0.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=oa.BOOLEAN(Y0.tmapping),this.mapping=oa.INTEGER(Y0.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=oa.BOOLEAN(Y0.twrap),this.wrapS=oa.INTEGER(Y0.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=oa.INTEGER(Y0.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=oa.BOOLEAN(Y0.tanisotropy),this.useRendererMaxAnisotropy=oa.BOOLEAN(Y0.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=oa.INTEGER(Y0.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Y0.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(Y0.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class J0 extends gG{constructor(){super(...arguments),this.paramsConfig=$0}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(X0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new X0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Z0=new p.a(0,0,1);class Q0 extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=new eY(n,i,r,s);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,Z0,e.direction),this.createCoreGroupFromGeometry(o)}}Q0.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},Q0.INPUT_CLONED_STATE=Qi.FROM_NODE;const K0=Q0.DEFAULT_PARAMS;const t1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(K0.radius,{range:[0,1]}),this.radiusTube=oa.FLOAT(K0.radiusTube,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(K0.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=oa.INTEGER(K0.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=oa.VECTOR3(K0.direction),this.center=oa.VECTOR3(K0.center)}};class e1 extends gG{constructor(){super(...arguments),this.paramsConfig=t1}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new Q0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class n1 extends pG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=e.p,a=e.q,l=new nY(n,i,r,s,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}n1.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},n1.INPUT_CLONED_STATE=Qi.FROM_NODE;const i1=n1.DEFAULT_PARAMS;const r1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(i1.radius),this.radiusTube=oa.FLOAT(i1.radiusTube),this.segmentsRadial=oa.INTEGER(i1.segmentsRadial,{range:[1,128]}),this.segmentsTube=oa.INTEGER(i1.segmentsTube,{range:[1,32]}),this.p=oa.INTEGER(i1.p,{range:[1,10]}),this.q=oa.INTEGER(i1.q,{range:[1,10]}),this.center=oa.VECTOR3(i1.center)}};class s1 extends gG{constructor(){super(...arguments),this.paramsConfig=r1}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new n1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var o1;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(o1||(o1={}));const a1=[o1.SET_PARAMS,o1.UPDATE_MATRIX];class l1 extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=wz[e.applyOn];switch(n){case yz.GEOMETRIES:return this._update_geometries(t,e);case yz.OBJECTS:return this._update_objects(t,e)}ar.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=dG._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=a1[e.objectMode];switch(n){case o1.SET_PARAMS:return this._update_objects_params(t,e);case o1.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ar.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=Az[e.rotationOrder];this._r.copy(e.r).multiplyScalar(Ln.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,Az[t.rotationOrder])}}l1.DEFAULT_PARAMS={applyOn:wz.indexOf(yz.GEOMETRIES),objectMode:a1.indexOf(o1.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:Az.indexOf(Tz.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},l1.INPUT_CLONED_STATE=Qi.FROM_NODE;const c1=l1.DEFAULT_PARAMS;const u1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(c1.applyOn,{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=oa.INTEGER(c1.objectMode,{visibleIf:{applyOn:wz.indexOf(yz.OBJECTS)},menu:{entries:a1.map(((t,e)=>({name:t,value:e})))}}),this.group=oa.STRING(c1.group,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}}),this.rotationOrder=oa.INTEGER(c1.rotationOrder,{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3(c1.t),this.r=oa.VECTOR3(c1.r),this.s=oa.VECTOR3(c1.s),this.scale=oa.FLOAT(c1.scale,{range:[0,10]}),this.pivot=oa.VECTOR3(c1.pivot,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}})}};class h1 extends gG{constructor(){super(...arguments),this.paramsConfig=u1}static type(){return PK.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(l1.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(wz.indexOf(t))}setObjectMode(t){this.p.objectMode.set(a1.indexOf(t))}cook(t){this._operation=this._operation||new l1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const d1=new class extends aa{constructor(){super(...arguments),this.useSecondInput=oa.BOOLEAN(1),this.reference=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.SOP},visibleIf:{useSecondInput:0}})}};class p1 extends gG{constructor(){super(...arguments),this.paramsConfig=d1}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let r=0;r({name:t,value:e})))}};function f1(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const g1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(wz.indexOf(yz.GEOMETRIES),{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.count=oa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(0)}),this.r0=oa.VECTOR3([0,0,0],{...f1(0)}),this.rotationOrder1=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(1)}),this.r1=oa.VECTOR3([0,0,0],{...f1(1)}),this.rotationOrder2=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(2)}),this.r2=oa.VECTOR3([0,0,0],{...f1(2)}),this.rotationOrder3=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(3)}),this.r3=oa.VECTOR3([0,0,0],{...f1(3)}),this.rotationOrder4=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(4)}),this.r4=oa.VECTOR3([0,0,0],{...f1(4)}),this.rotationOrder5=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(5)}),this.r5=oa.VECTOR3([0,0,0],{...f1(5)})}};class v1 extends gG{constructor(){super(...arguments),this.paramsConfig=g1,this._core_transform=new Mz,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=wz[this.pv.applyOn];switch(n){case yz.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case yz.OBJECTS:return this._apply_matrix_to_objects(t,e)}ar.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[Hr.POSITION,Hr.NORMAL,Hr.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&Wr.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class w1 extends gG{constructor(){super(...arguments),this.paramsConfig=b1,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}setMode(t){this.p.mode.set(x1.indexOf(t))}cook(t){const e=x1[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case y1.RESET_OBJECT:return this._reset_objects(e);case y1.CENTER_GEO:return this._center_geos(e,!1);case y1.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ar.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),Mz.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let r=i;const s=t[1];s&&(r=s.objectsWithGeo());for(let t=0;te.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let r=0,s=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,s=Math.max(s,e.y+e.h),r=Math.max(r,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?xi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.input_graph_node(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const r=t;n=xi.findNode(i,r,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new co,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(ai.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return ks.create(this.param,e,t,n)}}class J1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class Z1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const Q1=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],K1=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class t2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const r=t[0],s=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(r)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,s,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(Q1.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?K1.indexOf(n)>=0?t[n]:-1:t}return-1}}class e2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],r=t[1];let s=null;try{s=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(s){const t=s.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(r){const t=n[r];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class n2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new co,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const r=t[0],s=this.getReferencedParam(r);if(s){s.isDirty()&&await s.compute();const t=s.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class i2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],r=t[2],s=this.node(),o=s?xi.findNode(s,n):null;let a;o&&o.type()==e$.type()&&(a=o.stamp_value(r)),null==a&&(a=i),e(a)}else e(0)}))}}class r2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class s2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class o2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class a2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class l2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class c2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.objectsCount())}}else e(0)}))}}class u2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(sr.tailDigits(t))}else e(0)}else e(0)}))}}class h2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class d2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class p2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class _2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.pointsCount())}}else e(0)}))}}class m2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class f2 extends $1{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class g2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class v2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let r=t[2]||1;return n&&(e=n.substr(i,r)),e}}class y2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class x2{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),r=b.clone(i);if(0==this._validateNames(r))return;b.clone(this._created_spare_param_names).concat(r).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=hG.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(r.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},r=b.merge(n,i);let s=this._init_value_serialized_by_param_name.get(t.name());null==s&&(s=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:s,raw_input:o,options:r})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Es.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class b2{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new x2(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var w2;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(w2||(w2={}));class T2{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class A2{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class E2{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)u.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)u.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class M2{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new E2(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const S2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\";\\\\\\\",[w2.BODY]:\\\\\\\";\\\\\\\"},C2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\"\\\\\\\",[w2.BODY]:\\\\\\\"\\\\t\\\\\\\"};class N2{static node_comment(t,e){let n=`// ${t.path()}`,i=C2[e];if(e==w2.BODY){let e=this.node_distance_to_material(t);t.type()==er.OUTPUT&&(e+=1),i=i.repeat(e)}return e==w2.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let r=C2[n];if(n==w2.BODY&&(r=r.repeat(this.node_distance_to_material(t))),e=`${r}${e}`,i){const t=e[e.length-1],i=S2[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==w2.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=er.INPUT&&t.type()!=er.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class L2{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new GI,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)u.pushOnArrayAtEntry(n,t,i)}const r=new Map;for(let t of i)r.set(t.graphNodeId(),!0);for(let e of t)r.get(e.graphNodeId())||(i.push(e),r.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new M2(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,yf.FUNCTION,w2.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,w2.DEFINE),this.addDefinitions(t,e,yf.VARYING,w2.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,w2.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,w2.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const r=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)r.push(e)}}if(r.length>0){const t=new vf(r),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const s=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),u.pushOnArrayAtEntry(s,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=s.get(e);if(n){const t=n[0];if(t){const e=N2.node_comment(t.node,i);u.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=N2.line_wrap(t.node,e.line,i);u.pushOnArrayAtEntry(a,i,n)}const r=N2.post_line_separator(i);u.pushOnArrayAtEntry(a,i,r)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let r=0;r0){const s=this._lines.get(e),o=N2.node_comment(t,n);if(u.pushOnArrayAtEntry(s,n,o),f.uniq(r).forEach((e=>{e=N2.line_wrap(t,e,n),u.pushOnArrayAtEntry(s,n,e)})),n!=w2.BODY||!i){const t=N2.post_line_separator(n);u.pushOnArrayAtEntry(s,n,t)}}}}class O2{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&u.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=xf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const r=this._graph_id_by_depth.get(e);r&&r.forEach((e=>{var r;if(null===(r=this._graph_ids_by_shader_name.get(t))||void 0===r?void 0:r.get(e)){const r=this._graph.nodeFromId(e);this.add_nodes_with_children(r,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==er.INPUT){const r=t.parent();if(r){const s=this.sorted_nodes_for_shader_name_for_parent(r,i);for(let r of s)r.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(r,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const r=this._graph_id_by_depth.get(n);r&&r.forEach((n=>{var r;if(!e||(null===(r=this._graph_ids_by_shader_name.get(e))||void 0===r?void 0:r.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const r=i[0];return t.context()==r.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(u.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),r=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(r.length>0)for(let e of r)u.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==er.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const R2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),P2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),I2=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class F2 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case UI.type():case HI.type():e.push(t);break;case gf.type():case Lf.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case HP.type():e.push(t);break;case gf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new L2(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(F2.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"mvPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldNormal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"gl_Position\\\\\\\",Do.VEC4),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"cameraPosition\\\\\\\",Do.VEC3),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return F2.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new A2(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return F2.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return R2.get(t)}insert_body_after(t){return P2.get(t)}lines_to_remove(t){return I2.get(t)}_replace_template(t,e){const n=this.builder_lines(e,w2.FUNCTION_DECLARATION),i=this.builder_lines(e,w2.DEFINE),r=this.builder_lines(e,w2.BODY);let s=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let u=!1,h=!1;for(let t of s){1==u&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),u=!1),1==h&&(r&&this._insert_lines(o,r),h=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(u=!0),l&&t.indexOf(l)>=0&&(h=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=z[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var D2,k2;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(D2||(D2={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(k2||(k2={}));const B2=[k2.GL_FRAGCOORD,k2.GL_POINTCOORD];class z2 extends F2{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const r=i.createMaterial();r.name=e,t.customMaterials[e]=r}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const r=t.customMaterials[n];r&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(r),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const r=this._shaders_by_name.get(xf.VERTEX),s=this._shaders_by_name.get(xf.FRAGMENT);r&&s&&(t.vertexShader=r,t.fragmentShader=this.processFilterFragmentShader(s),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const r=t.io.inputs.named_input(n),s=t.variableForInput(n),o=this.variable_config(n);let a=null;if(r)a=uf.vector3(s);else if(o.default_from_attribute()){const r=t.io.inputs.namedInputConnectionPointsByName(n);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),r=o.if_condition();r&&e.addBodyLines(t,[`#if ${r}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const s=o.postLines();s&&e.addBodyLines(t,s),r&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,r=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(r)for(let n of r)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),r=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),s=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${s} = ${r}`])}handle_globals_output_name(t){var e;switch(t.output_name){case k2.TIME:return void this.handleTime(t);case k2.RESOLUTION:return void this.handle_resolution(t);case k2.MV_POSITION:return void this.handle_mvPosition(t);case k2.GL_POSITION:return void this.handle_gl_Position(t);case k2.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case k2.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Af(t.globals_node,Do.FLOAT,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),u.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Do.VEC2,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let r of l){const l=t.glVarName(r),c=e.current_shader_name,u={globals_node:t,shaders_collection_controller:e,output_name:r,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:s,body_lines_by_shader_name:a};this.handle_globals_output_name(u)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==xf.VERTEX&&B2.includes(t)||i.push(t);return i}}const U2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const G2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var V2=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const H2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),j2=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const W2=new Map([]);W2.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return U2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH,class extends z2{templateShader(){const t=V.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return G2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:V2,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return H2.get(t)}insert_body_after(t){return j2.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class q2 extends z2{custom_assembler_class_by_custom_name(){return W2}}class X2 extends q2{templateShader(){const t=V.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class Y2 extends q2{templateShader(){const t=V.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class $2 extends q2{templateShader(){const t=V.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var J2=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class Z2 extends q2{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",Z2.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?V.physical:V.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),Z2.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"metalness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"roughness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"emissive\\\\\\\",Do.VEC3,[1,1,1])),Z2.USE_SSS&&e.push(new Vo(\\\\\\\"SSSModel\\\\\\\",Do.SSS_MODEL,J2)),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){const t=F2.create_variable_configs();return t.push(new A2(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new A2(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new A2(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),Z2.USE_SSS&&t.push(new A2(\\\\\\\"SSSModel\\\\\\\",{default:J2,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),t}}Z2.USE_SSS=!0;class Q2 extends Z2{isPhysical(){return!0}}const K2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),t3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const e3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),n3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const i3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),r3=new Map;r3.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return K2.get(t)}insert_body_after(t){return t3.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),r3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return e3.get(t)}insert_body_after(t){return n3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class s3 extends z2{custom_assembler_class_by_custom_name(){return r3}templateShader(){const t=V.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"gl_PointSize\\\\\\\",Do.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return F2.create_globals_node_output_connections().concat([new Vo(k2.GL_POINTCOORD,Do.VEC2)])}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return F2.create_variable_configs().concat([new A2(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return i3.get(t)}}const o3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),a3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const l3=new Map([]);l3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return o3.get(t)}insert_body_after(t){return a3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const c3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class u3 extends z2{templateShader(){const t=V.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",l3),l3}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(u3.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return u3.create_globals_node_output_connections()}create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return c3.get(t)}}class h3 extends F2{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include \\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._root_nodes)switch(r.type()){case UI.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._leaf_nodes)switch(r.type()){case HP.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}setup_shader_names_and_variables(){var t;const e=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new dQ,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new dQ}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,r){var s;if(n){const n=t.variableForInput(e),o=uf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=r.current_shader_name;if(e&&(null===(s=e.allocation())||void 0===s?void 0:s.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;r.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const r=n;this.add_export_body_line(t,n,i,r,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const r=t.gl_type(),s=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,r,s,e),a=t.glVarName(t.output_name),l=`${r} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(s),u=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==u){const n=this.textureAllocationsController().variable(s);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Af(t,Do.FLOAT,e);n.addDefinitions(t,[i]);const r=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[r]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,e,n);if(o){const i=`${s} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class d3 extends F2{templateShader(){return{fragmentShader:\\\\\\\"#include \\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(xf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==xf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}create_shader_configs(){return[new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let r;\\\\\\\"color\\\\\\\"==i&&(r=`diffuseColor.xyz = ${uf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(r=`diffuseColor.a = ${uf.any(n)}`),r&&e.addBodyLines(t,[r])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],r=[];for(let e of t.io.outputs.used_output_names()){const s=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":r.push(new Af(t,Do.FLOAT,e)),i.push(`float ${s} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,s)}}e.addDefinitions(t,r,n),e.addBodyLines(t,i)}}const p3=new Map([]);class _3 extends z2{custom_assembler_class_by_custom_name(){return p3}}const m3=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),f3=new Map([[xf.FRAGMENT,[]]]);class g3 extends _3{templateShader(){return{vertexShader:jB,fragmentShader:WB,uniforms:I.clone(qB)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return fs.add_user_data_render_hook(e,$B.render_hook.bind($B)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"density\\\\\\\",Do.FLOAT,1)])}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"pos_normalized\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return g3.create_globals_node_output_connections()}insert_body_after(t){return m3.get(t)}lines_to_remove(t){return f3.get(t)}create_shader_configs(){return[new T2(xf.VERTEX,[],[]),new T2(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{}),new A2(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return g3.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map;let l,c;for(let r of t.io.outputs.used_output_names()){const h=t.glVarName(r),d=e.current_shader_name;switch(r){case\\\\\\\"time\\\\\\\":l=new Af(t,Do.FLOAT,r),d&&u.pushOnArrayAtEntry(o,d,l),c=`float ${h} = ${r}`;for(let t of s)u.pushOnArrayAtEntry(o,t,l),u.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class v3{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(B_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(um,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Em,Kl),t.registerNode(Mm,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Mg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(Bg,ic),t.registerNode(Jg,ic),t.registerNode(ev,ic),t.registerNode(ov,rc),t.registerNode(lv,rc),t.registerNode(dv,rc),t.registerNode(_v,rc),t.registerNode(gv,nc),t.registerNode(xv,ic),t.registerNode(Tv,nc),t.registerNode(Mv,sc),t.registerNode(Cv,sc),t.registerNode(Sv,sc),t.registerNode(Nv,sc),t.registerNode(Lv,sc),t.registerNode(Ov,sc)}}.run(t),class{static run(t){t.registerNode(Fv,cc),t.registerNode(Bv,lc),t.registerNode(Uv,lc),t.registerNode(Hv,lc),t.registerNode(Kv,oc),t.registerNode(dy,oc),t.registerNode(hy,oc),t.registerNode(_y,lc),t.registerNode(yl,ac),t.registerNode(DN,oc),t.registerNode(ol,ac),t.registerNode(UN,lc),t.registerNode(VN,lc),t.registerNode(QN,oc),t.registerNode(qa,ac),t.registerNode(nL,cc),t.registerNode(oL,lc),t.registerNode(Za,ac),t.registerNode(NL,lc),t.registerNode(el,cc),t.registerNode(PL,cc),t.registerNode(jL,cc),t.registerNode(qL,lc),t.registerNode($L,lc),t.registerNode(bl,ac),t.registerNode(ZL,lc),t.registerNode(hl,ac),t.registerNode(tO,uc),t.registerNode(eO,uc),t.registerNode(nO,uc),t.registerNode(iO,uc),t.registerNode(rO,uc),t.registerNode(sO,uc)}}.run(t),class{static run(t){t.registerNode(jO,gc),t.registerNode(zR,vc),t.registerNode(WO,xc),t.registerNode(AR,gc),t.registerNode(jR,xc),t.registerNode(LR,fc),t.registerNode(qO,xc),t.registerNode(XO,xc),t.registerNode(gf,_c,{except:[`${Ki.COP}/builder`]}),t.registerNode(fO,dc),t.registerNode(YO,gc),t.registerNode(xR,gc),t.registerNode(YR,hc),t.registerNode(eP,fc),t.registerNode(nP,gc),t.registerNode(sP,_c),t.registerNode($O,xc),t.registerNode(lP,pc),t.registerNode(cP,gc),t.registerNode(JO,dc),t.registerNode(dP,pc),t.registerNode(hR,pc),t.registerNode(ER,gc),t.registerNode(dR,pc),t.registerNode(xP,gc),t.registerNode(ZO,gc),t.registerNode(QO,gc),t.registerNode(bR,pc),t.registerNode(TP,gc),t.registerNode(EP,gc),t.registerNode(SP,gc),t.registerNode(NP,gc),t.registerNode(lO,dc),t.registerNode(vO,dc),t.registerNode(xO,dc),t.registerNode(wO,dc),t.registerNode(KO,gc),t.registerNode(RP,hc),t.registerNode(GP,fc),t.registerNode(tR,gc),t.registerNode(HP,_c),t.registerNode(WP,hc),t.registerNode(XP,hc),t.registerNode(JP,fc),t.registerNode(QP,bc),t.registerNode(_O,dc),t.registerNode(hO,dc),t.registerNode(eR,gc),t.registerNode(sI,pc),t.registerNode(oI,pc),t.registerNode(lI,hc),t.registerNode(nR,gc),t.registerNode(iR,gc),t.registerNode(pR,gc),t.registerNode(uI,gc),t.registerNode(_R,gc),t.registerNode(mR,gc),t.registerNode(mI,gc),t.registerNode(dI,gc),t.registerNode(SR,gc),t.registerNode(vI,gc),t.registerNode(yI,gc),t.registerNode(BI,bc),t.registerNode(kI,pc),t.registerNode(rR,gc),t.registerNode(OR,fc),t.registerNode(UI,_c),t.registerNode(HI,_c),t.registerNode(fR,gc),t.registerNode(YI,yc),t.registerNode(QI,yc),t.registerNode(KI,yc),t.registerNode(tF,yc),t.registerNode(iF,_c),t.registerNode(oF,_c),t.registerNode(sR,dc),t.registerNode(gR,pc),t.registerNode(jI,pc),t.registerNode(lF,hc),t.registerNode(gF,pc),t.registerNode(yF,gc),t.registerNode(oR,gc),t.registerNode(aR,xc),t.registerNode(wR,gc),t.registerNode(bF,pc),t.registerNode(lR,gc),t.registerNode(XI,mc),t.registerNode(vR,pc),t.registerNode(kP,fc),t.registerNode(TF,fc,VF),t.registerNode(IP,fc,VF),t.registerNode(MR,gc),t.registerNode(EF,fc),t.registerNode(cR,xc),t.registerNode(SF,hc),t.registerNode(OF,_c),t.registerNode(IF,fc),t.registerNode(Lf,_c),t.registerNode(kF,_c),t.registerNode(NO,dc),t.registerNode(IO,dc),t.registerNode(LO,dc),t.registerNode(PO,dc),t.registerNode(FO,dc),t.registerNode(OO,dc),t.registerNode(RO,dc),t.registerNode(zF,pc),t.registerNode(GF,pc)}}.run(t),class{static run(t){t.registerNode(qF,wc),t.registerNode(YF,wc),t.registerNode(JF,wc),t.registerNode(iD,wc)}}.run(t),class{static run(t){t.registerNode(dD,Ac),t.registerNode(AD,Ac),t.registerNode(ek,Ec),t.registerNode(ak,Tc),t.registerNode(pk,Ec),t.registerNode(yk,Tc),t.registerNode(Ik,Ec),t.registerNode(Uk,Ec),t.registerNode(jk,Tc),t.registerNode(tB,Ec),t.registerNode(rB,Tc),t.registerNode(lB,Ec),t.registerNode(dB,Tc),t.registerNode(AB,Ec),t.registerNode(LB,Ec),t.registerNode(IB,Sc),t.registerNode(kB,Tc),t.registerNode(UB,Tc),t.registerNode(HB,Ec),t.registerNode(QB,Cc),t.registerNode(ez,Cc),t.registerNode(rz,Mc),t.registerNode(sz,Mc),t.registerNode(oz,Mc),t.registerNode(az,Mc),t.registerNode(lz,Mc),t.registerNode(cz,Mc)}}.run(t),class{static run(t){t.registerNode(gz,Pc),t.registerNode(Uz,Pc),t.registerNode(Zz,Pc),t.registerNode(rU,Pc),t.registerNode(uU,Pc),t.registerNode(vU,Pc),t.registerNode(CU,Lc),t.registerNode(PU,Fc),t.registerNode(HU,Nc),t.registerNode(YU,Rc),t.registerNode(ZU,Fc),t.registerNode(nG,Fc),t.registerNode(NG,Nc),t.registerNode(jG,Lc),t.registerNode(YG,Fc),t.registerNode(ZG,Nc),t.registerNode(lH,Oc),t.registerNode(dH,Oc),t.registerNode(mH,Oc),t.registerNode(yH,Ic),t.registerNode(xH,Ic),t.registerNode(bH,Ic),t.registerNode(wH,Ic),t.registerNode(TH,Ic),t.registerNode(AH,Ic)}}.run(t),class{static run(t){t.registerNode(RH,Qc),t.registerNode(DH,Qc),t.registerNode(zH,Zc),t.registerNode(VH,Zc),t.registerNode(WH,Kc),t.registerNode(XH,Kc),t.registerNode(JH,Kc),t.registerNode(QH,Kc),t.registerNode(aj,Qc),t.registerNode(rj,Qc),t.registerNode(hj,Qc),t.registerNode(_j,Kc),t.registerNode(gj,Zc),t.registerNode(yj,Jc),t.registerNode(bj,Kc),t.registerNode(Sj,Kc),t.registerNode(Nj,Kc),t.registerNode(Oj,Kc),t.registerNode(Ij,Qc),t.registerNode(kj,Qc),t.registerNode(zj,Kc),t.registerNode(Vj,Qc),t.registerNode(Wj,Zc),t.registerNode(Xj,Kc),t.registerNode(Zj,Jc),t.registerNode(eW,Qc),t.registerNode(iW,Jc),t.registerNode(oW,Qc),t.registerNode(cW,tu),t.registerNode(uW,tu),t.registerNode(hW,tu),t.registerNode(dW,tu),t.registerNode(pW,tu),t.registerNode(_W,tu)}}.run(t),class{static run(t){t.registerNode(bW,Dc),t.registerNode(PV,Bc),t.registerNode(wW,kc),t.registerNode(gW,kc),t.registerNode(TW,kc),t.registerNode(AW,kc),t.registerNode(EW,kc),t.registerNode(MW,kc)}}.run(t),class{static run(t){t.registerOperation(SW),t.registerOperation(GW),t.registerOperation($W),t.registerOperation(KW),t.registerOperation(iq),t.registerOperation(gq),t.registerOperation(hq),t.registerOperation(wq),t.registerOperation(eX),t.registerOperation(sX),t.registerOperation(yY),t.registerOperation(AY),t.registerOperation(s$),t.registerOperation(A$),t.registerOperation(DJ),t.registerOperation(YJ),t.registerOperation(rZ),t.registerOperation(lZ),t.registerOperation(_Z),t.registerOperation(PZ),t.registerOperation(AZ),t.registerOperation(WZ),t.registerOperation(JZ),t.registerOperation(fQ),t.registerOperation(TQ),t.registerOperation(LQ),t.registerOperation(DQ),t.registerOperation(XQ),t.registerOperation(nK),t.registerOperation(pK),t.registerOperation(xK),t.registerOperation(AK),t.registerOperation(RK),t.registerOperation(GK),t.registerOperation(QK),t.registerOperation(h0),t.registerOperation(w0),t.registerOperation(H0),t.registerOperation(X0),t.registerOperation(Q0),t.registerOperation(n1),t.registerOperation(l1),t.registerOperation(S1),t.registerOperation(I1),t.registerOperation(B1),t.registerNode(LW,Hc),t.registerNode(RW,Uc),t.registerNode(UW,Uc),t.registerNode(jW,Gc),t.registerNode(QW,Gc),t.registerNode(nq,Gc),t.registerNode(aq,Gc),t.registerNode(cq,Gc),t.registerNode(_q,Gc),t.registerNode(xq,Gc),t.registerNode(Mq,Gc),t.registerNode(Cq,Gc),t.registerNode(Lq,Gc),t.registerNode(Dq,Gc),t.registerNode(Bq,qc),t.registerNode(Uq,qc),t.registerNode(rX,qc),t.registerNode(lX,Yc),t.registerNode(dY,Wc),t.registerNode(vY,Yc),t.registerNode(bY,Yc),t.registerNode(SY,Yc),t.registerNode(IY,Yc),t.registerNode(UY,qc),t.registerNode(HY,Yc),t.registerNode(e$,qc),t.registerNode(l$,Yc),t.registerNode(p$,Hc),t.registerNode(b$,Hc),t.registerNode(S$,Wc),t.registerNode(N$,Wc),t.registerNode(j$,qc),t.registerNode(q$,qc),t.registerNode(CJ,zc),t.registerNode(LJ,qc),t.registerNode(zJ,Hc),t.registerNode(GJ,qc),t.registerNode(WJ,Yc),t.registerNode(tZ,qc),t.registerNode(QJ,Wc),t.registerNode(aZ,Yc),t.registerNode(hZ,$c),t.registerNode(pZ,$c),t.registerNode(gZ,qc),t.registerNode(yZ,qc),t.registerNode(bZ,Yc),t.registerNode(TZ,zc),t.registerNode(SZ,$c),t.registerNode(RZ,zc),t.registerNode(kZ,Wc),t.registerNode(VZ,Wc),t.registerNode(jZ,qc),t.registerNode(XZ,Wc),t.registerNode($Z,Hc),t.registerNode(KZ,qc),t.registerNode(eQ,zc,{userAllowed:!1}),t.registerNode(mQ,Vc),t.registerNode(yQ,qc),t.registerNode(MQ,Yc),t.registerNode(zQ,qc),t.registerNode(NQ,qc),t.registerNode(PQ,jc),t.registerNode(EG,zc),t.registerNode(WQ,qc),t.registerNode(JQ,qc),t.registerNode(sK,$c),t.registerNode(dK,qc),t.registerNode(fK,Gc),t.registerNode(TK,Hc),t.registerNode(SK,qc),t.registerNode(BK,qc),t.registerNode(DK,qc),t.registerNode(qK,zc),t.registerNode(YK,zc),t.registerNode(jK,qc),t.registerNode(e0,Yc),t.registerNode(i0,qc),t.registerNode(_0,qc),t.registerNode(f0,Wc),t.registerNode(v0,Wc),t.registerNode(yG,Wc),t.registerNode(E0,Hc),t.registerNode(C0,Wc),t.registerNode(O0,Yc),t.registerNode(V0,Yc),t.registerNode(q0,qc),t.registerNode(J0,qc),t.registerNode(e1,Yc),t.registerNode(s1,Yc),t.registerNode(h1,qc),t.registerNode(p1,qc),t.registerNode(v1,qc),t.registerNode(w1,qc),t.registerNode(E1,Yc),t.registerNode(N1,qc),t.registerNode(P1,qc),t.registerNode(k1,qc),t.registerNode(U1,qc),t.registerNode(H1,Xc),t.registerNode(j1,Xc),t.registerNode(W1,Xc),t.registerNode(q1,Xc),t.registerNode(X1,Xc),t.registerNode(Y1,Xc)}}.run(t)}}.run(ai),class{static run(t){t.registerCamera(lH),t.registerCamera(dH)}}.run(ai),class{static run(t){t.expressionsRegister.register(J1,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(Z1,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(t2,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(e2,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(n2,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(i2,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(r2,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(s2,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(o2,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(a2,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(l2,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(c2,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(u2,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(h2,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(d2,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(p2,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(_2,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(m2,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(f2,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(g2,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(v2,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(y2,\\\\\\\"windowSize\\\\\\\")}}.run(ai),class{static run(t){t.assemblersRegister.register(Hn.GL_MESH_BASIC,b2,X2),t.assemblersRegister.register(Hn.GL_MESH_LAMBERT,b2,Y2),t.assemblersRegister.register(Hn.GL_MESH_PHONG,b2,$2),t.assemblersRegister.register(Hn.GL_MESH_STANDARD,b2,Z2),t.assemblersRegister.register(Hn.GL_MESH_PHYSICAL,b2,Q2),t.assemblersRegister.register(Hn.GL_PARTICLES,b2,h3),t.assemblersRegister.register(Hn.GL_POINTS,b2,s3),t.assemblersRegister.register(Hn.GL_LINE,b2,u3),t.assemblersRegister.register(Hn.GL_TEXTURE,b2,d3),t.assemblersRegister.register(Hn.GL_VOLUME,b2,g3)}}.run(ai))}}v3._started=!1,v3.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2809283"}},"type":2,"external":true,"timestamp":1723904728127},{"data":{"url":"blob:https://ipfs.arkivo.art/27c5dcfb-fdad-48bd-ba16-b466ea7e26dd","host":"","path":"https://ipfs.arkivo.art/27c5dcfb-fdad-48bd-ba16-b466ea7e26dd","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":1723904728141},{"data":{"url":"blob:https://ipfs.arkivo.art/27c5dcfb-fdad-48bd-ba16-b466ea7e26dd","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":1723904735466}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized Content-Security-Policy directive 'prefetch-src'.","level":"error","timestamp":1723904726961},{"text":"brightness Bm","level":"log","timestamp":1723904728285},{"text":"[.WebGL-0x3c0801157100]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723904735466},{"text":"[.WebGL-0x3c0801157100]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723904735466},{"text":"[.WebGL-0x3c0801157100]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723904735466},{"text":"[.WebGL-0x3c0801157100]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723904735466}],"screenshotDelay":10000},"timestamp":1723904726410},"created_at":"2024-08-17T14:25:57.281+00:00","updated_at":"2024-08-17T14:25:57.298+00:00"}